Patent Application
20240268277
The present disclosure relates to systems and methods for autonomous crop removal.
As technology advances, tasks that had previously been performed by humans are increasingly becoming automated. While tasks performed in highly controlled environments, such as factory assembly lines, can be automated by directing a machine to perform the task the same way each time, tasks performed in unpredictable environments, such as agricultural environments, depend on dynamic feedback and adaptation to perform the task. Autonomous systems often struggle to identify and locate objects in unpredictable environments. Improved methods of object tracking would advance automation technology and increase the ability of autonomous systems to react and adapt to unpredictable environments.
In various aspects, the present disclosure provides a method for autonomous crop maintenance and seedline tracking, the method comprising: determining, by a processor, a density of one or more crops in a region; generating, by the processor, a kernel density estimation (KDE) of the one or more crops; identifying, by the processor, local minima in the density of the crops; determining, by the processor, a persistence of each local minimum of the local minima; selecting, by the processor, a subset of the local minima based on the persistence, clustering, by the processor, the crops into a number of clusters corresponding to the number of seedlines based on locations of the subset of the local minima; determining, by the processor, a position of each cluster along an axis of the seedline; determining, by the processor, seedline locations of the number of seedlines based on the position of each cluster; and generating, by the processor, one or more virtual bands around the seedline locations.
In some aspects, the region is a crop field. In some aspects, the axis is perpendicular to a predicted orientation of the seedlines. In some aspects, the bands are designated based on a seedline width. In some aspects, the method further comprises creating the kernel density estimation (KDE) of the crop density using a Gaussian kernel. In some aspects, the subset of the local minima comprises a number of the local minima corresponding to one more than a number of seedlines. In some aspects, the crop is onion, pepper, strawberry, carrot, corn, soybeans, barley, oats, wheat, alfalfa, cotton, hay, tobacco, rice, sorghum, tomato, potato, grape, rice, lettuce, bean, pea, sugar beet, broccoli, cauliflower, mustard, kale, or brussels sprouts. In some aspects, maintaining the crops located within the bands comprises weeding the region within the bands or thinning the crops located within the bands. In some aspects, the method further comprises designating one or more out of band regions corresponding to regions outside the bands. In some aspects, the method comprises ignoring plants located in the out of band regions. In some aspects, plants located in the out of band regions are not targeted.
In various aspects, the present disclosure provides an autonomous plant targeting system comprising: a processor; and a memory comprising instructions stored thereon, which, when executed by the processor causes the system to perform operations comprising: determining a density of one or more crops in a region; generating a kernel density estimation (KDE) of the one or more crops; identifying local minima in the density of the crops; determining a persistence of each local minimum of the local minima; selecting a subset of the local minima based on the persistence, clustering the crops into a number of clusters corresponding to the number of seedlines based on locations of the subset of the local minima; determining a position of each cluster along an axis of the seedlines; determining seedline locations of the number of seedlines based on the position of each cluster; and generating one or more virtual bands around the seedline locations.
In some aspects, the system further comprises designating out of band regions corresponding to regions outside the bands. In some aspects, the system comprises ignoring plants located in the out of band regions. In some aspects, plants located in the out of band regions are not targeted. In some aspects, the subset of the local minima comprises a number of the local minima corresponding to one more than a number of seedlines. In some aspects, creating the kernel density estimation comprises using a Gaussian kernel on locations of the crops. In some aspects, the system further comprises using one-dimensional persistence homology to determine the persistence of each local minimum. In some aspects, the number of seedlines corresponds to an expected number of seedlines in the region.
In various aspects, the present disclosure provides a non-transitory computer readable medium containing computer executable instructions that, when executed by a computer hardware arrangement, cause the computer hardware arrangement to perform procedures comprising: determining, by a processor, a density of one or more crops in a region; generating, by the processor, a kernel density estimation (KDE) of the one or more crops; identifying, by the processor, local minima in the density of the crops; determining, by the processor, a persistence of each local minimum of the local minima; selecting, by the processor, a subset of the local minima based on the persistence, clustering, by the processor, the crops into a number of clusters corresponding to the number of seedlines based on locations of the subset of the local minima; determining, by the processor, a position of each cluster along an axis of the seedlines; determining, by the processor, seedline locations of the number of seedlines based on the position of each cluster; and generating, by the processor, one or more virtual bands around the seedline locations.
In some aspects, the techniques described herein relate to a method for autonomous crop thinning, including: receiving, by a processor, one or more images of a crop field containing a plurality of crops; processing, by the processor, the images using a machine learning model to identify one or more individual crops; determining, by the processor, a location and a parameter of each of the one or more identified crops; generating a crop boundary around each identified crop based on the determined location and the parameter of each of the one or more identified crops; and selecting target crops for removal based on their respective locations and parameters relative to the crop boundaries.
In some aspects, the techniques described herein relate to an autonomous plant targeting system including: a processor; and a memory including instructions stored thereon, which, when executed by the processor causes the system to perform operations including: receiving, by the processor, one or more images of a crop field containing a plurality of crops; processing, by the processor, the images using a machine learning model to identify one or more individual crops; determining, by the processor, a location and a parameter of each of the one or more identified crops; generating, by the processor, a crop boundary around each identified crop based on the determined location and the parameter of each of the one or more identified crops; and selecting, by the processor, target crops for removal based on their respective locations and parameters relative to the crop boundaries.
In some aspects, the techniques described herein relate to a non-transitory computer readable medium containing computer executable instructions that, when executed by a computer hardware arrangement, cause the computer hardware arrangement to perform procedures including: receiving one or more images of a crop field containing a plurality of crops; processing the images using a machine learning model to identify one or more individual crops; determining a location and a parameter of each of the one or more identified crops; generating a crop boundary around each identified crop based on the determined location and the parameter of each of the one or more identified crops; and selecting target crops for removal based on their respective locations and parameters relative to the crop boundaries.
The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings of which:
Described herein are systems and methods for autonomously thinning and maintaining crops. Crop thinning may comprise targeting and removing or eradicating select crops to maintain crop density at a desired level. Reducing crop density may improve growth or survivability of the remaining crops. Crop thinning may be used to counterbalance practices of over planting, which may be done to compensate for the proportion of planted seeds that fail to germinate. An autonomous crop thinning method of the present disclosure may comprise identifying and targeting select crops based on parameters including plant spacing, plant health, plant size, growth stage, or combinations thereof. As described herein, an autonomous plant targeting system may identify and locate crops in a region, evaluate parameters of the crops (e.g., spacing, health, size, or combinations thereof), select crops for thinning based on one or more of the parameters, and eradicate the selected crops (e.g., by irradiating the crop with a laser). Examples of crops that may be thinned using the methods described herein include onion, pepper, strawberry, carrot, corn, soybeans, barley, oats, wheat, alfalfa, cotton, hay, tobacco, rice, sorghum, tomato, potato, grape, rice, lettuce, bean, pea, sugar beet, or brassica (e.g., broccoli, cauliflower, mustard, kale, or brussels sprouts). In some embodiments, thinning may be performed concurrently with weeding. For example, an autonomous plant targeting system may perform both crop thinning and weeding as it moves through a crop field.
The technology described in the patent document for autonomous crop thinning represents a substantial advancement over previous agricultural automation technologies. Traditional methods of crop thinning have largely been manual, requiring substantial human labor which can be both time-consuming and costly. The systems and methods described in this patent document address these limitations by introducing a high degree of autonomy and precision in identifying, selecting, and targeting individual crops for thinning. Unlike earlier technologies, this autonomous crop thinning system utilizes advanced imaging and predictive analytics to create a virtual representation of the crop field. This allows for dynamic feedback and real-time adaptation to the specific conditions of the field. The system's ability to designate crop boundaries and select target crops based on a combination of location, health, and size parameters is a marked improvement over less sophisticated systems that may not account for such a comprehensive range of factors. Furthermore, the use of laser irradiation for targeting selected crops offers a level of precision that minimizes damage to surrounding crops, which is a common drawback of bulkier mechanical thinning equipment.
