Patent Application
20150206255
This disclosure relates generally to precision agriculture and more specifically to varying seeding density across a cultivated field targeted discretely based upon remote sensing-measured spatial patterning in the crop canopy.
Precision agriculture technology is intended to achieve the highest possible yields from a cultivated field using a minimum of inputs, thereby controlling costs, conserving resources, and obtaining the highest possible profit. This technology generally includes varying the population density of seeds for the crop according to the soil capability, which include the physical and chemical conditions that contribute to or impede crop yield. The value provided by varying the population density of plants in a crop arises because portions of a field with high soil capability that can sustain high yields should receive greater density of seeds per unit area to support the desired higher yield. In locations within the field with poor soil capability, lower population densities are generally planted. Lower seed density can actually enhance yield in locations with poor soil capability through reduction of competition among individual plants for limiting water and nutrients. Variable density seeding and its benefits is an emerging science within precision agriculture. Crop-specific seeding densities are provided by most seed companies based on what the field sub-region can yield, but maps of spatially-variable yields for spatially-variable seed population densities are often not available or are fraught with errors.
To facilitate further description of the embodiments, the following drawings are provided in which:
For simplicity and clarity of illustration, the drawing figures illustrate the general manner of construction, and descriptions and details of well-known features and techniques may be omitted to avoid unnecessarily obscuring the present disclosure. Additionally, elements in the drawing figures are not necessarily drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated relative to other elements to help improve understanding of embodiments of the present disclosure. The same reference numerals in different figures denote the same elements.
The terms “first,” “second,” “third,” “fourth,” and the like in the description and in the claims, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms “include,” and “have,” and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, device, or apparatus that comprises a list of elements is not necessarily limited to those elements, but may include other elements not expressly listed or inherent to such process, method, system, article, device, or apparatus.
The terms “left,” “right,” “front,” “back,” “top,” “bottom,” “over,” “under,” and the like in the description and in the claims, if any, are used for descriptive purposes and not necessarily for describing permanent relative positions. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the apparatus, methods, and/or articles of manufacture described herein are, for example, capable of operation in other orientations than those illustrated or otherwise described herein.
The terms “couple,” “coupled,” “couples,” “coupling,” and the like should be broadly understood and refer to connecting two or more elements mechanically and/or otherwise. Two or more electrical elements may be electrically coupled together, but not be mechanically or otherwise coupled together. Coupling may be for any length of time, e.g., permanent or semi-permanent or only for an instant. “Electrical coupling” and the like should be broadly understood and include electrical coupling of all types. The absence of the word “removably,” “removable,” and the like near the word “coupled,” and the like does not mean that the coupling, etc. in question is or is not removable.
As defined herein, two or more elements are “integral” if they are comprised of the same piece of material. As defined herein, two or more elements are “non-integral” if each is comprised of a different piece of material.
As defined herein, “approximately” can, in some embodiments, mean within plus or minus ten percent of the stated value. In other embodiments, “approximately” can mean within plus or minus five percent of the stated value. In further embodiments, “approximately” can mean within plus or minus three percent of the stated value. In yet other embodiments, “approximately” can mean within plus or minus one percent of the stated value.
Various embodiments can include a method for prescribing variable seed density planting. The method can include obtaining first EOS data collected approximately on an estimated day (DOY′) during a past crop-growing season in which NDVI* data most closely resembles a spatial-yield pattern measured during harvest in the past crop-growing season. The method also can include converting the first EOS data to first reflectance data and first NDVI data. The method additionally can include calculating first NDVI* data on a per pixel basis for the first EOS data based on the first NDVI data using satellite scene statistics of the first EOS data. The method further can include generating an NDVI* map for a first field using the first NDVI* data for the first EOS data. The method additionally can include generating a variable seed density prescription map using the NDVI* map. The variable seed density prescription map can be spatially defined.
Several embodiments can include a system for prescribing variable seed density planting. The system can include one or more processing modules and one or more non-transitory memory storage modules storing computing instructions configured to run on the one or more processing modules and perform one or more acts. The one or more acts can include obtaining first EOS data collected approximately on an estimated day (DOY′) during a past crop-growing season in which NDVI* data most closely resembles a spatial-yield pattern measured during harvest in the past crop-growing season. The one or more acts also can include converting the first EOS data to first reflectance data and first NDVI data. The one or more acts additionally can include calculating first NDVI* data on a per pixel basis for the first EOS data based on the first NDVI data using satellite scene statistics of the first EOS data. The one or more acts further can include generating an NDVI* map for a first field using the first NDVI* data for the first EOS data. The one or more acts additionally can include generating a variable seed density prescription map using the NDVI* map. The variable seed density prescription map can be spatially defined.
