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This research aims to enhance the accuracy of crop residue cover estimation and segmentation using a new deep learning model, RCTUnet.

May 21, 2026

RCTUnet: a deep learning model for crop-residue-soil image segmentation and crop residue cover extraction.

Key Points

This research aims to enhance the accuracy of crop residue cover estimation and segmentation using a new deep learning model, RCTUnet.
Utilized RCTUnet, incorporating ResNet50, convolutional block attention, and global context fusion modules.
Evaluated on a dataset of 1220 images across four crop rotations.
Compared performance against traditional segmentation models like Unet, SegNet, and DeepLabV3.
RCTUnet improved overall segmentation accuracy by 3.24% to 8.28% over traditional models.
Achieved residue recall increases between 7.37% and 27.05% compared to classic models.
Demonstrated a 45.5% reduction in root mean square error (RMSE), achieving an RMSE of 4.875 compared to Unet's 8.941.

Abstract

Accurate quantification of crop residue cover (CRC) is crucial for monitoring and evaluating conservation tillage practices, yet it poses a significant image segmentation challenge. The subtle visual distinctions between fragmented residue and soil, compounded by variable illumination and shadows in field imagery, often lead to poor segmentation performance. To overcome these limitations, we introduce RCTUnet, a novel deep learning architecture designed for robust crop-residue-soil segmentation and precise CRC estimation. RCTUnet's architecture synergistically integrates three key components: (1) a ResNet50 backbone for deep, multi-scale feature extraction; (2) a convolutional block attention module (CBAM) to adaptively focus on salient residue features across both channel and spatial dimensions; and (3) a transformer-based global context fusion module (GCFM) to model long-range spatial dependencies, which is critical for interpreting heterogeneous residue patterns. We evaluated RCTUnet on a dataset of 1220 field-acquired images spanning four typical crop rotations. Experimental results show that, compared to traditional models: (1) RCTUnet achieves significantly higher crop-residue-soil segmentation accuracy than classic models including Unet, Unet++, DeepLabV3, segmentation network (SegNet), and fully convolutional network (FCN), with improvements of 3.24%, 3.42%, 4.88%, 8.28%, and 6.05% in overall accuracy, respectively; (2) RCTUnet yields superior residue-soil segmentation performance, with increases in residue recall of 7.67%, 7.37%, 14.09%, 27.05%, and 16.91%, respectively; (3) RCTUnet shows enhanced CRC estimation accuracy, achieving a root mean square error (RMSE) of 4.875, representing a 45.5% improvement over Unet (RMSE=8.941). These results demonstrate the efficacy of our hybrid approach, which combines deep hierarchical features, dual-domain attention, and global context modeling. RCTUnet provides a robust and reliable tool for automated CRC assessment, advancing the capabilities of in-field agricultural monitoring.

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