Sh-DeepLabv3+: An Improved Semantic Segmentation Lightweight Network for Corn Straw Cover Form Plot Classification

Yueyong Wang, Xuebing Gao, Yu Sun, Yuanyuan Liu (), Libin Wang and Mengqi Liu
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Yueyong Wang: College of Information and Technology & Smart Agriculture Research Institute, Jilin Agricultural University, Changchun 130118, China
Xuebing Gao: College of Information and Technology & Smart Agriculture Research Institute, Jilin Agricultural University, Changchun 130118, China
Yu Sun: College of Information and Technology & Smart Agriculture Research Institute, Jilin Agricultural University, Changchun 130118, China
Yuanyuan Liu: College of Information and Technology & Smart Agriculture Research Institute, Jilin Agricultural University, Changchun 130118, China
Libin Wang: Changchun Agricultural Machinery Research Institute, Changchun 130052, China
Mengqi Liu: College of Information and Technology & Smart Agriculture Research Institute, Jilin Agricultural University, Changchun 130118, China

Agriculture, 2024, vol. 14, issue 4, 1-19

Abstract: Straw return is one of the main methods for protecting black soil. Efficient and accurate straw return detection is important for the sustainability of conservation tillage. In this study, a rapid straw return detection method is proposed for large areas. An optimized Sh-DeepLabv3+ model based on the aforementioned detection method and the characteristics of straw return in Jilin Province was then used to classify plots into different straw return cover types. The model used Mobilenetv2 as the backbone network to reduce the number of model parameters, and the channel-wise feature pyramid module based on channel attention (CA-CFP) and a low-level feature fusion module (LLFF) were used to enhance the segmentation of the plot details. In addition, a composite loss function was used to solve the problem of class imbalance in the dataset. The results show that the extraction accuracy is optimal when a 2048 × 2048-pixel scale image is used as the model input. The total parameters of the improved model are 3.79 M, and the mean intersection over union (MIoU) is 96.22%, which is better than other comparative models. After conducting a calculation of the form–grade mapping relationship, the error value of the area prediction was found to be less than 8%. The results show that the proposed rapid straw return detection method based on Sh-DeepLabv3+ can provide greater support for straw return detection.

Keywords: conservation tillage; straw return detection; UVA remote sensing; deep learning; shallow feature extraction (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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