In a number of embodiments, the systems and methods described herein can facilitate and/or perform variable density planting of seed optimized to the spatially-variable soil capability of each field. Using EOS data, each crop type in each region can be calibrated to determine when, during the growing season, the spatial variability of yield indicative of soil capability can be displayed by that crop type in a particular region having substantially the same climate and day length characteristics. This calibration can be used to determine when to obtain EOS snapshots to portray the yield pattern on each field. A map generated from a prior year(s) for this pattern can be used to optimize variable seeding density across the field through linear interpolation. The resulting prescriptions then can guide variable density seed planting, potentially across vast farmed regions.
In many embodiments, a time-specific snapshot of remotely sensed data taken at a time certain well in advance of harvest that displays the pattern of yield across the field can be used. The timing for this snapshot can be forecasted for each field based upon relationships calibrated for crop type and region using a method that clocks the development stages of the each cropped field. The remotely-sensed measure of yield variability can be determined using a vegetation index, NDVI*, that can be calibrated to remove confounding effects from the soil background and atmospheric effects. The resulting timed NDVI* map can provide a surrogate for relative yield that can be used to scale the application density of the desired input, such as seeds.
In a number of embodiments, the systems and methods described herein can provide a remotely-sensed surrogate of yield to make seed density prescriptions simply and accurately. Conventional densities for seeding combined with the systems and methods described herein can facilitate variable density seeding for widespread agricultural use. The systems and methods described herein can combine remote sensing, particularly using Earth observation satellite data (EOS) and computer automation, to rapidly deliver seed prescriptions to farmers at low cost across thousands of square miles. In some embodiments, EOS data also can includes data collected from manned and unmanned aircraft because, like EOS data, they are viewing the earth from above, only closer to the service.
There can be two major parts in the process of variable density seeding: the first part can be deriving a map to guide the variable density application, and the second can be implementing this prescription upon the field. The second part for this process can be done using conventional methods using various machines to accomplish varying the seeding density. The first part of deriving the map can involve development of data to represent the spatial soil capability on across the field.
Spatial soil capability can be derived using data from soils mapping. U.S. Department of Agriculture maps can be very general, as they were generally developed through extrapolation from relatively few points, and can be incapable for differentiating soil capability at a sufficiently fine resolution to guide variable density inputs such as seeding. Maps can also be derived using data from yield measured at harvest with technology available on farm equipment to assist generating maps based upon yield. Yields are measured during harvest with equipment that monitors the rate of intake of harvested grain for known positions in the field established by GPS during harvesting. Basing maps of soil capability on yield for varying seed populations can be result in inherently inaccurate yield measurements due to improper calibration, buildup of material that prevents accurate readings, cutting crops within partial width of the harvester intake, wear on the equipment, highly variable grain moisture content, and harvesting on slopes.
Variable density fertilizer application using EOS data in one embodiment without correcting the soil background in EOS data can induce considerable and highly variable error for NDVI values across fields with little or no vegetation cover, which can make the crop development stage difficult to be determined from early season measurements when the crop cover is incomplete and the soil surface is exposed. NDVI values can be highly influenced by reflectance of soil before the canopy closes. The use of bare soil reflectance as maps of soil brightness can be a poor index upon which to judge soil properties for supporting variable density seeding because surface soil water content can be unrelated to soil capability, yet can have a controlling influence on soil brightness. Highly reflectant exposed crop residue can have a profound effect upon soil brightness that bears no relationship to the underlying soil properties. Neither surface soil water expression nor exposed crop residues can be correlated to soil properties that create soil capability or support yield. Determining when to obtain EOS data for determination of yield patterns can have a significant impact. For example, timing the EOS snapshot for assessing spatial patterns as a period during the crop's last vegetative state, which in the U.S. corn belt is from mid-July to mid-August, can result in an erroneous mapping of spatial yield and resultant deficiencies in a seeding prescription determined from it.
Operational Remotely-Sensed Seed Density Prescription
In many embodiments, remote sensing with EOS data can be a practical solution for variable prescription of seed density optimized to the variable soil capability across a field. The accuracy desired for such precision prescription can be achieved using systems and methods that correct for the confounding effects of atmospheric aerosols and soil background, which can advantageously enable evaluation of crop canopies through the growing season and comparison from year to year. Such seasonal curves, in turn, can enable measuring crop stages using EOS data, alone. In several embodiments, the systems and methods described herein can enable calibrating and then forecasting when the spatial-yield pattern can be displayed by the crop canopies during a brief several-week period each year.
In a number of embodiments, the systems and method described herein can satisfies various criteria for remote sensing-based precision agricultural guidance for variable density seed planting prescription and application:
Correcting NDVI from Atmospheric and Soil Background Effects
Even though soil capability often can be variable throughout a given field, most farmers plant the same seed density across their fields, which can result in a waste of money and possibly even a loss of yield through the competition for limited resources in soils with poor capability. Where there is much higher soil capability, the potential exists to enhance yield through higher density of plants. Remote sensing can advantageously facilitate optimizing the seeding rate for a field.