The technology provides a solution to several technological problems associated with automated crop thinning in unpredictable agricultural environments. One of the primary challenges in automating agricultural tasks is the variability and unpredictability of natural growth patterns and environmental conditions. The autonomous crop thinning system addresses this by incorporating advanced object tracking and identification methods that can adapt to the dynamic nature of crop fields. By generating a virtual representation of the region and continuously updating it as the autonomous system moves through the field, the technology ensures that decisions about which crops to thin are based on the latest data, thereby optimizing crop density and health. Another technological problem that this system solves is the difficulty in selectively targeting individual crops without affecting the surrounding plants. The precision targeting enabled by laser technology ensures that the system can eradicate unwanted crops without collateral damage. This level of precision is a technological solution that manual or mechanical methods cannot easily replicate. Additionally, the system's ability to dynamically update the average crop size and make thinning decisions based on real-time deviations from this average represents a sophisticated approach to maintaining crop uniformity and optimizing field yield. In summary, the autonomous crop thinning technology described in this patent document offers a comprehensive and precise solution to the challenges of automating crop management tasks in variable and unpredictable environments. Its integration of advanced imaging, predictive analytics, and precise targeting methods represents a notable improvement over past technologies and provides a clear technological solution to the problems faced in modern agriculture.
Described herein are systems and methods for autonomously thinning and maintaining crops. Crop thinning may comprise targeting and removing or eradicating select crops to maintain crop density at a desired level. Reducing crop density may improve growth or survivability of the remaining crops. Crop thinning may be used to counterbalance practices of over planting, which may be done to compensate for the proportion of planted seeds that fail to germinate. An autonomous crop thinning method of the present disclosure may comprise identifying and targeting select crops based on parameters including plant spacing, plant health, plant size, growth stage, or combinations thereof. As described herein, an autonomous plant targeting system may identify and locate crops in a region, evaluate parameters of the crops (e.g., spacing, health, size, or combinations thereof), select crops for thinning based on one or more of the parameters, and eradicate the selected crops (e.g., by irradiating the crop with a laser). Examples of crops that may be thinned using the methods described herein include onion, pepper, strawberry, carrot, corn, soybeans, barley, oats, wheat, alfalfa, cotton, hay, tobacco, rice, sorghum, tomato, potato, grape, rice, lettuce, bean, pea, sugar beet, or brassica (e.g., broccoli, cauliflower, mustard, kale, or brussels sprouts). In some embodiments, thinning may be performed concurrently with weeding. For example, an autonomous plant targeting system may perform both crop thinning and weeding as it moves through a crop field.
As used herein, an “image” may refer to a representation of a region or object. For example, an image may be a visual representation of a region or object formed by electromagnetic radiation (e.g., light, x-rays, microwaves, or radio waves) scattered off of the region or object. In another example, an image may be a point cloud model formed by a light detection and ranging (LIDAR) or a radio detection and ranging (RADAR) sensor. In another example, an image may be a sonogram produced by detecting sonic, infrasonic, or ultrasonic waves reflected off of the region or object. As used herein, “imaging” may be used to describe a process of collecting or producing a representation (e.g., an image) of a region or an object.
As used herein a position, such as a position of an object or a position of a sensor, may be expressed relative to a frame of reference. Exemplary frames of reference include a surface frame of reference, a vehicle frame of reference, a sensor frame of reference, or an actuator frame of reference. Positions may be readily converted between frames of reference, for example by using a conversion factor or a calibration model. While a position, a change in position, or an offset may be expressed in a one frame of reference, it should be understood that the position, change in position, or offset may be expressed in any frame of reference or may be readily converted between frames of reference.
As used herein, a “sensor” may refer to a device capable of detecting or measuring an event, a change in an environment, or a physical property. For example, a sensor may detect light, such as visible, ultraviolet, or infrared light, and generate an image. Examples of sensors include cameras (e.g., a charge-coupled device (CCD) camera or a complementary metal-oxide-semiconductor (CMOS) camera), a LIDAR detector, an infrared sensor, an ultraviolet sensor, or an x-ray detector.
As used herein, “object” may refer to an item or a distinguishable area that may be observed, tracked, manipulated, or targeted. For example, an object may be a plant, such as a crop or a weed. In another example, an object may be a piece of debris. In another example, an object may be a distinguishable region or point on a surface, such as a marking or surface irregularity.
As used herein, “targeting” or “aiming” may refer to pointing or directing a device or action toward a particular location or object. For example, targeting an object may comprise pointing a sensor (e.g., a camera) or implement (e.g., a laser) toward the object. Targeting or aiming may be dynamic, such that the device or action follows an object moving relative to the targeting system. For example, a device positioned on a moving vehicle may dynamically target or aim at an object located on the ground by following the object as the vehicle moves relative to the ground.
As used herein, a “weed” may refer to an unwanted plant, such as a plant of an unwanted type or a plant growing in an undesirable place or at an undesirable time. For example, a weed may be a wild or invasive plant. In another example, a weed may be a plant within a field of cultivated crops that is not the cultivated species. In another example, a weed may be a plant growing outside of or between cultivated rows of crops. As used herein, a “crop” may be a cultivated plant.
As used herein, “manipulating” an object may refer to performing an action on, interacting with, or altering the state of an object. For example, manipulating may comprise irradiating, illuminating, heating, burning, killing, moving, lifting, grabbing, spraying, or otherwise modifying an object.
As used herein, “electromagnetic radiation” may refer to radiation from across the electromagnetic spectrum. Electromagnetic radiation may include, but is not limited to, visible light, infrared light, ultraviolet light, radio waves, gamma rays, or microwaves.
The object tracking methods described herein may be implemented by an autonomous plant targeting system to target and eliminate selected plants. Such tracking methods may facilitate object tracking relative to a moving body, such as a moving vehicle. For example, an autonomous plant targeting system may be used to track a plant of interest identified in images or representations collected by a first sensor, such as a prediction sensor, over time relative to the autonomous plant targeting system while the system is in motion relative to the plant. The tracking information may be used to determine a predicted location of the plant relative to the system at a later point in time. The autonomous plant targeting system may then locate the same plant in an image or representation collected by a second sensor, such as a targeting sensor, using the predicted location. In some embodiments, the first sensor is a prediction camera, and the second sensor is a targeting camera. One or both of the first sensor and the second sensor may be moving relative to the plant. For example, the prediction camera may be coupled to and moving with the autonomous plant targeting system.
Targeting the plant may comprise precisely locating the plant using the targeting sensor, targeting the plant with a laser, and eradicating the plant by burning it with laser light, such as infrared light. The prediction sensor may be part of a prediction module configured to determine a predicted location of an object of interest, and the targeting sensor may be part of a targeting module configured to refine the predicted location of the object of interest to determine a target location and target the object of interest with the laser at the target location. The prediction module may be configured to communicate with the targeting module to coordinate a camera handoff using point to point targeting, as described herein. The targeting module may target the object at the predicted location. In some embodiments, the targeting module may use the trajectory of the object to dynamically target the object while the system is in motion such that the position of the targeting sensor, the laser, or both is adjusted to maintain the target.
An autonomous plant targeting system may identify, target, and eliminate plants without human input. Optionally, the autonomous plant targeting system may be positioned on a self-driving vehicle or a piloted vehicle or may be a trailer pulled by another vehicle such as a tractor. As illustrated in
While the primary focus of the patent application is on the identification, selection, and targeting of plants for the purpose of autonomous crop thinning, the underlying technology is not limited to plant detection. The system's advanced imaging and predictive analytics capabilities are designed to identify and locate objects in unpredictable environments, which inherently allows for the detection of a wide range of objects beyond just plants. The methods and systems described are capable of distinguishing and tracking any distinguishable items or areas that can be observed within their operational field. This includes, but is not limited to, debris, infrastructure elements, or other items that may be present in an agricultural setting. The flexibility and adaptability of the system's object tracking technology enable it to be applied to various scenarios where autonomous detection and manipulation of objects are beneficial. Therefore, while the application predominantly illustrates the system's utility in an agricultural context, the principles and mechanisms of object detection and targeting it employs can be generalized to other applications where identifying and interacting with various objects is desired.
In some embodiments, the object tracking methods described herein may be performed by a detection system. The detection system may comprise a prediction system and, optionally, a targeting system. In some embodiments, the detection system may be positioned on or coupled to a vehicle, such as a self-driving plant targeting vehicle or a plant targeting trailer pulled by a tractor. The prediction system may comprise a prediction sensor configured to image a region of interest, and the targeting system may comprise a targeting sensor configured to image a portion of the region of interest. Imaging may comprise collecting a representation (e.g., an image) of the region of interest or the portion of the region of interest. In some embodiments, the prediction system may comprise a plurality of prediction sensors, enabling coverage of a larger region of interest. In some embodiments, the targeting system may comprise a plurality of targeting sensors.
