Dense orchard landscape mapping based on image merging with skeleton prediction

Abstract

To address the difficulty of individual vine-plant segmentation in large orchards, this study proposes a segmentation model for individual canopies that combines the characteristics of tree deciduousness and natural growth patterns. This enables the landscape mapping of orchards under intensive planting conditions. In this study, winter and summer unmanned aerial vehicle (UAV) remote-sensing images of kiwifruit orchards were used for canopy segmentation. It involved: 1) target location identification of tree trunks in winter images and alignment of winter and summer image information; 2) semantic segmentation of branches in winter images; 3) screening of individual skeletons and prediction of branch growth; 4) removal of non-canopy parts of summer images using digital image processing to obtain canopy leaf segmentation results; and 5) segmentation of the extent of the individual plant canopies based on the predicted results of the individual plants. The branch precision rate for the final kiwifruit canopy at the plant level was 99.25% and the recall rate was 95.96%. The average recall rate of the canopy precision segmentation was 82.79%, the accuracy rate of which was 73.31%. This model does not involve plant height information and can implement instance segmentation of deciduous trees of the same species, with similar characteristics, and dense planting. The proposed solution addresses the problem of large-scale standardized planting of the same species, especially those that do not have obvious height or color differences and cannot be segmented using a digital elevation model.