The yield of a crop can be determined by its health and can be indicated by the greenness of the crop. Expression of greenness can be dependent upon the capability of the soil and enhancement through inputs made by the farmer. Soil capability can determine the density of plants that can be maintained at many locations throughout the cropped field. Remotely-sensed crop greenness can be portrayed by vegetation indices that can combine red and near infrared light from EOS data. Greenness, a term used here by convention, makes sense to the visual world, but paradoxically can be most accurately determined using reflected red light that is inversely proportional to the green vigor of the canopy. Plants appear green because chlorophyll strongly absorbs red light in the act of photosynthesis; green is simply what is not used and reflected back and visible to the human eye. Crop canopies reflect highly in the near infrared, as do many background surfaces, a common example being dry soils. However, the ratio of red versus near infrared light enables the use of vegetation indices to measure plant canopy vigor. NDVI is the most commonly used among such indices, as provided in Equation 1.
where NIR is the near infrared reflectance and Red is the red reflection within the digital data commonly measured by sensors borne on EOS platforms.
In its role as an estimator of canopy greenness, NDVI can be insufficiently accurate for use in precision agriculture due to confounding soil background reflectance and atmospheric aerosol effects of scatter and attenuation. Both effects can alter the plant vigor signal in NDVI. In many embodiments, the accuracy for NDVI to portray vegetation vigor can be enhanced by conversion to NDVI* that can stretch the NDVI values from zero to one to represent the full range of vegetation greenness from none to saturated as portrayed in EOS data. NDVI* can outperform all vegetation indices commonly used in the field of remote sensing. Conversion of NDVI to NDVI*, as provided in Equation 2, can correct for the error-inducing effects from soil background and atmospheric aerosols to provide accurate scaled index values appropriate for application to precision agriculture.
where NDVIi is the measured NDVI for the ith pixel, NDVIS is the saturated value for NDVI, and NDVI0 is the NDVI value representing bare soil.
In a number of embodiments, NDVI* can be calibrated using scene statistics, and can involve no specific ground target or ground-based measurements. NDVI0 can be the calibration for bare soil. There are times of the year when a maximally verdant target suitable for setting NDVIS can be missing in the scene, for example, during spring and fall when a crops are becoming established or are senescing prior to harvest. In various embodiments, the NDVIS value can be chosen as an empirical constant, as the peak value for non-cloudy scenes in an atmosphere relatively clear of aerosols occupies a known range that can be determined empirically. The choice of a set NDVI value to represent NDVIS can produce insignificant influence upon the resulting NDVI* values.
NDVI of bare soil can be regionally variable with nearly all values greater than zero, sometimes considerably so (for example NDVI of 0.2). The NDVI0 term in Equation 2 can correct for this elevated soil background. Without the soil background correction provided with NDVI*, crop response during a period critical to timing of the crop's seasonal growth and maturation can be unreliably measured using remote sensing.
Over time and in the absence of correction, rather than presenting an expected smooth growth curve, raw NDVI curves from growing crops can fluctuate in magnitude, often displaying an erroneous saw-tooth pattern due to variable atmospheric aerosol contents on the days that the images were collected. Aerosol effects can cause NDVI values to be depressed for images collected when atmospheric aerosol content is high. In several embodiments, NDVI* curves can correct this error to become relatively smooth as the crop progresses through the season. Because NDVI * can correct the NDVI signal for the effect of both atmospheric and soil background influences, it can enable remote sensing alone to perform a suite of useful agronomic analyses stemming from seasonal curves. For example, the phenologic stage of a cropped field can be determined from serial EOS snapshots converted to NDVI*. By contrast, NDVI can be unsuitable for this calculation due to the error it contains.
Clocking Function to Determine Crop Phenology
To be scalable across thousands of square miles and to be automatable, data for precision-agriculture input can be determined by remote sensing methods rather than relying upon record keeping and reporting (e.g., reporting by the farmer). Such manual reporting of critical information can be infeasible in practice because farmers can be extremely busy during the growing season and often cannot be relied upon to complete reporting when confronted by more immediate and pressing tasks. Advantageously, in a number of embodiments, an initiation date for each cropped field can be determined using NDVI* values collected through the first 45 days of the growing season for calculating a crop initiation point.
In several embodiments, determining an initiation point for a crop can enable clocking forward set numbers of days to predict growth stages according to math relationships determined by calibration for each crop type and farmed region. The term “region” as used herein can be defined as an area having substantially the same climate and a latitude within about three 3 degrees (about 200 miles).
For application to a farmed region, the clocking function can be determined using multiple EOS images. Data then can be extracted for calculations to represent conditions on each field growing a single crop type. A suite of multiple-date NDVI* values representative of the field can be accumulated through at least the initial approximately 45 days of crop growth. Either the field average or the field median can be extracted and plotted by day of year (DOY), incrementing from 1 to 365, to yield a time-wise crop growth curve that represents the field. These and other actions described herein can be completely automatable within the systems and methods described herein.
