Extraction of Cotton Cultivation Areas Based on Deep Learning and Sentinel-2 Image Data

Liyuan Li, Hongfei Tao (), Yan Xu, Lixiran Yu, Qiao Li, Hong Xie and Youwei Jiang
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Liyuan Li: College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Hongfei Tao: College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Yan Xu: Xinjiang Uygur Autonomous Region Ecological Water Resources Research Center, Academician and Expert Workstation of the Department of Water Resources of the Xinjiang Uygur Autonomous Region, Urumqi 830052, China
Lixiran Yu: College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Qiao Li: College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Hong Xie: Changji Water Conservancy Management Station, Santunhe River Basin Management Office, Changji 831100, China
Youwei Jiang: College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, China

Agriculture, 2025, vol. 15, issue 16, 1-18

Abstract: Cotton is a crucial economic crop, and timely and accurate acquisition of its spatial distribution information is of great significance for yield prediction, as well as for the formulation and adjustment of agricultural policies. To accurately and efficiently extract cotton cultivation areas at a large scale, in this study, we focused on the Santun River Irrigation District in Xinjiang as the research area. Utilizing Sentinel-2 satellite imagery from 2019 to 2024, four cotton extraction models—U-Net, SegNet, DeepLabV3+, and CBAM-UNet—were constructed. The models were evaluated using metrics, including the mean intersection over union (mIoU), precision, recall, F1-score, and over accuracy (OA), to assess the models’ performances in cotton extraction. The results demonstrate that the CBAM-UNet model achieved the highest accuracy, with an mIoU, precision, recall, F1-score, and OA of 84.02%, 88.99%, 94.75%, 91.78%, and 95.56%, respectively. The absolute error of the extracted cotton areas from 2019 to 2024 ranged between 923.69 and 1445.46 hm 2 , with absolute percentage errors of less than 10%. The coefficient of determination (R 2 ) between the extracted results and statistical data was 0.9817, indicating the best fit. The findings of this study provide technical support for rapid cotton identification and extraction in large- and medium-sized irrigation districts.

Keywords: cotton; deep learning; attention mechanism; CBAM-UNet (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: 2025
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