[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2498":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":22,"tags":24,"view_count":30,"doi":8,"paper":31,"created_at":40},2498,"Bangladesh AI Model AgroVisNet Spots Crop Blight on Phones(轻量级 99.52% 准确率 0.46 MB 量化部署)","https:\u002F\u002Fagritechinsights.com\u002Findex.php\u002F2026\u002F09\u002F10\u002Fbangladesh-ai-model-spots-crop-blight-on-phones","孟加拉国研究团队发表《AgroVisNet: A lightweight Convolutional Network and the BD-PlantDX Expert-Validated Benchmark for Radish, Potato and Pointed Gourd Disease Classification》。BD-PlantDX 基准数据集包含 12432 张高分辨率田间图像,涵盖 12 类(萝卜、马铃薯、尖瓜的健康与患病状态)。AgroVisNet 采用分组瓶颈残差块配以序列通道和空间注意力机制,在 BD-PlantDX 上达到 99.52% 测试准确率和相同加权 F1 分数。仅 290572 可训练参数,比所评估的 ImageNet 预训练轻量级骨干少 8.7-16.8 倍参数量。量化部署仅 0.46 MB,单 CPU 8.40 毫秒即可分类一张图像。",null,"Agritech Insights 2026-09-10","2026-09-09T16:00:00Z","论文",10,false,78,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,22,12,8,1,"孟加拉国团队提出轻量级CNN模型AgroVisNet并发布万张级田间病害基准数据集，99.52%准确率、0.46MB量化模型可在手机端8.4毫秒推理，对发展中国家小农户病害识别具有实质参考价值。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","边缘计算","小农农业","作物病害识别",0,{"doi":8,"openalex_id":8,"authors":32,"venue":8,"cited_by_count":30,"oa_url":8,"card":33,"direction":37,"ingested_from":39},[],{"tldr":34,"method":35,"finding":36,"direction":37,"opportunity":38},"提出轻量网络AgroVisNet与BD-PlantDX数据集，实现手机端作物病害高精度识别。","构建12432张12类田间图像基准，用分组瓶颈残差块与注意力机制训练。","测试准确率99.52%，仅29万参数，量化后0.46MB，CPU单张8.4毫秒。","农业人工智能与决策模型","可探索跨作物跨区域泛化、田间复杂光照鲁棒性及边缘设备实时多病害检测。","agent","2026-09-15T00:04:27.679059Z"]