NDVI* can be a direct expression of the chlorophyll contained in the crop canopy. Like other allometric measurements of organisms (e.g., weight, length, etc.), growth of the crop and its photosynthetic capacity represented by NDVI*, describes a sigmoid or “S” shape. NDVI* forms an initial tail, followed by linear growth, followed by a plateau, therefore describing an S-shaped curve through the growing season, discounting the last stages of maturation and senescence with declining NDVI*.
Turning to the drawings,
In a number of embodiments, the clocking function combined with known growth stages for each field can permit calibration against AED to forecast when to perform treatments vital to the health and yield of the crop. For example, corn can be frequently fertilized at planting and again before tassel formation. The time of tasseling can be predicted accurately when calibrated as elapsed days from AED.
The initial tail of the sigmoid NDVI* crop growth curve, as shown in
To account for the initial tail of the growth curve and the role played by the delaying effect of low temperature, conventional calculation and accounting of heat units, also called growing degree days, can be used. Heat units can involve cumbersome entry and tracking of temperature data, with mathematical calculations made from these data for each field and each crop type. In several embodiments, the clocking function can bypass the initial temperature-impaired tail of the growth curve by clocking the crop during its linear growth phase. The linear phase can begin when the crop is no longer affected by growth-limiting temperatures. In various embodiments, the clocking function can calculate a theoretic point when temperature-limited growth has passed and the linear growth period has begun, which can obviate the need to include heat units in phenology calculations. In many embodiments, the clocking function can enable assessment of the phenology on many individual fields across tens of thousands of square miles covered by EOS data and can do so without reference to temperature.
NDVI*, A Surrogate for Spatial-Yield Patterns
As an expression of canopy chlorophyll, NDVI* can be an indicator of potential crop yield. Chlorophyll is a metabolically expensive molecule that is conserved—no excess of chlorophyll is produced in plants, including crops. The function of chlorophyll is photosynthesis that provides the carbohydrate feedstock for all biochemical processes in the plant. The higher the rate of photosynthesis and attendant biochemical processes in the crop canopy, the higher the yield. Therefore, NDVI* magnitude can be a direct indicator of photosynthesis and crop yield. The NDVI* magnitude can be controlled by soil capability inclusive of hydrology and physical and chemical conditions that are all influenced by topography. Thus, with all other factors being equal in the cultivation of a crop (e.g., seed density and fertilization), the pattern of NDVI* expressed by a cropped field can be an indicator of the spatial pattern of potential yield created by soil capability.
Crop spatial-yield patterns largely can be determined by the health and vigor of the crop canopy expressed as NDVI* magnitude. For an individual field, the spatial-yield pattern can be demonstrated through snapshots of NDVI* when taken at a specific time in the growing season that is first determined through calibration. Once the timing is known, it can be targeted for EOS data collection using the clocking function. For example, in several embodiments, the spatial-yield pattern in corn can be assessed with single EOS snapshots taken during a certain time window predicted using an elapsed interval relative to that field's AED. For corn, this window for display of NDVI* as a surrogate for yield can occur in the latter period of crop growth but well in advance of senescence. The forecasted day when the spatial-yield pattern is best displayed by NDVI* can be designated DOY′.
Turning ahead in the drawings,
In a number of embodiments, both the measured yield and NDVI* in
In some embodiments, calibrating the clocking function to predict DOY′, as in
The measured spatial-yield variability of the example corn field, which is the uppermost and largest of the
Turning ahead in the drawings,
The reduction in the elapsed period from AED to DOY′, such as shown in
DOY′ NDVI* Maps for Variable Density Seed Prescription
The NDVI* map from an image at or close to DOY′ can be the input for prescribing and delivery of variable seed planting densities across a field. This NDVI* at DOY′ (hereafter, DOY′ can be inclusive of imagery obtained within the approximate two-week window for NDVI* spatial display of yield) can be used for variable density seeding prescription for the following year. In an alternate embodiment, the NDVI* at DOY′ from a number of previous years can be combined as a statistical sample to create a variable seeding prescription that potentially can remove or reduce the effect of patterns due to differential cultivation practices in any one year, whether intended or not (e.g., machine malfunction during planting, accidental double planting pass, malfunctioning irrigation systems, crop disease/pets, high rainfall, low rainfall, etc.).