The region of interest may correspond to a region of overlap between the targeting sensor field of view and the prediction sensor field of view. Such overlap may be contemporaneous or may be temporally separated. For example, the prediction sensor field of view encompasses the region of interest at a first time and the targeting sensor field of view encompasses the region of interest at a second time but not at the first time. Optionally, the detection system may move relative to the region of interest between the first time and the second time, facilitating temporally separated overlap of the prediction sensor field of view and the targeting sensor field of view.
In some embodiments the prediction sensor may have a wider field of view than the targeting sensor. The prediction system may further comprise an object identification module to identify an object of interest in a prediction image or representation collected by the prediction sensor. The object identification module may differentiate an object of interest from other objects in the prediction image.
The prediction module may determine a predicted location of the object of interest and may send the predicted location to the targeting system. The predicted location of the object may be determined using the object tracking methods described herein.
The targeting system may point the targeting sensor toward a desired portion of the region of interest predicted to contain the object, based on the predicted location received from the prediction system. In some embodiments, the targeting module may direct an implement toward the object. In some embodiments, the implement may perform an action on or manipulate the object. In some embodiments, the targeting module may use the trajectory of the object to dynamically target the object while the system is in motion such that the position of the targeting sensor, the implement, or both is adjusted to maintain the target. U.S. patent application Ser. No. 17/576,814, which is incorporated by reference, describes machine learning models for automated identification, maintenance, control, or targeting of objects.
An example of a detection system 300 is provided in
The prediction module is primarily responsible for the initial identification and tracking of objects. It utilizes a prediction sensor, which could be a camera or another type of imaging device, to capture images or representations of a region of interest. This module processes the collected data to generate a virtual representation of the region, identifying the locations and parameters of individual objects, such as crops, within that space.
Once an object has been identified and its trajectory predicted, the prediction module communicates this information to the targeting module. The targeting module is equipped with a targeting sensor that receives the predicted location of the object from the prediction module. Using this data, the targeting module can then precisely aim an implement, such as a laser, at the object. The targeting sensor ensures that the implement is accurately directed towards the object's current or future location, accounting for any movement of the object or the autonomous system itself. The prediction module's ability to forecast the object's location allows the targeting module to compensate for any delays between the identification of the object and the moment of action, ensuring that the targeting is precise and effective. This coordination is particularly useful when the autonomous system is in motion, as it allows for dynamic adjustments to be made in real-time, ensuring that the targeting remains accurate despite any changes in the relative positions of the system and the objects.
In other example embodiments, the system does not require the prediction system to be physically located in front of the targeting system. The primary objective is to ensure that the prediction system's field of view precedes the targeting system's field of view in the direction of the system's movement, allowing for the timely prediction and subsequent targeting of objects. As a nonlimiting example, in other example embodiments the prediction sensor may be angled in such a way that its field of view extends further ahead in the travel path, even if the sensor itself is not positioned at the frontmost point of the system. This flexibility in sensor arrangement is particularly advantageous in scenarios where space constraints or design considerations necessitate a more compact or non-linear configuration of system components.
A detection system of the present disclosure may be used to target objects on a surface, such as the ground, a dirt surface, a floor, a wall, an agricultural surface (e.g., a field), a lawn, a road, a mound, a pile, or a pit. In some embodiments, the surface may be a non-planar surface, such as uneven ground, uneven terrain, or a textured floor. For example, the surface may be uneven ground at a construction site, in an agricultural field, or in a mining tunnel, or the surface may be uneven terrain containing fields, roads, forests, hills, mountains, houses, or buildings. The detection systems described herein may locate an object on a non-planar surface more accurately, faster, or within a larger area than a single sensor system or a system lacking an object matching module.
Alternatively or in addition, a detection system may be used to target objects that may be spaced from the surface they are resting on, such as a tree top distanced from its grounding point, and/or to target objects that may be locatable relative to a surface, for example, relative to a ground surface in air or in the atmosphere. In addition, a detection system may be used to target objects that may be moving relative to a surface, for example, a vehicle, an animal, a human, or a flying object.
The object identification module 420 may identify objects in images collected by the prediction sensor. For example, the object identification module 420 may identify plants in an image and may differentiate the plants from other plants in the image, such as crops. The object location module 425 may determine locations of the objects identified by the object identification module 420 and to compile a set of identified objects and their corresponding locations. Object identification and object location may be performed on a series of images collected by the prediction sensor 410 over time. The set of identified objects and corresponding locations from in two or more images from the object location module 425 may be sent to the deduplication module 430.
In some embodiments, the object identification module 420 may employ machine learning models, which are trained to identify specific features of objects based on a large dataset of labeled images. The training process involves feeding the model numerous examples of images that contain the objects of interest, along with annotations that describe what the objects are and where they are located within the images. For example, in the context of crop thinning, the model might learn to recognize the shape, color, texture, and size of various crops and weeds. The model is trained to differentiate between these plants, allowing it to identify which ones are crops that ought to be preserved and which are weeds or excess crops that can be targeted for removal. Once trained, the object identification module 420 can process new images from the prediction sensor and apply the learned patterns to identify objects in real-time. It can differentiate objects of interest from the background and other objects that are not relevant to the task at hand. The module may use various machine learning techniques, such as convolutional neural networks (CNNs).
The deduplication module 430 may use object locations in a first image collected at a first time and object locations in a second image collected at a second time to identify objects, such as object O, appearing in both the first image and the second image. The set of identified objects and corresponding locations may be deduplicated by the deduplication module 430 by assigning locations of an object appearing in both the first image and the second image to the same object O. In some embodiments, the deduplication module 430 may use a velocity estimate from the velocity tracking module 415 to identify corresponding objects appearing in both images. The resulting deduplicated set of identified objects may contain unique objects, each of which has one or more corresponding locations determined at one or more time points.
Machine learning can be applied to this process by using models that recognize and match features of objects across different images. For instance, a machine learning model could be trained on a dataset of sequential images where objects of interest move or change appearance slightly. The model would learn to associate different instances of the same object across these images, despite variations in perspective, lighting, or partial occlusions. Such a model could use techniques like feature matching and object tracking algorithms that are robust to changes in the object's environment. By learning the typical motion patterns or changes in appearance of objects within the field, the deduplication module could more accurately determine when different images feature the same object, thereby reducing the likelihood of counting an object more than once.
The reconciliation module 435 may receive the deduplicated set of objects from the deduplication module 430 and may reconcile the deduplicated set by removing objects. In some embodiments, objects may be removed if they are no longer being tracked. For example, an object may be removed if it has not been identified in a predetermined number of images in the series of images. In another example, an object may be removed if it has not been identified in a predetermined period of time. In some embodiments, objects no longer appearing in images collected by the prediction sensor 410 may continue to be tracked. For example, an object may continue to be tracked if it is expected to be within the prediction field of view based on the predicted location of the object. In another example, an object may continue to be tracked if it is expected to be within range of a targeting system based on the predicted location of the object. The reconciliation module 435 may provide the reconciled set of objects to the location prediction module 440.
The reconciliation module is responsible for maintaining an accurate and current list of objects being tracked. It removes objects that are no longer relevant, such as those that have not been detected for a set period or number of frames. Machine learning can assist in this process by predicting which objects are likely to reappear based on their last known trajectory and the typical behavior of objects within the environment. A predictive machine learning model could analyze the movement patterns of objects and predict their future positions. If an object temporarily disappears from view-perhaps due to occlusion or moving out of the frame—the model could estimate the likelihood of its return. This would allow the reconciliation module to make informed decisions about whether to keep tracking an object or remove it from the list, optimizing the system's resources and attention.
The location prediction module 440 may determine a predicted location at a future time of object O from the reconciled set of objects. In some embodiments, the predicted location may be determined from two or more corresponding locations determined from images collected at two or more time points or from a single location combined with velocity information from the velocity tracking module 415. The predicted location of object O may be based on a vector velocity, including speed and direction, of object O relative to the moving body between the location of object O in a first image collected at a first time and the location of object O in a second image collected at a second time. Optionally, the vector velocity may account for a distance of the object O from the moving body along the imaging axis (e.g., a height or elevation of the object relative to the surface). Alternatively or in addition, the predicted location of the object may be based on the location of object O in the first image or in the second image and a vector velocity of the vehicle determined by the from the velocity tracking module 415.
