Research on a UAV-Based Method for Predicting Shallow Residual Film Pollution in Cotton Fields Using RDT-Net

Lupeng Miao, Ruoyu Zhang, Huting Wang (), Yue Chen, Songxin Ye, Yuting Jia and Zhiqiang Zhai ()
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Lupeng Miao: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
Ruoyu Zhang: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
Huting Wang: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
Yue Chen: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
Songxin Ye: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
Yuting Jia: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
Zhiqiang Zhai: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China

Agriculture, 2025, vol. 15, issue 22, 1-21

Abstract: Traditional cotton field plastic film residue monitoring relies on manual sampling, with low efficiency and limited accuracy; therefore, large-scale nondestructive monitoring is difficult to achieve. A UAV-based prediction method for shallow plastic film residue pollution in cotton fields that uses RDT-Net and machine learning is proposed in this study. This study focuses on the weight of residual plastic film in shallow layers of cotton fields and UAV-captured surface film images, establishing a technical pathway for drone image segmentation and weight prediction. First, the images of residual plastic film in cotton fields captured by the UAV are processed via the RDT-Net semantic segmentation model. A comparative analysis of multiple classic semantic segmentation models reveals that RDT-Net achieves optimal performance. The local feature extraction process in ResNet50 is combined with the global context modeling advantages of the Transformer and the Dice-CE Loss function for precise residue segmentation. The mPa, F1 score, and mIoU of RDT-Net reached 95.88%, 88.33%, and 86.48%, respectively. Second, a correlation analysis was conducted between the coverage rate of superficial residual membranes and the weight of superficial residual membranes across 300 sample sets. The results revealed a significant positive correlation, with R 2 = 0.79635 and PCC = 0.89239. Last, multiple machine learning prediction models were constructed on the basis of plastic film coverage. The ridge regression model achieved optimal performance, with a prediction R 2 of 0.853 and an RMSE of 0.1009, increasing accuracy in both the segmentation stage and prediction stage. Compared with traditional manual sampling, this method substantially reduces the monitoring time per cotton field, significantly decreases monitoring costs, and prevents soil structure disruption. These findings address shortcomings in existing monitoring methods for assessing surface plastic film content, providing an effective technical solution for large-scale, high-precision, nondestructive monitoring of plastic film pollution on farmland surfaces and in the plow layer. It also offers data support for the precise management of plastic film pollution in cotton fields.

Keywords: residual film pollution; machine learning; UAV; RDT-Net; information forecasting (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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