Turning ahead in the drawings,
b) presents the NDVI* map in three classes for the purpose of illustration because fewer classes enhance contrast for comparison of patterns. Employing more bins can impart greater precision and the limits of this precision can be defined by the inherent statistical properties of the data. The greatest source for error in correctly calculated NDVI* can be from geoposition, hence, the potential error in geopositional accuracy can be a consideration in choosing the number of classification bins. In the example shown in
If an entire field was treated in the same manner through the growing season, for example monolithic fertilizer application, seeding and watering, the pattern for yield represented by an EOS snapshot of NDVI* at DOY′ can illustrate the potential yield imparted by soil capability combined with topographic influences. For dryland cropped fields, in addition to the spatially variable soil physical and chemical properties, the yield pattern can also reflect soil hydrologic factors related to topographically-induced runoff, such as drainage from sloped ground and collection in swales and contour-furrow catchments. Swales and catchments present complexity for targeted seeding because they can receive sufficient water to support a crop during drought yet can drown the crop during a wet year. The same location of the field creating opposite results thus can depend upon the weather during the year in question. Such potential for differences in yield can be natural to dryland fields and can be understood and handled with appropriate adjustment. An editing feature to enable changing the seeding prescription on portions of the field can be used to overcome this dichotomy.
In addition to topography and soil capability, the expression of yield from a cultivated field, can be due to past treatments, residual fertilizer content, organic matter and other attributes that may not be equally influential across the field. Most agricultural fields are managed monolithically—supplied with seed and fertilizer evenly across the landscape. In many embodiments, the systems and methods described herein recognize differences across fields, such as those managed monolithically, in order to optimize inputs in a manner that enhances yield potential on all areas while conserving resources such as seed and fertilizer. Hence, the patterns that arise through the equal opportunity imparted by monolithic management can demonstrate the capability imparted solely by the soil and topography. After variable density prescriptions are made and operated for a time, in several embodiments, any change in the pattern of yield as displayed by NDVI* at DOY′ can be considered the norm and seeding prescriptions can be made based upon this new norm. The repeated application of the systems and methods described herein can advantageously provide a method to fine tune the seed prescription over time.
In many embodiments, the spatial-yield pattern for NDVI* measured at DOY′ can be reassessed at intervals of one to several years. Combining multiple years of NDVI* at DOY′ can yield an average pattern of soil-capability indicating relative values of NDVI* that can be more correct than that measured in a single year. Optionally, as a cost savings through omitting further service, the user (e.g., the farmer) can choose to reapply the same seeding prescription based upon assessments using the NDVI* map-based seeding prescription from a prior year. This latter option can be preferable if the NDVI* differences in the field are extreme and caused by highly divergent soil properties, such as a field that is dominated by productive soil but also contains non-productive soil in which remnant sand dunes have poor water and nutrient holding capacity. In several embodiments, a cogent strategy for this example can be to greatly reduce seed density on the remnant dune according to the NDVI* values. The reduction in seeding can reduce interplant competition to achieve a better yield, even with reduced plant density. In many embodiments, such dichotomous choices need not involve multiple years of seeding prescription to understand the correct differential yield potential for the field.
In various embodiments, a consideration during calibration and application of the systems and methods described herein can be that differential cultivation practices on a cropped field can influence the spatial pattern of NDVI* and can prevent the true crop canopy expression of soil capability that is of direct interest. For first-time application of the systems and methods provided herein, a field should exhibit the spatial-yield pattern imparted by the soil and water available to the crop. To best display this pattern, the entire field can be treated in the same manner: coincidental and equal planting, fertilizing, irrigation (if irrigated), etc. The exemplary corn field was treated monolithically, which enabled the coherent data in
In a number of embodiments, if the entire field is cultivated and managed in the same manner, the spatial-yield pattern can be a competent indicator of the spatially-variable soil capability within the field. Like spatial differences in crop culture, the spatial-yield pattern and its surrogated NDVI* also can be altered by any impact that does not affect the entire field equally (e.g., hail, crop pests, or diseases). Understanding the past influences upon the crop canopy during the growing season or during prior years can provide a benefit when applying the systems and methods described herein. Hence, the most experienced and knowledgeable person, the farmer, can be a target of the systems and methods described herein. In this specification, “farmer” can refer to the person managing a field or causing it to be managed.
In many embodiments, the DOY′ NDVI* maps, such as shown in
In many embodiments, the electronic data for the NDVI* map can contain spatial information to guide seed application densities according to geographic position provided by GPS on board the farm equipment. Conventional spatial positioning can be used on farm equipment manufactured with integral GPS systems to enable precision agriculture operations such as variable density seeding. In several embodiments, conventional controller technology for metering seeds can be used for tractor-pulled equipment. The systems and methods described herein can provide a suite of mathematical data upon which to vary the seed density spatially, which can beneficially transform an average farmed field of crops into a cropped field that has been optimally seeded in order to provide the highest return for the lowest input cost. This seeding can occur through instructing the controller for spatially variable seeding according to the NDVI* map at positions determined from the GPS system onboard and integral to the seeding equipment.
In many embodiments, the systems and methods described herein can (1) provide variable densities of seed planted to match the variable conditions within each field, (2) evaluate many fields at a time for this variable density application using automated software, (3) deliver the analysis to the farmer through the Internet, (4) provide for simple manipulation of the output by the farmer within software, and/or (5) control farm hardware to apply the seeding prescription throughout each field. In some embodiments, each of the five aforementioned characteristics are included.