The targeting system 450 may receive the predicted location of the object O at a future time from the prediction system 400 and may use the predicted location to precisely target the object with an implement 475 at the future time. The targeting control module 460 of the targeting system 450 may receive the predicted location of object O from the location prediction module 440 of the prediction system 400 and may instruct the targeting sensor 465, the implement 475, or both to point toward the predicted location of the object. Optionally, the targeting sensor 465 may collect an image of object O, and the location refinement module 470 may refine the predicted location of object O based on the location of object O determined from the image. In some embodiments, the location refinement module 470 may account for optical distortions in images collected by the prediction sensor 410 or the targeting sensor 465, or for distortions in angular motions of the implement 475 or the targeting sensor 465 due to nonlinearity of the angular motions relative to object O. The targeting control module 460 may instruct the implement 475, and optionally the targeting sensor 465, to point toward the refined location of object O. In some embodiments, the targeting control module 460 may adjust the position of the targeting sensor 465 or the implement 475 to follow the object to account for motion of the vehicle while targeting. The implement 475, such as a laser, may then manipulate object O. For example, a laser may direct infrared light toward the predicted or refined location of object O. Object O may be a plant and directing infrared light toward the location of the plant may eradicate the plant.
In some embodiments, a prediction system 400 may further comprise a scheduling module 445. The scheduling module 445 may select objects identified by prediction module and schedule which ones to target with the targeting system. The scheduling module 445 may schedule objects for targeting based on parameters such as object location, relative velocity, implement activation time, confidence score, or combinations thereof. For example, the scheduling module 445 may prioritize targeting objects predicted to move out of a field of view of a prediction sensor or a targeting sensor or out of range of an implement. Alternatively or in addition, a scheduling module 445 may prioritize targeting objects identified or located with high confidence. Alternatively or in addition, a scheduling module 445 may prioritize targeting objects with short activation times. In some embodiments, a scheduling module 445 may prioritize targeting objects based on a user's preferred parameters.
The targeting system, which includes a targeting control module and a targeting sensor, could be designed to handle multiple targets by scheduling the targeting sequence based on various parameters such as object location, relative velocity, and implement activation time. The scheduling module 445 could prioritize objects and orchestrate the targeting sequence to efficiently transition between multiple targets. For the targeting sensor to engage multiple targets at once, it could utilize a rapid point-to-point movement system to quickly redirect the laser or other implements from one target to the next. Alternatively, if the technology allows, the implement could be a multi-beam laser capable of splitting its focus to target several locations in quick succession or even simultaneously, depending on the spatial arrangement of the targets and the capabilities of the laser system. Advanced algorithms within the targeting control module would generate the precise timing and movement patterns to align the laser with each predicted location of the objects. This would enable the targeting sensor to follow a pre-determined path that intersects with the objects at the right moments, considering the continuous movement of the autonomous plant targeting system through the field.
A prediction module of the present disclosure may be configured to track objects relative to a moving body using the tracking methods described herein. In some embodiments, a prediction module is configured to capture an image or representation of a region of a surface using the prediction camera or prediction sensor, identify an object of interest in the image, and determine a predicted location of the object.
The prediction module may include an object identification module configured to identify an object of interest and differentiate the object of interest from other objects in the prediction image. In some embodiments, the prediction module uses a machine learning model to identify and differentiate objects based on features extracted from a training dataset comprising labeled images of objects. For example, the machine learning model of or associated with the object identification module may be trained to identify plants and differentiate plants of interest from other plants, such as crops. In another example, the machine learning model of or associated with the object identification module may be trained to identify debris and differentiate debris from other objects. The object identification module may be configured to identify a plant and to differentiate between different plants, such as between a crop and a weed. In some embodiments, the machine learning model may be a deep learning model, such as a deep learning neural network.
In some embodiments, the object identification module comprises using an identification machine learning model, such as a convolutional neural network. The identification machine learning model may be trained with many images, such as high-resolution images, for example of surfaces with or without objects of interest. For example, the machine learning model may be trained with images of fields with or without weeds. Once trained, the machine learning model may be configured to identify a region in the image containing an object of interest. The region may be defined by a polygon, for example a rectangle. In some embodiments, the region is a bounding box. In some embodiments, the region is a polygon mask covering an identified region. In some embodiments, the identification machine learning model may be trained to determine a location of the object of interest, for example a pixel location within a prediction image.
The prediction module may further comprise a velocity tracking module to determine the velocity of a vehicle to which the prediction module is coupled. In some embodiments, the positioning system and the detection system may be positioned on the vehicle. Alternatively or in addition, the positioning system may be positioned on a vehicle that is spatially coupled to the detection system. For example, the positioning system may be located on a vehicle pulling the detection system. The velocity tracking module may comprise a positioning system, for example a wheel encoder or rotary encoder, an Inertial Measurement Unit (IMU), a Global Positioning System (GPS), a ranging sensor (e.g., laser, SONAR, or RADAR), or an Internal Navigation System (INS). For example, a wheel encoder in communication with a wheel of the vehicle may estimate a velocity or a distance traveled based on angular frequency, rotational frequency, rotation angle, or number of wheel rotations. In some embodiments, the velocity tracking module may utilize images from the prediction sensor to determine the velocity of the vehicle using optical flow.
The prediction module may comprise a system controller, for example a system computer having storage, random access memory (RAM), a central processing unit (CPU), and a graphics processing unit (GPU). The system computer may comprise a tensor processing unit (TPU). The system computer may comprise sufficient RAM, storage space, CPU power, and GPU power to perform operations to detect and identify a target. The prediction sensor may provide images of sufficient resolution on which to perform operations to detect and identify an object. In some embodiments, the prediction sensor may be a camera, such as a charge-coupled device (CCD) camera or a complementary metal-oxide-semiconductor (CMOS) camera, a LIDAR detector, an infrared sensor, an ultraviolet sensor, an x-ray detector, or any other sensor capable of generating an image.
A targeting module of the present disclosure may be configured to target an object tracked by a prediction module. In some embodiments, the targeting module may direct an implement toward the object to manipulate the object. For example, the targeting module may be configured to direct a laser beam toward a plant to burn the plant. In another example, the targeting module may be configured to direct a grabbing tool to grab the object. In another example, the targeting module may direct a spraying tool to spray fluid at the object. In some embodiments, the object may be a weed, a plant, an insect, a pest, a field, a piece of debris, an obstruction, a region of a surface, or any other object that may be manipulated. The targeting module may be configured to receive a predicted location of an object of interest from the prediction module and point the targeting camera or targeting sensor toward the predicted location. In some embodiments, the targeting module may direct an implement, such as a laser, toward the predicted location. The position of the targeting sensor and the position of the implement may be coupled. In some embodiments, a plurality of targeting modules are in communication with the prediction module.
The targeting module may comprise a targeting control module. In some embodiments, the targeting control module may control the targeting sensor, the implement, or both. In some embodiments, the targeting control module may comprise an optical control system comprising optical components configured to control an optical path (e.g., a laser beam path or a camera imaging path). The targeting control module may comprise software-driven electrical components capable of controlling activation and deactivation of the implement. Activation or deactivation may depend on the presence or absence of an object as detected by the targeting camera. Activation or deactivation may depend on the position of the implement relative to the target object location. In some embodiments, the targeting control module may activate the implement, such as a laser emitter, when an object is identified and located by the prediction system. In some embodiments, the targeting control module may activate the implement when the range or target area of the implement is positioned to overlap with the target object location.
The targeting control module may deactivate the implement once the object has been manipulated, such as grabbed, sprayed, burned, or irradiated; the region comprising the object has been targeted with the implement; the object is no longer identified by the target prediction module; a designated period of time has elapsed; or any combination thereof. For example, the targeting control module may deactivate the emitter once a region on the surface comprising a plant has been scanned by the beam, once the plant has been irradiated or burned, or once the beam has been activated for a pre-determined period of time.
The prediction modules and the targeting modules described herein may be used in combination to locate, identify, and target an object with an implement. The targeting control module may comprise an optical control system as described herein. The prediction module and the targeting module may be in communication, for example electrical or digital communication. In some embodiments, the prediction module and the targeting module are directly or indirectly coupled. For example, the prediction module and the targeting module may be coupled to a support structure. In some embodiments, the prediction module and the targeting module are configured on or coupled to a vehicle, such as the vehicle shown in
The targeting module may comprise a system controller, for example a system computer having storage, random access memory (RAM), a central processing unit (CPU), and a graphics processing unit (GPU). The system computer may comprise a tensor processing unit (TPU). The system computer may comprise sufficient RAM, storage space, CPU power, and GPU power to perform operations to detect and identify a target. The targeting sensor may provide images of sufficient resolution on which to perform operations to match an object to an object identified in a prediction image.
