针对农业保险中果园情景复杂、冠层重叠导致的株数精确计数难、作物单株冠层评估不准等核心问题，通过语义分割-边缘检测双分支模型B_SegNeXt方法，研究了如何面向芒果单株进行树冠提取和精准计数。创建多情景芒果单株树冠数据集验证模型的泛化能力与分割能力，同时，设计了多尺度卷积注意力骨干网络耦合边缘检测模块，构建主动融合解码器融合边缘特征与语义特征，结合正则化损失函数缓解双分支损失的冲突，并应用海南芒果主产区的无人机影像开展验证试验。结果表明：对比了PSPNet、Deeplabv3、Deeplabv3+、U-Net、SegNeXt、SegFormer、Segmenter及B_SegNeXt识别效果，B_SegNeXt较原始模型的精确度有明显提升。可见该模型能够有效提高芒果树单木冠层检测与计数精确度，为农业保险理赔、政府补贴发放、果园精准管理和经济作物种植规划等提供技术支撑。

Regarding the issue of the persistent challenges in agricultural insurance, particularly the inaccuracies in plant census quantification and individual canopy assessment caused by canopy occlusion and heterogeneous orchard environments, which caused by the overlapping canopy and the complex orchard scenario, the B_SegNeXt method of semantic segmentation-edge detection dual-branch model was adopted to study how to extract tree crowns and accurately count for individual mango plants. A multi-scenario mango single tree canopy dataset was created to verify the generalization ability and segmentation ability of the model. Meanwhile, a multi-scale convolutional attention backbone network coupled with an edge detection module was designed, and an active fusion decoder was constructed to fuse edge features and semantic features. The regularization loss function was combined to alleviate the conflict of double-branch losses. Verification experiments were carried out using drone images from the main mango production areas in Hainan. The results show that by comparing the recognition effects of PSPNet, Deeplabv3, Deeplabv3 +, U-Net, SegNeXt, SegFormer, Segmenter and B_SegNeXt, the accuracy of B_SegNeXt has been significantly improved compared with the original model. It can be seen that this model can effectively improve the accuracy of canopy detection and counting of individual mango trees, providing technical support for agricultural insurance claims, government subsidy distribution, precise management of orchards, and planning of economic crop planting.