Recognition of Plastic Film in Terrain-Fragmented Areas Based on Drone Visible Light Images

Xiaoyi Du, Denghong Huang (), Li Dai and Xiandan Du
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Xiaoyi Du: School of Geography & Environmental Science, School of Karst Science, Guizhou Normal University, Guiyang 550001, China
Denghong Huang: School of Geography & Environmental Science, School of Karst Science, Guizhou Normal University, Guiyang 550001, China
Li Dai: School of Geography & Environmental Science, School of Karst Science, Guizhou Normal University, Guiyang 550001, China
Xiandan Du: School of Geography & Environmental Science, School of Karst Science, Guizhou Normal University, Guiyang 550001, China

Agriculture, 2024, vol. 14, issue 5, 1-18

Abstract: In order to meet the growing demand for food and achieve food security development goals, contemporary agriculture increasingly depends on plastic coverings such as agricultural plastic films. The remote sensing-based identification of these plastic films has gradually become a necessary tool for agricultural production management and soil pollution prevention. Addressing the challenges posed by the complex terrain and fragmented land parcels in karst mountainous regions, as well as the frequent presence of cloudy and foggy weather conditions, the extraction efficacy of mulching films is compromised. This study utilized a DJI Mavic 2 Pro UAV to capture visible light images in an area with complex terrain features such as peaks and valleys. A plastic film sample dataset was constructed, and the U-Net deep learning model parameters integrated into ArcGIS Pro were continuously modified and optimized to achieve precise plastic film identification. The results are as follows: (1) Sample quantity significantly affects recognition performance. When the sample size is 800, the accuracy of plastic film extraction notably improves, with area accuracy reaching 91%, a patch quantity accuracy of 96.38%, and an IOU and F1-score of 85.89% and 94.20%, respectively, compared to the precision achieved with a sample size of 300; (2) Different learning rates, batch sizes, and iteration numbers have a certain impact on the training effectiveness of the U-Net model. The most suitable model parameters improved the training effectiveness, with the highest training accuracy achieved at a learning rate of 0.001, a batch size of 10, and 25 iterations; (3) Comparative experiments with the Support Vector Machine (SVM) model validate the suitability of U-Net model parameters and sample datasets for precise identification in rugged terrains with fragmented spatial distribution, particularly in karst mountainous regions. This underscores the applicability of the U-Net model in recognizing plastic film coverings in karst mountainous regions, offering valuable insights for agricultural environmental health assessment and green planting management in farmlands.

Keywords: karst mountainous terrain; UAV; U-Net modeling; ground cover recognition (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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