Banding may be used to designate bands, or regions of interest, on a surface (e.g., crop field) to be maintained by an automated plant targeting system of the present disclosure. In some embodiments, a band may correspond to a crop seedline (e.g., a line along which seeds were planted in a crop field). These bands are designated areas within a digital representation of the crop field that are identified and used by the autonomous plant targeting system for the purpose of crop maintenance. Banding may comprise statically or dynamically creating sub-sections of a virtual region (e.g., a virtual representation of a surface). These bands are virtual zones that encompass the seedlines and a specified width around them. The width of these bands can be predetermined or dynamically calculated based on real-time data. An autonomous plant targeting system of the present disclosure may implement banding to limit the area in which plants are targeted. Benefits of banding may include reducing the number of potential targets to be targeted (e.g., irradiated with a laser), shortening field maintenance times, and filtering out incomplete data, such as peripheral data that is not fully within view of a camera (e.g., a prediction camera or a targeting camera).
The banding methods described herein may facilitate seedline prioritization, focusing automated crop maintenance (e.g., using an autonomous plant targeting system) on crop seedlines rather than the entire field. Examples of crops that may be maintained using the methods described herein include onion, pepper, strawberry, carrot, corn, soybeans, barley, oats, wheat, alfalfa, cotton, hay, tobacco, rice, sorghum, tomato, potato, grape, rice, lettuce, bean, pea, sugar beet, or brassica (e.g., broccoli, cauliflower, mustard, kale, or brussels sprouts). With seedline prioritization, automated crop maintenance may focus on targeting plants (e.g., weeds or crops to be thinned) around the seedlines and purposefully, ignoring the rest of the field. Less precise plant removal methods (e.g., conventional crop maintenance or weeding techniques) may be used on the outside regions (e.g., the regions between the seedline bands), enabling a high precision system (e.g., an autonomous plant targeting system) to focus on the seedline bands that the less precise methods would not be able to maintain without damaging the crop.
In some embodiments, bands may be static. Static bands may be manually designated within a virtual space (e.g., a virtual representation of a surface) by a user. For example, a user may manually designate rows along which crops were planted as bands and regions between crop lines or seedlines as out of band regions. Accuracy of static banding may be limited by uncertainty or error of vehicle navigation (e.g., navigation of a human-driven tractor or a self-driving plant targeting system), seedline irregularity, or both. Seedline irregularities may result from planting inconsistencies (e.g., caused by the planting equipment not planting in a straight line). Seedline irregularities may contribute to navigation uncertainty. Navigation uncertainty and seedline errors may cause the seedline to shift laterally relative to the autonomous plant targeting system, resulting in shifting of a static banding region relative to the seedline.
In some embodiments, the user can begin by uploading or accessing a map of the crop field within the application. This map may be generated from satellite imagery, aerial photography, or data collected by field sensors or drones. The application displays the field map, allowing the user to visually identify the rows where crops are planted. These rows may be represented as lines or highlighted areas on the map. Using tools within the application, the user can draw or overlay virtual bands directly onto the map along the identified crop rows. The software may provide options to set the width of the bands, which could be based on the type of crop or the specific maintenance requirements. The application automatically designates the regions between the user-defined bands as out of band regions. Alternatively, the user may manually designate these areas using a similar drawing or selection tool. The user can review and adjust the positions and widths of the bands and out of band regions as needed. Once satisfied, the user confirms the designations, and the information is saved within the system. The designated band information may then be integrated with the autonomous crop maintenance system. This integration allows the system to focus its maintenance activities, such as weeding or thinning, within the specified bands and ignore the out of band regions.
In some embodiments, bands may be dynamic. Dynamic bands may be generated in real time, such as while an autonomous plant targeting system is traveling through a crop field. For example, dynamic bands may be based on predicted crop lines or seedlines. Predicted crop lines may be determined from detected information (e.g., plant locations), optionally using some user input, such as seedline spacing, width, or distance.
Crop line may be predicted based on one or more methods. As a nonlimiting example, high-resolution images of the field are captured using cameras or drones. Image processing algorithms analyze these images to identify patterns that correspond to rows of crops. The system can use edge detection, color differentiation, or pattern recognition to distinguish crop lines from the soil or other background elements. LIDAR or RADAR may be used to create detailed topographical maps of the field. By measuring the distance to the ground and plants, the system can determine the raised areas where crops are likely planted, thus predicting the crop lines. If the planting equipment was equipped with GPS during seeding, the system could retrieve this data to predict the crop lines based on the recorded planting paths. In still other nonlimiting embodiments, previous records of crop line locations, planting patterns, and field layouts may be used to predict current crop lines. In other example embodiments, a machine learning model can be trained on a dataset that includes various field images, sensor data, and known crop line locations. Once trained, the model can predict crop lines in new datasets by identifying similar patterns and features.
An example of an implementation of banding and seedline tracking is described with reference to
An example of a seedline tracking method is described with reference to
Additional methods that may be used to identify seedlines and designate bands around them. For example, a method of seedline identification may utilize a search radius parameter. For an identified crop, a search may be performed within a set radius. Crops within the search radius are clustered and the search is expanded using the crops in the cluster. A crop within the search radius of a crop in the cluster becomes part of the cluster. Using the identified clusters, a statistical averaging with outlier filtering may be performed. The location of the statistical average of each cluster may be used as the estimated position of a seedline. Alternative clustering methods may also be utilized to cluster identified crops. In some example embodiments, once clusters are formed, the system calculates the centroid of each cluster by statistically averaging the positions of the crops, while simultaneously applying outlier rejection algorithms to enhance the accuracy of the seedline position estimation. In addition to radius-based clustering, other data clustering algorithms such as DBSCAN (Density-Based Spatial Clustering of Applications with Noise), OPTICS (Ordering Points To Identify the Clustering Structure), or agglomerative hierarchical clustering can be utilized to identify groups of crops that form seedlines based on spatial proximity and density criteria.
The methods described herein may be implemented by an optical control system, such as a laser optical system, to target an object of interest. For example, an optical system may be used to target an object of interest identified in an image or representation collected by a first sensor, such as a prediction sensor, and locate the same object in an image or representation collected by a second sensor, such as a targeting sensor. In some embodiments, the first sensor is a prediction camera and the second sensor is a targeting camera. Targeting the object may comprise precisely locating the object using the targeting sensor and targeting the object with an implement.
Described herein are optical control systems for directing a beam, for example a light beam, toward a target location on a surface, such as a location of an object of interest. In some embodiments, the implement is a laser. However, other implements are within the scope of the present disclosure, including but not limited to a grabbing implement, a spraying implement, a planting implement, a harvesting implement, a pollinating implement, a marking implement, a blowing implement, or a depositing implement.
In some embodiments, an emitter is configured to direct a beam along an optical path, for example a laser path. In some embodiments, the beam comprises electromagnetic radiation, for example light, radio waves, microwaves, or x-rays. In some embodiments, the light is visible light, infrared light, or ultraviolet light. The beam may be coherent. In one embodiment, the emitter is a laser, such as an infrared laser.
One or more optical elements may be positioned in a path of the beam. The optical elements may comprise a beam combiner, a lens, a reflective element, or any other optical elements that may be configured to direct, focus, filter, or otherwise control light. The elements may be configured in the order of the beam combiner, followed by a first reflective element, followed by a second reflective element, in the direction of the beam path. In another example, one or both of the first reflective element or the second reflective element may be configured before the beam combiner, in order of the direction of the beam path. In another example, the optical elements may be configured in the order of the beam combiner, followed by the first reflective element in order of the direction of the beam path. In another example, one or both of the first reflective element or the second reflective element may be configured before the beam combiner, in the direction of the beam path. Any number of additional reflective elements may be positioned in the beam path.
The beam combiner may also be referred to as a beam combining element. In some embodiments, the beam combiner may be a zinc selenide (ZnSe), zinc sulfide (ZnS), or germanium (Ge) beam combiner. For example, the beam combiner may be configured to transmit infrared light and reflect visible light. In some embodiments, the beam combiner may be a dichroic mirror. In some embodiments, the beam combiner may be configured to pass electromagnetic radiation having a wavelength longer than a cutoff wavelength and reflect electromagnetic radiation having a wavelength shorter than the cutoff wavelength. In some embodiments, the beam combiner may be configured to pass electromagnetic radiation having a wavelength shorter than a cutoff wavelength and reflect electromagnetic radiation having a wavelength longer than the cutoff wavelength. In other embodiments, the beam combiner may be a polarizing beam splitter, a long pass filter, a short pass filter, or a band pass filter.