In a number of embodiments, the systems and methods described herein can be enabled by harnessing NDVI* growth curves to establish surrogate spatial-yield patterns. Three exemplary options for application of the systems and methods described herein are discussed below, each delivered through Internet connectivity in software that harnesses the knowledge and experience of the farmer and the companies that supply the seed. Other options can be employed in many different embodiments or examples not specifically depicted or described herein. For each of three options described, variable densities of seeding can be applied to the field according to the software operating through APIs to control the equipment of the farmer. Variable density seeding is relatively new, and ways to use variable density prescriptions, and what they should be, are still being determined, chiefly by the companies that grow and sell seed. Thus, the options are described herein as examples of the various methods for applying the DOY′ NDVI* maps that can be used for optimal prescription in various embodiments.
In Option 1 the farmer can allow the software to estimate the seeding density for the field, based solely upon the DOY′ NDVI* map and a peak seeding density for the crop type. For example, 42,000 seeds per acre for corn is an approximate maximum density that is provided by a leading seed company. Using this upper limit for Option 1, the software then can assign 42,000 seeds per acre to correspond with the theoretic high value of one for NDVI*. For this option all pixelwise values of NDVI* then can be scaled from this high down to a theoretic low value of zero seeds per acre at zero NDVI*, although, in many embodiments, no zero potential should exist in a cultivated field. For cultivated fields, in several embodiments, the seeding density will typically be bunched within the range for the DOY′ NDVI* map from approximately 0.4 to approximately 0.9, which correspond to lowest and highest values of NDVI* expected for a competently cropped field at DOY′. The linear scaling method with a high value of 42,000 seeds/acre at the NDVI* equal to one and a zero seed at zero NDVI* low value yields a density of from 16,800 (at NDVI* of 0.4) to 37,800 seeds per acre (NDVI* of 0.9). These values are commensurate with seeding densities published in the literature published by the aforementioned seed company.
In Option 2, the farmer can choose the maximal seeding density for the field based upon experience. The software then can pair the maximum measured DOY′ NDVI* for the field with the maximum set by the farmer and can scale a linear relationship between this maximum to the low point, zero seed at zero NDVI*, as in Option 1 for scaling the remainder of the field.
In Option 3, the farmer can choose the maximum and minimum seeding densities for the field. The software then can find the statistical maximum and minimum in the field and can calculate the various relative seeding densities in between the two bracketing values.
For each of the three options described, in several embodiments, the software can show the seeding densities according to the classes of DOY′ NDVI* on the field, from lowest to highest. The densities from the selected option can be compared to the other two options for the farmer to examine and then ratify, or make adjustments. In many embodiments, the software can provide a color-keyed map of the seeding prescription for each option. There are many potential adjustments for combining software algorithms and the DOY′ NDVI* map to determine seeding density. In some embodiments, for example, a simple add-on to the software can include economic calculators for the cost of seed and other inputs necessary for growing a competent crop.
In several embodiments, the digital data associated with choosing seeding options and the spatially-variable densities of seeds planted on each field can be stored data that advantageously can establish a history for that field. In many embodiments, these data can be called up through software and compared across years. In a number of embodiments, this digital history can be used to readily identify a field's spatial soil capability and topographic control of hydrology.
In many embodiments, the NDVI* map from one crop type grown in a previous year can be used for determination of the seeding density of another crop in the following year. In various embodiments, this NDVI* map reuse is available because the spatial pattern of NDVI* represents the soil capability that can affect the growth of any crop. In some embodiments, the seeding should follow the recommendations for the intended crop type according to the seed company or experience of the farmer as in Options 1, 2 or 3, for example.
In several embodiments, the stored history for a field of interest through software can support calculation of the potential return on investment and avoid seeding, fertilizing, and irrigation of zones that may repeatedly fail to provide a return or to reduce inputs such as seed to a point that a return on investment can occur. In many embodiments, assessment of potential return on investment can be made with only limited data on the cost of inputs to attain a crop (e.g., costs for seed, fertilizer, soil ameliorants, diesel, general wear and tear on the farm equipment performing the planting, and financing costs). In various embodiments, such data can be kept for each farmed region and can be updated automatically through an Internet connection. In a number of embodiments, decisions can be presented to the farmer for zones within the field that can best assure return on investment. Similarly, in several embodiments, the software can forecast yields and return on investment. In various embodiments, the systems and methods described herein can provide a decision support role in which the potential value of the yield can be assessed against input costs.
Flow Charts
Turning ahead in the drawings,
The conventions used in
λ refers to crop type,
j refers to the jth day, which for EOS data, is the day of the overpass,
i refers to the ith pixel,
m refers to the mth field, and
n refers to numbers of samples.
