[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2491":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"view_count":31,"doi":8,"paper":32,"created_at":41},2491,"DASO-RiceNet: 细粒度水稻病害与损伤分类的双注意力序列优化网络(准确率 96.5%)","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F18\u002F1945","台湾朝阳科技大学 Yung-Fa Huang 等 2026 年 9 月 9 日在《Agriculture》发表(专刊'Applying Artificial Intelligence to Sustainable Crop Protection: Managing Pests and Diseases')。提出 DASO-RiceNet(Dual-Attention Semantic Optimization Network)深度学习框架,采用多阶段残差骨干进行特征提取,序列双注意力模块融合通道与空间注意力以强调诊断相关特征并抑制背景信息。在 10 类水稻病害与损伤数据集上,DASO-RiceNet 准确率 0.965、宏精确率 0.963、宏召回率 0.966、宏 F1 0.964,优于所对比的 CNN 与 Transformer 基线模型。Grad-CAM 和 LIME 提供了模型预测的定性洞察。",null,"MDPI Agriculture 2026-09-09","2026-09-08T16:00:00Z","论文",10,false,75,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},16,21,17,13,8,1,"细粒度水稻病害识别新框架，指标扎实且具可解释性，对作物病虫害智能诊断有参考价值。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","可解释AI","深度学习","水稻病害",0,{"doi":8,"openalex_id":8,"authors":33,"venue":8,"cited_by_count":31,"oa_url":8,"card":34,"direction":38,"ingested_from":40},[],{"tldr":35,"method":36,"finding":37,"direction":38,"opportunity":39},"提出双注意力序列优化网络DASO-RiceNet，实现10类水稻病害与损伤的高精度分类。","多阶段残差骨干+序列双注意力（通道与空间），在10类水稻病害数据集上训练评估。","准确率96.5%、宏F1 0.964，优于CNN与Transformer基线，Grad-CAM\u002FLI","农业人工智能与决策模型","可探索轻量化部署与田间复杂背景下的跨域泛化，并结合多模态数据提升早期病害预警能力。","agent","2026-09-15T00:04:27.118669Z"]