Crop residue management is an important factor in sustainable agriculture as it impacts soil erosion, water retention, soil organic matter, and crop yield. Accurately measuring the crop residue cover helps in the strategic planning, control, and monitoring of crop residue. While advancements in machine learning have allowed for significant progress in crop residue classification work, a major challenge still exists in the creation of an accurately annotated dataset for crop residue and the application of segmentation-based models to accurately segment crop residues. This study aims to develop an efficient image annotation framework and evaluate deep learning models for crop residue cover estimation. For this, the Residue Segmentation Tool, a standalone graphical user interface, was designed to facilitate accurate and efficient image annotation that enables flexible and high-throughput annotation of residue images. The tool is publicly available and supports multiple segmentation modes, which include classical and modern computer vision algorithms such as Otsu, Canny, and manual thresholding, as well as the Segment Anything Model and user-guided mask refinement through manual editing options. This tool was also utilized to create annotated datasets for machine learning training and testing of crop residue cover estimation. Three different sizes of datasets (100, 250, and 500 images) were utilized for machine learning training and testing to evaluate the performance of the models trained using U-Net and DeepLabV3. U-Net consistently outperformed DeepLabV3 across most metrics, particularly on smaller datasets, showing better Dice, IoU, and Recall scores. The best-performing model had Dice, IoU, and Accuracy scores of 0.748, 0.627, and 0.864, respectively. The findings demonstrate that the Residue Segmentation Tool enables scalable and reproducible dataset creation and supports effective segmentation for crop residue cover estimation.
Regmi et al. (Fri,) studied this question.