Referring to
In a number of embodiments, method 500 next can include a block S102 of collecting EOS data. In several embodiments, EOS data can be collected for all jth days for crop λ for Field m. Images can be obtained through the growing season, such as obtained about one week apart, for calibrating the clocking function for each crop type.
In several embodiments, method 500 next can include a block S104 of calculating reflectance and NDVI. For example, the images can be converted to reflectance and NDVI as described above.
In many embodiments, method 500 next can include a block S106 of extracting NDVI scene statistics and calculating NDVI* based on these statistics for each pixel across the EOS image.
In some embodiments, method 500 can include a decision block S108 that designates that EOS data can be continually gathered and processed throughout the growing season. Decision block S108 is designated as a decision block in recognition that image collection can involve decision for when and how often images will be needed.
In various embodiments, method 500 next, after block S106, can include a block S110 of extracting NDVI* pixel data for a specific crop type λ on Field m.
In many embodiments, method 500 next can include a block S112 of extracting median values of NDVI* for Field m. The median values can provide a statistical representation of the sample. Median values tend to be more robust indicators of field trends than averages. In other embodiments, averages can be extracted.
In a number of embodiments, method 500 next can include a block S114 of collecting field medians together to represent the growth of the crop through the season and determining the AED for each Field m. For example, the AED can be determined using the graphical method shown in
Returning to block S110, in some embodiments, the flow can proceed to a block S116 of displaying visual displays of the NDVI* across Field m.
In several embodiments, method 500 can include a block S118 of obtaining and displaying the yield measured at the time of harvest across Field m.
In some embodiments, method 500 next can include a decision block S120 of visually comparing the displays from block S116 and block S118 to select the image date that best matches the measure spatial expression of yield, at or near DOY′.
In various embodiments, the flow can proceed from block S114 and/or decision block S120 to a block S122 of determining an estimate of elapsed days. In many embodiments, the AED value determined for Field m in block S114, as expressed as DOY, can be subtracted from the selected approximate DOY′ date of the imagery to determine the estimate of elapsed days.
In many embodiments, method 500 next can include a block S124 of repeating blocks S116 through S122 to create a statistical sample to calibrate elapsed days to DOY′ from AED.
In some embodiments, method 500 next can include a block S126 of estimating the elapsed days to DOY′ from AED according to the AED of Field m. For example, the pooled values collected in block S124 can be fitted with a linear relationship using regression, such as using the graphical method illustrated in
In several embodiments, the calibration actions in blocks S110 through S126 can be repeated for each crop type λ. In a number of embodiments, the mathematical relationship from block S126 for each crop type λ can be output to a block S128, and the output can be used in block S208 of
Turning ahead in the drawings,
In a number of embodiments, method 600 next can include a block S202 of collecting EOS data during the linear phase of the NDVI* growth curve for each Field m. For example, the linear phase can be similar to the linear phase shown in
In several embodiments, method 600 next can include a block S204 of converting the linear growth phase data to NDVI*. In a number of embodiments, block S204 can follow the individual actions included in blocks S102 through S106 of
In some embodiments, method 600 next can include a block S206 of estimating AED for each Field m of crop type λ. In many embodiments, the NDVI* values within the linear growth phase for crop λ can be processed using the linear regression calibration procedure of the clocking function, as shown in
In a number of embodiments, method 600 next can include a block S208 of estimating when the spatial-yield pattern will naturally be displayed by Field m. In many embodiments, block S208 can receive the output of the relationship for the number of elapsed days for displaying the spatial-yield pattern (DOY′) that was output from block S128 in
In some embodiments, method 600 next can include a block S210 of acquiring EOS data for Field m to represent the spatial-yield pattern approximately on the DOY′.
In various embodiments, method 600 next can include a block S212 of processing the spatial-yield pattern image for DOY′ to determine NDVI*.
In several embodiments, method 600 next can include a block S214 of extracting pixel values for NDVI* for Field m.
In some embodiments, the flow can continue to a block S216 to begin preparation for planting Field m. In many embodiments, method 600 can include block S216 of optimizing the analysis for n classes of Field m. In several embodiments, this analysis can choose the number of classes in consideration of the variability of the NDVI* map of Field m and the breadth of the NDVI* values. This optimization can be done using software. In some embodiments, the number of classes can be set by the user (e.g., the farmer) as long as the precision of the data will support the number of classes chosen.
In many embodiments, method 600 next can include a decision block S218 of the user (e.g., the farmer) selecting the settings desired, such as which option of the three options and the intended piece of farm equipment for planting the variable seed density prescription. The appropriate farm equipment, also known as a planter, should have the capability to vary the seed density according to software input.
In various embodiments, method 600 next can include a block S220 of scaling the seeding density for the various zones in the field according to the choice made in decision block S218 to provide a variable seed density prescription.
