Evaluation of residual plastic film pollution in pre-sowing cotton field using UAV imaging and semantic segmentation

Zhiqiang Zhai; Xuegeng Chen; Ruoyu Zhang; Fasong Qiu; Qingjian Meng; Jiankang Yang; Haiyuan Wang

doi:10.3389/fpls.2022.991191

Evaluation of residual plastic film pollution in pre-sowing cotton field using UAV imaging and semantic segmentation

Front Plant Sci. 2022 Sep 9:13:991191. doi: 10.3389/fpls.2022.991191. eCollection 2022.

Authors

Zhiqiang Zhai^{1

2}, Xuegeng Chen^{1

2}, Ruoyu Zhang^{1

2}, Fasong Qiu^{1

2}, Qingjian Meng^{1

2}, Jiankang Yang^{1

2}, Haiyuan Wang^{1

2}

Affiliations

¹ College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China.
² Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi, China.

Abstract

To accurately evaluate residual plastic film pollution in pre-sowing cotton fields, a method based on modified U-Net model was proposed in this research. Images of pre-sowing cotton fields were collected using UAV imaging from different heights under different weather conditions. Residual films were manually labelled, and the degree of residual film pollution was defined based on the residual film coverage rate. The modified U-Net model for evaluating residual film pollution was built by simplifying the U-Net model framework and introducing the inception module, and the evaluation results were compared to those of the U-Net, SegNet, and FCN models. The segmentation results showed that the modified U-Net model had the best performance, with a mean intersection over union (MIOU) of 87.53%. The segmentation results on images of cloudy days were better than those on images of sunny days, with accuracy gradually decreasing with increasing image-acquiring height. The evaluation results of residual film pollution showed that the modified U-Net model outperformed the other models. The coefficient of determination(R²), root mean square error (RMSE), mean relative error (MRE) and average evaluation time per image of the modified U-Net model on the CPU were 0.9849, 0.0563, 5.33% and 4.85 s, respectively. The results indicate that UAV imaging combined with the modified U-Net model can accurately evaluate residual film pollution. This study provides technical support for the rapid and accurate evaluation of residual plastic film pollution in pre-sowing cotton fields.

Keywords: UAV imaging; cotton field; deep learning; pollution; residual film.