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

Abstract

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.

Plant phenotyping relevance

研究圃場の多年生グラウンドカバー定着を画像と深層学習で推定する手法を開発し、ROI改善、クラウド推論、Webアプリまで実装しており、植物状態の取得・抽出が中心である。

Code and data availability

The paper describes the RegenPGC Dataset (4,192 field images), Kura clover images, trained EfficientDet/DeepLabV3+ models, and a Django web app, but no block contains an explicit public deposit or availability statement with an authors' URL for any of these paper-specific assets. The only public datasets mentioned (CAW

No evidence-backed public reproduction asset is currently recorded.