Lightweight Deep Learning for Real-Time Cotton Monitoring: UAV-Based Defoliation and Boll-Opening Rate Assessment

Minghui Xia, Xuegeng Chen, Xinliang Tian, Haojun Wen, Yan Zhao (), Hongxia Liu, Wei Liu and Yuchen Zheng
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Minghui Xia: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003,China
Xuegeng Chen: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003,China
Xinliang Tian: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003,China
Haojun Wen: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003,China
Yan Zhao: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003,China
Hongxia Liu: Administrative Committee of Shihezi National Agricultural Science and Technology Park, Shihezi 832000, China
Wei Liu: Administrative Committee of Shihezi National Agricultural Science and Technology Park, Shihezi 832000, China
Yuchen Zheng: College of Information Science and Technology, Shihezi University, Shihezi 832003, China

Agriculture, 2025, vol. 15, issue 19, 1-22

Abstract: Unmanned aerial vehicle (UAV) imagery provides an efficient approach for monitoring cotton defoliation and boll-opening rates. Deep learning, particularly convolutional neural networks (CNNs), has been widely applied in image processing and agricultural monitoring, achieving strong performance in tasks such as disease detection, weed recognition, and yield prediction. However, existing models often suffer from heavy computational costs and slow inference speed, limiting their real-time deployment in agricultural fields. To address this challenge, we propose a lightweight cotton maturity recognition model, RTCMNet (Real-time Cotton Monitoring Network). By incorporating a multi-scale convolutional attention (MSCA) module and an efficient feature fusion strategy, RTCMNet achieves high accuracy with substantially reduced computational complexity. A UAV dataset was constructed using images collected in Xinjiang, and the proposed model was benchmarked against several state-of-the-art networks. Experimental results demonstrate that RTCMNet achieves 0.96 and 0.92 accuracy on defoliation rate and boll-opening rate classification tasks, respectively. Meanwhile, it contains only 0.35 M parameters—94% fewer than DenseNet121—and only requires an inference time of 33 ms, representing a 97% reduction compared to DenseNet121. Field tests further confirm its real-time performance and robustness on UAV platforms. Overall, RTCMNet provides an efficient and low-cost solution for UAV-based cotton maturity monitoring, supporting the advancement of precision agriculture.

Keywords: UAV remote sensing; cotton phenotyping; defoliation rate; boll-opening rate; lightweight deep learning; real-time monitoring (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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