Segmentation and Proportion Extraction of Crop, Crop Residues, and Soil Using Digital Images and Deep Learning

Guangfu Gao, Shanxin Zhang, Jianing Shen, Kailong Hu, Jia Tian, Yihan Yao, Qingjiu Tian, Yuanyuan Fu, Haikuan Feng, Yang Liu and Jibo Yue ()
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Guangfu Gao: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China
Shanxin Zhang: School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
Jianing Shen: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China
Kailong Hu: Key Laboratory of Emergency Satellite Engineering and Application, Ministry of Emergency Management, Beijing 100124, China
Jia Tian: School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
Yihan Yao: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China
Qingjiu Tian: International Institute for Earth System Science, Nanjing University, Nanjing 210023, China
Yuanyuan Fu: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China
Haikuan Feng: Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
Yang Liu: Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
Jibo Yue: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China

Agriculture, 2024, vol. 14, issue 12, 1-18

Abstract: Conservation tillage involves covering the soil surface with crop residues after harvest, typically through reduced or no-tillage practices. This approach increases the soil organic matter, improves the soil structure, prevents erosion, reduces water loss, promotes microbial activity, and enhances root development. Therefore, accurate information on crop residue coverage is critical for monitoring the implementation of conservation tillage practices. This study collected “crop–crop residues–soil” images from wheat-soybean rotation fields using mobile phones to create calibration, validation, and independent validation datasets. We developed a deep learning model named crop–crop residue–soil segmentation network (CCRSNet) to enhance the performance of cropland “crop–crop residues–soil” image segmentation and proportion extraction. The model enhances the segmentation accuracy and proportion extraction by extracting and integrating shallow and deep image features and attention modules to capture multi-scale contextual information. Our findings indicated that (1) lightweight models outperformed deeper networks for “crop–crop residues–soil” image segmentation. When CCRSNet employed a deep network backbone (ResNet50), its feature extraction capability was inferior to that of lighter models (VGG16). (2) CCRSNet models that integrated shallow and deep features with attention modules achieved a high segmentation and proportion extraction performance. Using VGG16 as the backbone, CCRSNet achieved an mIoU of 92.73% and a PA of 96.23% in the independent validation dataset, surpassing traditional SVM and RF models. The RMSE for the proportion extraction accuracy ranged from 1.05% to 3.56%. These results demonstrate the potential of CCRSNet for the accurate, rapid, and low-cost detection of crop residue coverage. However, the generalizability and robustness of deep learning models depend on the diversity of calibration datasets. Further experiments across different regions and crops are required to validate this method’s accuracy and applicability for “crop–crop residues–soil” image segmentation and proportion extraction.

Keywords: image segmentation; proportion extraction; deep learning; crop residue coverage (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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