An optical control system of the present disclosure may further comprise a lens positioned in the optical path. In some embodiments, a lens may be a focusing lens positioned such that the focusing lens focuses the beam, the scattered light, or both. For example, a focusing lens may be positioned in the visible light path to focus the scattered light onto the targeting camera. In some embodiments, a lens may be a defocusing lens positioned such that the defocusing lens defocuses the beam, the scattered light, or both. In some embodiments, the lens may be a collimating lens positioned such that the collimating lens collimates the beam, the scattered light, or both. In some embodiments, two or more lenses may be positioned in the optical path. For example, two lenses may be positioned in the optical path in series to expand or narrow the beam.
The positions and orientations of one or both of the first reflective element and the second reflective element may be controlled by one or more actuators. In some embodiments, an actuator may be a motor, a solenoid, a galvanometer, or a servo. For example, the position of the first reflective element may be controlled by a first actuator, and the position and orientation of the second reflective element may be controlled by a second actuator. In some embodiments, a single reflective element may be controlled by a plurality of actuators. For example, the first reflective element may be controlled by a first actuator along a first axis and a second actuator along a second axis. Optionally, the mirror may be controlled by a first actuator, a second actuator, and a third actuator, providing multi-axis control of the mirror. In some embodiments, a single actuator may control a reflective element along one or more axes. In some embodiments, a single reflective element may be controlled by a single actuator.
An actuator may change a position of a reflective element by rotating the reflective element, thereby changing an angle of incidence of a beam encountering the reflective element. Changing the angle of incidence may cause a translation of the position at which the beam encounters the surface. In some embodiments, the angle of incidence may be adjusted such that the position at which the beam encounters the surface is maintained while the optical system moves with respect to the surface. In some embodiments, the first actuator rotates the first reflective element about a first rotational axis, thereby translating the position at which the beam encounters the surface along a first translational axis, and the second actuator rotates the second reflective element about a second rotational axis, thereby translating the position at which the beam encounters the surface along a second translational axis. In some embodiments, a first actuator and a second actuator rotate a first reflective element about a first rotational axis and a second rotational axis, thereby translating the position at which the beam encounters the surface of the first reflective element along a first translational axis and a second translational axis. For example, a single reflective element may be controlled by a first actuator and a second actuator, providing translation of the position at which the beam encounters the surface along a first translation axis and a second translation axis with a single reflective element controlled by two actuators. In another example, a single reflective element may be controlled by one, two, or three actuators.
The first translational axis and the second translational axis may be orthogonal. A coverage area on the surface may be defined by a maximum translation along the first translational axis and a maximum translation along the second translation axis. One or both of the first actuator and the second actuator may be servo-controlled, piezoelectric actuated, piezo inertial actuated, stepper motor-controlled, galvanometer-driven, linear actuator-controlled, or any combination thereof. One or both of the first reflective element and the second reflective element may be a mirror; for example, a dichroic mirror, or a dielectric mirror; a prism; a beam splitter; or any combination thereof. In some embodiments, one or both of the first reflective element and the second reflective element may be any element capable of deflecting the beam.
A targeting camera may be positioned to capture light, for example visible light, traveling along a visible light path in a direction opposite the beam path, for example laser path. The light may be scattered by a surface, such as the surface with an object of interest, or an object, such as an object of interest, and travel toward the targeting camera along visible light path. In some embodiments, the targeting camera is positioned such that it captures light reflected off of the beam combiner. In other embodiments, the targeting camera is positioned such that it captures light transmitted through the beam combiner. With the capture of such light, the targeting camera may be configured to image a target field of view on a surface. The targeting camera may be coupled to the beam combiner, or the targeting camera may be coupled to a support structure supporting the beam combiner. In one embodiment, the targeting camera does not move with respect to the beam combiner, such that the targeting camera maintains a fixed position relative to the beam combiner.
An optical control system of the present disclosure may further comprise an exit window positioned in the beam path. In some embodiments, the exit window may be the last optical element encountered by the beam prior to exiting the optical control system. The exit window may comprise a material that is substantially transparent to visible light, infrared light, ultraviolet light, or any combination thereof. For example, the exit window may comprise glass, quartz, fused silica, zinc selenide, zinc sulfide, a transparent polymer, or a combination thereof. In some embodiments, the exit window may comprise a scratch-resistant coating, such as a diamond coating. The exit window may prevent dust, debris, water, or any combination thereof from reaching the other optical elements of the optical control system. In some embodiments, the exit window may be part of a protective casing surrounding the optical control system.
After exiting the optical control system, the beam may be directed along beam path toward a surface. In some embodiments, the surface contains an object of interest, for example a plant. Rotational motions of reflective elements may produce a laser sweep along a first translational axis and a laser sweep along a second translational axis. The rotational motions of reflective elements may control the location at which the beam encounters the surface. For example, the rotation motions of reflective elements may move the location at which the beam encounters the surface to a position of an object of interest on the surface. In some embodiments, the beam is configured to damage the object of interest. For example, the beam may comprise electromagnetic radiation, and the beam may irradiate the object. In another example, the beam may comprise infrared light, and the beam may burn the object. In some embodiments, one or both of the reflective elements may be rotated such that the beam scans an area surrounding and including the object.
A prediction camera or prediction sensor may coordinate with an optical control system, such as optical control system, to identify and locate objects to target. The prediction camera may have a field of view that encompasses a coverage area of the optical control system covered by amiable laser sweeps. The prediction camera may be configured to capture an image or representation of a region that includes the coverage area to identify and select an object to target. The selected object may be assigned to the optical control system. In some embodiments, the prediction camera field of view and the coverage area of the optical control system may be temporally separated such that prediction camera field of view encompasses the target at a first time and the optical control system coverage area encompasses the target at a second time. Optionally, the prediction camera, the optical control system, or both may move with respect to the target between the first time and the second time.
In some embodiments, a plurality of optical control systems may be combined to increase a coverage area on a surface. The plurality of optical control systems may be configured such that the laser sweep along a translational axis of each optical control system overlaps with the laser sweep along the translational axis of the neighboring optical control system. The combined laser sweep defines a coverage area that may be reached by at least one beam of a plurality of beams from the plurality of optical control systems. One or more prediction cameras may be positioned such that a prediction camera field of view covered by the one or more prediction cameras fully encompasses the coverage area. In some embodiments, a detection system may comprise two or more prediction cameras, each having a field of view. The fields of view of the prediction cameras may be combined to form a prediction field of view that fully encompass the coverage area. In some embodiments, the prediction field of view does not fully encompass the coverage area at a single time point but may encompass the coverage area over two or more time points (e.g., image frames). Optionally, the prediction camera or cameras may move relative to the coverage area over the course of the two or more time points, enabling temporal coverage of the coverage area. The prediction camera or prediction sensor may be configured to capture an image or representation of a region that includes coverage area to identify and select an object to target. The selected object may be assigned to one of the plurality of optical control systems based on the location of the object and the area covered by laser sweeps of the individual optical control systems.
The plurality of optical control systems may be configured on a vehicle, such as vehicle 100 illustrated in
The vehicle may be configured to navigate a surface containing a plurality of objects, including one or more objects of interest, for example a crop field containing a plurality of plants and one or more plants. The vehicle may comprise one or more of a plurality of wheels, a power source, a motor, a prediction camera, or any combination thereof. In some embodiments, the vehicle has sufficient clearance above the surface to drive over a plant, for example a crop, without damaging the plant. In some embodiments, a space between an inside edge of a left wheel and an inside edge of a right wheel is wide enough to pass over a row of plants without damaging the plants. In some embodiments, a distance between an outside edge of a left wheel and an outside edge of a right wheel is narrow enough to allow the vehicle to pass between two rows of plants, for example two rows of crops, without damaging the plants. In one embodiment, the vehicle comprising the plurality of wheels, the plurality of optical control systems, and the prediction camera may navigate rows of crops and emit a beam of the plurality of beams toward a target, for example a plant, thereby burning or irradiating the plant.
The methods described herein may be implemented using a computer system. In some embodiments, the systems described herein include a computer system. In some embodiments, a computer system may implement the methods autonomously without human input. In some embodiments, a computer system may implement the methods based on instructions provided by a human user through a detection terminal.
