Welcome
The era of data sharing is here, but scientists still struggle to share lessons learned from agricultural data collection and data processing. It's challenging, insecure, and time-consuming. This project aims to mitigate these challenges by bringing together experts in plant science, secure networked systems, software engineering, and geospatial science.The team will develop a cyber-physical system to 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 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.
