2results about How to "Prevent oversegmentation" patented technology

The invention provides a disease and pest image recognition method and system. The disease and pest image of the surface of a target crop is acquired; segmenting the pest image into a plurality of super-pixel units based on the texture complexity of the target crop; determining color gamut differences of the superpixel units and neighborhood superpixel units under different color channels, generating color gamut difference vectors representing local anomalies of the superpixel units, and identifying candidate scab regions of the target crops; determining gradient histogram features of the candidate scab area under the red channel and the blue channel, and further determining mutual information entropy of a gradient histogram between the red channel and the blue channel; based on the color gamut difference vector and the mutual information entropy corresponding to the candidate scab area, cross identification of the target crop scab is carried out, and a disease and pest plaque on the surface of the target crop is obtained. According to the technical scheme provided by the invention, the scab area and the healthy area can be accurately distinguished under the crop surface with high texture complexity.

This invention relates to a point cloud instance segmentation method for Torreya grandis seedlings that integrates deep learning and adaptive clustering. Addressing the challenge of instance segmentation caused by the long, dense, and heavily occluded leaves of Torreya grandis seedlings, this invention proposes an innovative framework: First, multi-view images are acquired using consumer-grade devices, and high-precision 3D reconstruction is achieved using 3D Gaussian sputtering technology. Then, a dedicated neural network, Torreya grandisSegNet, is designed for semantic segmentation. Its core includes a dual-attention kernel point convolution module and an adaptive enhanced inverse residual MLP module, enhancing the feature extraction capability for complex needle-like structures. In the instance segmentation stage, the Newton-Raphson optimization algorithm is introduced to improve DBSCAN clustering, enabling automatic search for the optimal parameter combination for initial segmentation. Finally, overlapping leaves are processed through topological skeleton analysis to complete refined instance separation. This method significantly improves the accuracy and adaptability of point cloud segmentation for coniferous plants, providing reliable technical support for the selection of superior Torreya grandis varieties.