The detection interface may support various communication protocols to ensure compatibility and interoperability with different devices and systems. These protocols could include cellular networks (e.g., LTE, 5G) for remote communication, enabling the user to control and receive updates from the system from virtually anywhere, and LoRaWAN for long-range, low-power communication, particularly useful in rural or expansive agricultural settings. Regarding the hardware associated with the detection interface 1420, the detection interface may include hardware components such as: transceivers capable of both transmitting and receiving signals, signal amplifiers to boost communication range and quality, microcontrollers or processors to manage communication protocols and data handling, power management circuits to ensure efficient energy use, especially when the system is battery-powered. In other example embodiments, the detection interface enables several functional capabilities, such as: real-time data transmission, allowing the user to receive live updates on the system's status and the progress of the crop thinning process; remote control commands, enabling the user to start, stop, or adjust the operation of the autonomous plant targeting system from the detection terminal; software updates and configuration changes that can be sent to the autonomous system to improve performance or modify operational parameters; and diagnostic data retrieval for maintenance and troubleshooting purposes.
The detection terminal 1400 further includes detection engine 1410. The detection engine may receive information regarding the status of a detection system, for example a detection system of
In other example embodiments, the detection engine may receive additional types of information, including: environmental data such as: temperature and humidity levels; soil moisture content; weather conditions impacting the operation, like wind speed and precipitation; and light intensity and spectral data for assessing photosynthetic activity. The detection engine may also receive metrics related to the performance and efficiency of the autonomous system, such as: energy consumption and battery life estimates; area coverage rate, indicating how quickly the system is progressing through the field; number of plants thinned per unit of time; operational logs detailing system activity and any errors or malfunctions. Furthermore, the detection engine may receive Data points that provide insights into the health and status of the crops, such as spectral analysis results that may indicate plant stress or disease; growth metrics, including plant height and leaf area index (LAI); imagery data that could reveal signs of pest infestation or nutrient deficiencies. Even further, the detection engine may receive information related to the autonomous system's navigation and positioning within the field, such as GPS coordinates and path tracking data; obstacle detection and avoidance logs; and alignment with crop rows and accuracy of movement relative to the planned path.
Actual embodiments of the illustrated devices will have more components included therein which are known to one of ordinary skill in the art. For example, each of the illustrated devices will have a power source, one or more processors, computer-readable media for storing computer-executable instructions, and so on. These additional components are not illustrated herein for the sake of clarity.
In some examples, the procedures described herein may be performed by a computing device or apparatus, such as a computing device having the computing device architecture 1600 shown in
The components of the computing device can be implemented in circuitry. For example, the components can include and/or can be implemented using electronic circuits or other electronic hardware, which can include one or more programmable electronic circuits (e.g., microprocessors, graphics processing units (GPUs), digital signal processors (DSPs), central processing units (CPUs), and/or other suitable electronic circuits), and/or can include and/or be implemented using computer software, firmware, or any combination thereof, to perform the various operations described herein.
Procedures 500 and 600 are illustrated as logical flow diagrams, the operation of which represent a sequence of operations that can be implemented in hardware, computer instructions, or a combination thereof. In the context of computer instructions, the operations represent computer-executable instructions stored on one or more computer-readable storage media that, when executed by one or more processors, perform the recited operations. Generally, computer-executable instructions include routines, programs, objects, components, data structures, and the like that perform particular functions or implement particular data types. The order in which the operations are described is not intended to be construed as a limitation, and any number of the described operations can be combined in any order and/or in parallel to implement the processes.
Additionally, the processes described herein may be performed under the control of one or more computer systems configured with executable instructions and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) executing collectively on one or more processors, by hardware, or combinations thereof. As noted above, the code may be stored on a computer-readable or machine-readable storage medium, for example, in the form of a computer program comprising a plurality of instructions executable by one or more processors. The computer-readable or machine-readable storage medium may be non-transitory.
Computing device architecture 1600 can include a cache of high-speed memory connected directly with, in close proximity to, or integrated as part of processor 1610. Computing device architecture 1600 can copy data from memory 1615 and/or the storage device 1630 to cache 1612 for quick access by processor 1610. In this way, the cache can provide a performance boost that avoids processor 1610 delays while waiting for data. These and other modules can control or be configured to control processor 1610 to perform various actions. Other computing device memory 1615 may be available for use as well. Memory 1615 can include multiple different types of memory with different performance characteristics. Processor 1610 can include any general purpose processor and a hardware or software service, such as service 1 1632, service 2 1634, and service 3 1636 stored in storage device 1630, configured to control processor 1610 as well as a special-purpose processor where software instructions are incorporated into the processor design. Processor 1610 may be a self-contained system, containing multiple cores or processors, a bus, memory controller, cache, etc. A multi-core processor may be symmetric or asymmetric.
To enable user interaction with the computing device architecture, input device 1645 can represent any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech and so forth. Output device 1635 can also be one or more of a number of output mechanisms known to those of skill in the art, such as a display, projector, television, speaker device, etc. In some instances, multimodal computing devices can enable a user to provide multiple types of input to communicate with computing device architecture 1600. Communication interface 1640 can generally govern and manage the user input and computing device output. There is no restriction on operating on any particular hardware arrangement and therefore the basic features here may easily be substituted for improved hardware or firmware arrangements as they are developed.
Storage device 1630 is a non-volatile memory and can be a hard disk or other types of computer readable media which can store data that are accessible by a computer, such as magnetic cassettes, flash memory cards, solid state memory devices, digital versatile disks, cartridges, random access memories (RAMs) 1625, read only memory (ROM) 1620, and hybrids thereof. Storage device 1630 can include services 1632, 1634, 1636 for controlling processor 1610. Other hardware or software modules are contemplated. Storage device 1630 can be connected to the computing device connection 1605. In one aspect, a hardware module that performs a particular function can include the software component stored in a computer-readable medium in connection with the necessary hardware components, such as processor 1610, connection 1605, output device 1635, and so forth, to carry out the function.
Various example embodiments of the disclosure are discussed in detail below. While specific implementations are discussed, it should be understood that this description is for illustration purposes only. A person skilled in the relevant art will recognize that other components and configurations may be used without parting from the spirit and scope of the disclosure. Thus, the following description and drawings are illustrative and are not to be construed as limiting. Numerous specific details are described to provide a thorough understanding of the disclosure. However, in certain instances, well-known or conventional details are not described in order to avoid obscuring the description. References to one or an embodiment in the present disclosure can be references to the same embodiment or any embodiment; and such references mean at least one of the example embodiments.
Reference to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosure. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative example embodiments mutually exclusive of other example embodiments. Moreover, various features are described which may be exhibited by some example embodiments and not by others. Any feature of one example can be integrated with or used with any other feature of any other example.
The terms used in this specification generally have their ordinary meanings in the art, within the context of the disclosure, and in the specific context where each term is used. Alternative language and synonyms may be used for any one or more of the terms discussed herein, and no special significance should be placed upon whether or not a term is elaborated or discussed herein. In some cases, synonyms for certain terms are provided. A recital of one or more synonyms does not exclude the use of other synonyms. The use of examples anywhere in this specification including examples of any terms discussed herein is illustrative only and is not intended to further limit the scope and meaning of the disclosure or of any example term. Likewise, the disclosure is not limited to various example embodiments given in this specification.
Without intent to limit the scope of the disclosure, examples of instruments, apparatus, methods, and their related results according to the example embodiments of the present disclosure are given below. Note that titles or subtitles may be used in the examples for convenience of a reader, which in no way should limit the scope of the disclosure. Unless otherwise defined, technical and scientific terms used herein have the meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. In the case of conflict, the present document, including definitions will control.
Additional features and advantages of the disclosure will be set forth in the description which follows, and in part will be obvious from the description, or can be learned by practice of the herein disclosed principles. The features and advantages of the disclosure can be realized and obtained by means of the instruments and combinations particularly pointed out in the appended claims. These and other features of the disclosure will become more fully apparent from the following description and appended claims or can be learned by the practice of the principles set forth herein.
For clarity of explanation, in some instances the present technology may be presented as including individual functional blocks representing devices, device components, steps or routines in a method embodied in software, or combinations of hardware and software.
In the drawings, some structural or method features may be shown in specific arrangements and/or orderings. However, it should be appreciated that such specific arrangements and/or orderings may not be required. Rather, in some embodiments, such features may be arranged in a different manner and/or order than shown in the illustrative figures. Additionally, the inclusion of a structural or method feature in a particular figure is not meant to imply that such feature is required in all embodiments and, in some embodiments, it may not be included or may be combined with other features.
