Extraction of agricultural plastic film mulching in karst fragmented arable lands based on unmanned aerial vehicle visible light remote sensing

Considering the fragmentation of karst mountainous areas, a Dajiang Innovation Mavic 2 Pro quadcopter drone was used to collect visible light images of a modern intensive agricultural planting area in response to the high labor intensity and low efficiency of manual assessment of agricultural plastic film mulching (PFM). First, the characteristics of RGB values of 10 main types of features in the sample area, such as PFM, crops, buildings, and roads, were analyzed based on the color index, geometric size, and texture characteristics. Second, based on the construction principle and form of the excess green index, we constructed the excess blue index (ExB), which comprehensively utilized the three visible light bands of red, green, and blue. By calculating the ExB of the image, the characteristics of the PFM were enhanced. Third, Gaussian High-Pass Filter (GHPF) was used for ExB images to retain high-frequency information of PFM targets and reduce the influence of noisy ground types such as plant ashes, blue-roofed buildings, asphalt roads, and water on identifying PFM. Then, combined with field measurements of PFM data, the GHPF images were segmented using Otsu and color slices. The polygon area method was used to eliminate polygons that were too large or too small. Combined with random samples from field surveys, the threshold segmented PFM polygons were verified. The accuracies of the PFM area by methods of Otsu and human-computer interaction threshold segmentation (HC-ITS) are 85.44% and 97.36%, respectively, and the average accuracies of the verification areas B-I and B-II are 90.03% and 82.32%, respectively. It is proved that the PFM ExB proposed in this study has good applicability. We find that the HC-ITS method is suitable for scenes with complex ground object types, and the Otsu method is suitable for scenes with fewer ground object types. (C) 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)