NSF Award
2403496
This I-Corps project utilizes experiential learning coupled with first-hand investigation of the industry ecosystem to assess the translation potential of the technology. The project focuses on the development of a smartphone application for agricultural crop disease detection. Early detection of pests and diseases helps contain the spread of the disease. While crop diseases can have a significant impact on crop yields, leading to economic losses for farmers and potentially affecting food security, there are no reliable computer vision-based solutions to help researchers and farmers achieve reliable disease scoring in the field. This technology is a mobile application for plant disease detection that automates crop pest and disease recognition in the field and goes beyond what may be achieved with manual field scouting. The use of this technology may improve crop yield and empower more efficient management and decision making in agricultural production.<br/><br/>The technology is based on the prior development of agricultural crop disease detection using a smartphone application. Plants infected by pests or diseases typically have marks or lesions on the leaves, stems, flowers, or fruits. The technology is designed to be deployed in the cloud for real-time disease detection and uses advanced, lightweight deep learning models and large image sets collected directly from agricultural fields covering all scenarios to achieve accurate disease detection results. In addition, the technology is designed to provide detection results in multiple modalities based on users’ needs including disease score and images with segmentation for visualization and measurements of the size or count of diseased areas. The application may be used to collect and record the appropriate crop images to the cloud. This application may significantly speed up pest and disease detection efficiency while reducing labor use and eliminating expertise from human professionals as compared to the traditional scores-in-notes manual method.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