While the concepts of the present disclosure are susceptible to various modifications and alternative forms, specific embodiments thereof have been shown by way of example in the drawings and will be described herein in detail. It should be understood, however, that there is no intent to limit the concepts of the present disclosure to the particular forms disclosed, but on the contrary, the intention is to cover all modifications, equivalents, and alternatives consistent with the present disclosure and the appended claims.
The term “computer-readable medium” includes, but is not limited to, portable or non-portable storage devices, optical storage devices, and various other mediums capable of storing, containing, or carrying instruction(s) and/or data. A computer-readable medium may include a non-transitory medium in which data can be stored and that does not include carrier waves and/or transitory electronic signals propagating wirelessly or over wired connections. Examples of a non-transitory medium may include, but are not limited to, a magnetic disk or tape, optical storage media such as compact disk (CD) or digital versatile disk (DVD), flash memory, memory or memory devices. A computer-readable medium may have stored thereon code and/or machine-executable instructions that may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and/or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, or the like.
In some embodiments the computer-readable storage devices, mediums, and memories can include a cable or wireless signal containing a bit stream and the like. However, when mentioned, non-transitory computer-readable storage media expressly exclude media such as energy, carrier signals, electromagnetic waves, and signals per se.
Specific details are provided in the description above to provide a thorough understanding of the embodiments and examples provided herein. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For clarity of explanation, in some instances the present technology may be presented as including individual functional blocks including functional blocks comprising devices, device components, steps or routines in a method embodied in software, or combinations of hardware and software. Additional components may be used other than those shown in the figures and/or described herein. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.
Individual embodiments may be described above as a process or method which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
Processes and methods according to the above-described examples can be implemented using computer-executable instructions that are stored or otherwise available from computer-readable media. Such instructions can include, for example, instructions and data which cause or otherwise configure a general-purpose computer, special purpose computer, or a processing device to perform a certain function or group of functions. Portions of computer resources used can be accessible over a network. The computer executable instructions may be, for example, binaries, intermediate format instructions such as assembly language, firmware, source code, etc. Examples of computer-readable media that may be used to store instructions, information used, and/or information created during methods according to described examples include magnetic or optical disks, flash memory, USB devices provided with non-volatile memory, networked storage devices, and so on.
Devices implementing processes and methods according to these disclosures can include hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof, and can take any of a variety of form factors. When implemented in software, firmware, middleware, or microcode, the program code or code segments to perform the necessary tasks (e.g., a computer-program product) may be stored in a computer-readable or machine-readable medium. A processor(s) may perform the necessary tasks. Typical examples of form factors include laptops, smart phones, mobile phones, tablet devices or other small form factor personal computers, personal digital assistants, rackmount devices, standalone devices, and so on. Functionality described herein also can be embodied in peripherals or add-in cards. Such functionality can also be implemented on a circuit board among different chips or different processes executing in a single device, by way of further example.
The instructions, media for conveying such instructions, computing resources for executing them, and other structures for supporting such computing resources are example means for providing the functions described in the disclosure.
The various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, firmware, or combinations thereof. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The techniques described herein may also be implemented in electronic hardware, computer software, firmware, or any combination thereof. Such techniques may be implemented in any of a variety of devices such as general-purpose computers, wireless communication device handsets, or integrated circuit devices having multiple uses including application in wireless communication device handsets and other devices. Any features described as modules or components may be implemented together in an integrated logic device or separately as discrete but interoperable logic devices. If implemented in software, the techniques may be realized at least in part by a computer-readable data storage medium comprising program code including instructions that, when executed, performs one or more of the methods described above. The computer-readable data storage medium may form part of a computer program product, which may include packaging materials. The computer-readable medium may comprise memory or data storage media, such as random access memory (RAM) such as synchronous dynamic random access memory (SDRAM), read-only memory (ROM), non-volatile random access memory (NVRAM), electrically erasable programmable read-only memory (EEPROM), FLASH memory, magnetic or optical data storage media, and the like. The techniques additionally, or alternatively, may be realized at least in part by a computer-readable communication medium that carries or communicates program code in the form of instructions or data structures and that can be accessed, read, and/or executed by a computer, such as propagated signals or waves.
The program code may be executed by a processor, which may include one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, an application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Such a processor may be configured to perform any of the techniques described in this disclosure. A general-purpose processor may be a microprocessor; but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Accordingly, the term “processor,” as used herein may refer to any of the foregoing structure, any combination of the foregoing structure, or any other structure or apparatus suitable for implementation of the techniques described herein.
While illustrative embodiments have been illustrated and described, it will be appreciated that various changes can be made therein without departing from the spirit and scope of the disclosure.
In the foregoing description, aspects of the application are described with reference to specific embodiments thereof, but those skilled in the art will recognize that the application is not limited thereto. Thus, while illustrative embodiments of the application have been described in detail herein, it is to be understood that the inventive concepts may be otherwise variously embodied and employed, and that the appended claims are intended to be construed to include such variations, except as limited by the prior art. Various features and aspects of the above-described application may be used individually or jointly. Further, embodiments can be utilized in any number of environments and applications beyond those described herein without departing from the broader spirit and scope of the specification. The specification and drawings are, accordingly, to be regarded as illustrative rather than restrictive. For the purposes of illustration, methods were described in a particular order. It should be appreciated that in alternate embodiments, the methods may be performed in a different order than that described.
One of ordinary skill will appreciate that the less than (“<”) and greater than (“>”) symbols or terminology used herein can be replaced with less than or equal to (“≤”) and greater than or equal to (“≥”) symbols, respectively, without departing from the scope of this description.
Where components are described as being “configured to” perform certain operations, such configuration can be accomplished, for example, by designing electronic circuits or other hardware to perform the operation, by programming programmable electronic circuits (e.g., microprocessors, or other suitable electronic circuits) to perform the operation, or any combination thereof.
The phrase “coupled to” refers to any component that is physically connected to another component either directly or indirectly, and/or any component that is in communication with another component (e.g., connected to the other component over a wired or wireless connection, and/or other suitable communication interface) either directly or indirectly.
Claim language or other language reciting “at least one of” a set and/or “one or more” of a set indicates that one member of the set or multiple members of the set (in any combination) satisfy the claim. For example, claim language reciting “at least one of A and B” means A, B, or A and B. In another example, claim language reciting “at least one of A, B, and C” means A, B, C, or A and B, or A and C, or B and C, or A and B and C. The language “at least one of” a set and/or “one or more” of a set does not limit the set to the items listed in the set. For example, claim language reciting “at least one of A and B” can mean A, B, or A and B, and can additionally include items not listed in the set of A and B.
As used herein, the terms “about” and “approximately,” in reference to a number, is used herein to include numbers that fall within a range of 10%, 5%, or 1% in either direction (greater than or less than) the number unless otherwise stated or otherwise evident from the context (except where such number would exceed 100% of a possible value).
While preferred embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention. It is intended that the following claims define the scope of the invention and that methods and structures within the scope of these claims and their equivalents be covered thereby.
Although embodiments of the present invention have been described herein in the context of a particular implementation in a particular environment for a particular purpose, those skilled in the art will recognize that its usefulness is not limited thereto and that the embodiments of the present invention can be beneficially implemented in other related environments for similar purposes. The invention should therefore not be limited by the above described embodiments, method, and examples, but by all embodiments within the scope and spirit of the invention as claimed. The invention is not to be limited in terms of the particular embodiments described herein, which are intended as illustrations of various aspects. Many modifications and variations can be made without departing from its spirit and scope. Functionally equivalent systems, processes and apparatuses within the scope of the invention, in addition to those enumerated herein, may be apparent from the representative descriptions herein. Such modifications and variations are intended to fall within the scope of the appended claims. The invention is to be limited only by the terms of the appended claims, along with the full scope of equivalents to which such representative claims are entitled.
The preceding description of exemplary embodiments provides non-limiting representative examples referencing numerals to particularly describe features and teachings of different aspects of the invention. The embodiments described should be recognized as capable of implementation separately, or in combination, with other embodiments from the description of the embodiments. A person of ordinary skill in the art reviewing the description of embodiments should be able to learn and understand the different described aspects of the invention. The description of embodiments should facilitate understanding of the invention to such an extent that other implementations, not specifically covered but within the knowledge of a person of skill in the art having read the description of embodiments, would be understood to be consistent with an application of the invention.
This application claims priority to U.S. Provisional Application No. 63/444,863, filed Feb. 10, 2023, the contents of which are incorporated herein in its entirety. All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference.
| Number | Date | Country | |
|---|---|---|---|
| 63444863 | Feb 2023 | US |
