A multi-scale data assimilation framework for layered sensing and hierarchical control of disease spread in field crops

This project is focused on developing data analytics and decision-making techniques for early detection and mitigation of soybean diseases via fusing data from ground robots, UAVs and satellites. We aim to collect RGB and hyperspectral image data for soybean diseases from research farms at Iowa State and via collaboration with the Iowa Soybean Association and the NASA Jet Propulsion Lab (for satellite data). Upon data collection, we will develop a machine learning framework to efficiently fuse multi-resolution and multi-spectral information from ground robots, UAVs and satellites for early detection of a critical soybean disease and estimate the disease severity progression as well as spatial progression of the disease. Such a framework for disease identification, severity quantification and prediction will enable us to develop mitigation strategies that farmers can use to reduce the impact of the disease.

We present some initial works of the proposed effort that includes development of a deep convolutional neural network (CNN) based framework for identifying, classifying and quantifying a large variety (8 types) of biotic and abiotic stresses in soybean. We have also developed a 3D CNN framework for early detection of a soybean disease using hyperspectral data.

This project is supported by the USDA-NIFA under Grant No. 2017-67007-26151.