In the era of data sharing, it is still challenging, insecure, and time-consuming for scientists to share lessons learned from agricultural data collection and data processing. The focus of this project is to mitigate such challenges by intersecting expertise in plant science, secure networked systems, software engineering, and geospatial science. The proposed cyber-physical system will be evaluated in the laboratory and deployed on real crop farms in Missouri, Illinois, and Tennessee. All results will be shared with international organizations whose goal is to increase food security and improve human health and nutrition.
The proposed system will securely orchestrate data gathered using sensors, such as hyperspectral and thermal cameras to collect imagery on soybean, sorghum, and other crops. Preprocessed plant datasets will be then offered to scientists and farmers in different formats via a web-based system, ready to be processed by deep learning algorithms or consumed by thin clients. Data collected from different crop farms will be used to train distributed deep learning systems, using novel architectures that optimize privacy and training time. Such machine learning systems will be used to predict plant stress and detect pathogens. Finally, the cyber-physical system will integrate novel data processing software with existing NSF-funded hardware platforms, introducing novel algorithmic contributions in edge computing and giving feedback to farmers, closing the loop.