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Automatic acquisition of the canopy volume parameters of the Citrus reticulate Blanco cv. Shatangju tree is of great significance to precision management of the orchard. This research combined the point cloud deep learning algorithm with the volume calculation algorithm to segment the canopy of the Citrus reticulate Blanco cv. Shatangju trees. The 3D (Three-Dimensional) point cloud model of a Citrus reticulate Blanco cv. Shatangju orchard was generated using UAV tilt photogrammetry images. The segmentation effects of three deep learning models, PointNet++, MinkowskiNet and FPConv, on Shatangju trees and the ground were compared. The following three volume algorithms: convex hull by slices, voxel-based method and 3D convex hull were applied to calculate the volume of Shatangju trees. Model accuracy was evaluated using the coefficient of determination (R2) and Root Mean Square Error (RMSE). The results show that the overall accuracy of the MinkowskiNet model (94.57%) is higher than the other two models, which indicates the best segmentation effect. The 3D convex hull algorithm received the highest R2 (0.8215) and the lowest RMSE (0.3186 m3) for the canopy volume calculation, which best reflects the real volume of Citrus reticulate Blanco cv. Shatangju trees. The proposed method is capable of rapid and automatic acquisition for the canopy volume of Citrus reticulate Blanco cv. Shatangju trees.

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SummaryAmaranthus retroflexus, a troublesome agricultural weed native to North America, has expanded its distribution in large areas of China since its introduction around 1905. Geographical interpretation of changes in their distribution ranges could provide valuable insights on its spatiotemporal invasion patterns and could be used to predict the extent of its future spread. Based on compiled historical distribution occurrences of A. retroflexus in North American and Chinese ranges, invaded ecological niche models for three hypothetical invasion stages were developed. Native models on the basis of all available records within the North American range were also generated for reciprocal comparison with the invaded model. Climate similarity between native and invasive ranges was also investigated. Amaranthus retroflexus has exhibited a rapid and large range expansion after about a 50-year lag, especially in central and western China. It established a relative stable distribution in the 1960s and has been undergoing a more continuous westward expansion since then. Presently A. retroflexus has not yet reached full occupancy of suitable habitats in China. The results highlight prioritising habitats in south-western China for monitoring and control to prevent its further spread.