关键词: 地块提取, 无人机, 过绿指数, 超像素分割, VGG16, 图像识别

Abstract: The precise extraction of oilseed rape field can realize the separation of road, field ridge and plot, provide accurate operation area and nonoperation area for plant protection UAV, and promote the realization of autonomous operation of plant protection UAV. Based on UAV lowaltitude RGB images of rapeseed field in Minle County, Zhangye City, Gansu Province, this paper constructed a method combining simple linear iterative clustering (SLIC) segmentation and VGG16 classification network to achieve boundary extraction of rapeseed field blocks. Firstly, the image was grayed with green index to distinguish bare ground and vegetation covered area. Secondly, the main part of the field was extracted by histogram analysis and contour detection. Finally, by combining simple linear iterative clustering (SLIC) and VGG16 model, the grid was divided to identify the cropping regions in the segmented region and extract the whole field. The results showed that the average intersection ratio and average accuracy of the proposed model were 95.9% and 96.0%, respectively. The accuracy and integrity of the proposed model were better than those of the traditional algorithm. The model proposed in this paper can eliminate the images of weeds taken at low altitude, which can provide reference for the extraction of farmland boundaries and pave the way for the autonomous operation of plant protection UAV.

Key words: field extraction, UAV, excess green index, super pixel segmentation, VGG16, image recognition