In several embodiments, method 600 next can include a block S222 of transferring the variable seed density prescription through the API to the planter equipment. Many planters are now manufactured with variable density capability and integral GPS units, and can be used to apply the prescription for spatially variable planting of seed density. Modern farm hardware generally include APIs to allow software to designate the variable seed density prescription. The APIs generally contain a set of routines, protocols and tools for building such software applications.
In a number of embodiments, method 600 next can include a block S224 of planting the seed using the equipment using the variable seed density prescription. At a block S226, the flow can end.
Not shown within the flowcharts is the development of NDVI* maps in subsequent years, and back-comparison with results from prior years. Such back-comparisons can be instrumental in establishing a permanent planting prescription to be used on the field. Back comparison can provide for fine tuning the seeding prescription. In many embodiments, such reevaluation and course corrections can be performed using the systems and methods described and conventional methods of managing agricultural fields. Software applications for this reevaluation can be built into multi-year functionality within the operational software. In other embodiments, the same or similar actions as those shown in the
Turning ahead in the drawings,
Continuing with
As used herein, “processor” and/or “processing module” means any type of computational circuit, such as but not limited to a microprocessor, a microcontroller, a controller, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a graphics processor, a digital signal processor, or any other type of processor or processing circuit capable of performing the desired functions. In some examples, the one or more processors of the various embodiments disclosed herein can comprise CPU 810.
In the depicted embodiment of
In some embodiments, network adapter 820 can comprise and/or be implemented as a WNIC (wireless network interface controller) card (not shown) plugged or coupled to an expansion port (not shown) in computer system 700 (
Although many other components of computer system 700 (
When computer system 700 in
Although computer system 700 is illustrated as a desktop computer in
Turning ahead in the drawings,
In some embodiments, device 900 can include an input module 901. In certain embodiments, input module 901 can receive input, and can at least partially perform block S102 (
In various embodiments, device 900 can include an output module 902. In certain embodiments, output module 902 can generate and/or display out, and can at least partially perform block S116 (
In a number of embodiments, device 900 can include a calculation module 903. In certain embodiments, calculation module 903 can at least partially perform block S104 (
In several embodiments, device 900 can include a mapping module 904. In certain embodiments, mapping module 904 can at least partially perform block S116 (
In some embodiments, device 900 can include a seed prescription module 905. In certain embodiments, seed prescription module 905 can at least partially perform block S220 (
Although the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes may be made without departing from the spirit or scope of the disclosure. Accordingly, the disclosure of embodiments is intended to be illustrative of the scope of the disclosure and is not intended to be limiting. It is intended that the scope of the disclosure shall be limited only to the extent required by the appended claims. For example, a wide variety of crops and seeding densities other than those mentioned above may be employed depending upon the soil and crop in the field. Various delivery methods and mechanical systems may be employed for delivery of the prescribed amendments as determined by the variety of data from various sources as described above. As another example, to one of ordinary skill in the art, it will be readily apparent that any element of
Replacement of one or more claimed elements constitutes reconstruction and not repair. Additionally, benefits, other advantages, and solutions to problems have been described with regard to specific embodiments. The benefits, advantages, solutions to problems, and any element or elements that may cause any benefit, advantage, or solution to occur or become more pronounced, however, are not to be construed as critical, required, or essential features or elements of any or all of the claims, unless such benefits, advantages, solutions, or elements are stated in such claim.
Moreover, embodiments and limitations disclosed herein are not dedicated to the public under the doctrine of dedication if the embodiments and/or limitations: (1) are not expressly claimed in the claims; and (2) are or are potentially equivalents of express elements and/or limitations in the claims under the doctrine of equivalents.
This application is a continuation-in-part of U.S. patent application Ser. No. 13/455,987, filed Apr. 25, 2012, which claims the benefit of U.S. Provisional Application No. 61/490,499, filed May 26, 2011, and U.S. Provisional Application No. 61/486,193, filed May 13, 2011. This application also is a continuation-in-part of U.S. patent application Ser. No. 13/455,971, filed Apr. 25, 2012, which claims the benefit of U.S. Provisional Application No. 61/490,499, filed May 26, 2011, and U.S. Provisional Application No. 61/486,193, filed May 13, 2011. This application also claims the benefit of U.S. Provisional Application No. 61/973,757, filed Apr. 1, 2014. U.S. patent application Ser. Nos. 13/455,987 and 13/455,971, and U.S. Provisional Application Nos. 61/973,757, 61/490,499, and 61/486,193 are incorporated herein by reference in their entirety.
| Number | Date | Country | |
|---|---|---|---|
| 61490499 | May 2011 | US | |
| 61486193 | May 2011 | US | |
| 61490499 | May 2011 | US | |
| 61486193 | May 2011 | US | |
| 61973757 | Apr 2014 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 13455987 | Apr 2012 | US |
| Child | 14676660 | US | |
| Parent | 13455971 | Apr 2012 | US |
| Child | 13455987 | US |
