RegenPGC View: Semantic Segmentation of Perennial Groundcover Cropping Systems to Restore American Croplands

Modern, conventional row crop agricultural production relies on clean tillage of croplands and bare soil during the dormant season. While this paradigm of crop production has undoubtedly led to great increases in grain yields and efficiency, it has also resulted in significant soil erosion, groundwater contamination, degradation of local ecology, and hypoxic deadzones in US watersheds. Cover cropping with perennial plant species has been proposed as a way to mitigate these negative effects of crop production while having a minimum impact on crop yields. Measuring establishment of these perennial groundcovers (PGC) in research trials is subjective, tedious, and time-consuming when calculated with traditional methods whereas image based analyses are objective, efficient, and reproducible. For this project we have developed a deep learning approach using state of the art CNN architectures to estimate PGC establishment in research plots using a variety of open-source and internal image datasets. Our novel approach uses region of interest (ROI) markers in the field, to bound the predictions which improves upon other methods. We deployed the models on AWS Sagemaker serverless endpoints, and built a lightweight Django web application to host the images and inference services. Researchers will be able to acquire plot images with smartphone cameras and get fast, reliable data from their research trials using this “Local Sensing” data collection approach. We envision that this framework can be used by other researchers and growers as PGC adoption spreads throughout the Midwestern crop production areas.