[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4459":3,"related-4459":79},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":44,"search_phrases":74,"slug":77,"view_count":65,"doi":8,"paper":8,"created_at":78},4459,"中国农科院环发所构建知识与数据双驱动模型精准预测温室番茄生长态势","https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F572492.shtm","中国农业科学院农业环境与可持续发展研究所设施植物环境工程创新团队以番茄为对象，构建知识与数据双驱动的尺度自适应时序卷积混合模型，根据番茄不同生育期动态调整时序特征提取尺度，结合机理模型合成数据预训练与生理阈值约束，实现番茄不同器官干物质及叶面积指数的精准预测；研究成果发表在Computers and Electronics in Agriculture；仅依靠常规温室环境数据即可无损估算生长态势，对推动温室智能环控、产量预测具重要应用价值。",null,"[![Image 1: 科学网新闻频道](https:\u002F\u002Fnews.sciencenet.cn\u002Fimages\u002Fnews.jpg)](https:\u002F\u002Fnews.sciencenet.cn\u002F)\n\n[生命科学](https:\u002F\u002Fwww.sciencenet.cn\u002Flife\u002F) | [医学科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fmedicine\u002F) | [化学科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fchemistry\u002F) | [工程材料](https:\u002F\u002Fwww.sciencenet.cn\u002Fmaterial\u002F) | [信息科学](https:\u002F\u002Fwww.sciencenet.cn\u002Finformation\u002F) | [地球科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fearth\u002F) | [数理科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fmathematics\u002F) | [管理综合](https:\u002F\u002Fwww.sciencenet.cn\u002Fpolicy\u002F)[站内规定](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog-45-1064777.html) | [手机版](https:\u002F\u002Fwap.sciencenet.cn\u002F)\n\n[首页](https:\u002F\u002Fwww.sciencenet.cn\u002F) | [新闻](https:\u002F\u002Fnews.sciencenet.cn\u002F) | [博客](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog.php) | [院士](https:\u002F\u002Fnews.sciencenet.cn\u002Fys\u002F) | [人才](https:\u002F\u002Ftalent.sciencenet.cn\u002F) | [会议](https:\u002F\u002Fmeeting.sciencenet.cn\u002F) | [基金·项目](https:\u002F\u002Ffund.sciencenet.cn\u002F) | [论文](https:\u002F\u002Fpaper.sciencenet.cn\u002F) | [绘图](https:\u002F\u002Fvisual-web.sciencenet.cn\u002F) | [视频·直播](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog.php?mod=video) | [小柯机器人](https:\u002F\u002Fpaper.sciencenet.cn\u002FAInews) | [医学科普](https:\u002F\u002Fyxkp.sciencenet.cn\u002F)\n\n作者：李晨,王佳 来源：中国科学报 发布时间：2026\u002F10\u002F4 22:09:23 选择字号：小 中 大 双驱动模型可精准预测温室番茄生长态势 近日，中国农业科学院农业环境与可持续发展研究所设施植物环境工程创新团队构建了知识与数据双驱动模型，实现温室番茄生长的高精度无损预测，相关研究成果发表在《农业计算机与电子学》（_**Computers and Electronics in Agriculture**_）上。 作物生长精准估算是温室智能控制的核心前提。然而，传统机理模型参数获取难、泛化有限，而纯数据驱动模型常因缺乏生理约束致预测失真。因此，如何融合环境时序数据与作物生长规律，实现高精度且生理合理的生长预测仍是关键挑战。 科研团队以番茄为对象，提出一种知识与数据双驱动的尺度自适应时序卷积混合模型。该模型根据番茄不同生育期动态调整时序特征提取尺度，结合机理模型合成数据预训练与生理阈值约束，实现番茄不同器官干物质及叶面积指数的精准预测。该模型依靠常规温室环境数据即可无损估算生长态势，对推动温室智能环控、产量预测具有重要应用价值。 相关论文信息：https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112281 版权声明：凡本网注明“来源：中国科学报、科学网、科学新闻杂志”的所有作品，网站转载，请在正文上方注明来源和作者，且不得对内容作实质性改动；微信公众号、头条号等新媒体平台，转载请联系授权。邮箱：shouquan@stimes.cn。\n\n打印 发E-mail给：\n\n以下评论只代表网友个人观点，不代表科学网观点。\n\n 无标题页 \n\n[![Image 2](https:\u002F\u002Fnews.sciencenet.cn\u002Fimages\u002Fnewcomm.gif)](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtml\u002Fcomment.aspx?id=572492)\n\n相关新闻 相关论文\n*   1\n*   [《科学》（20261001出版）一周论文导读](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F572487.shtm)\n\n*   2\n*   [2026年度湖南省科学技术奖提名工作启动](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F572486.shtm)\n\n*   3\n*   [河南中央引导地方科技发展资金项目结项验收结果公示](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F572485.shtm)\n\n*   4\n*   [江西组织开展2027年度自然科学基金项目申报工作](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F572484.shtm)\n\n*   5\n*   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[更多>>](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog.php?mod=recommend)\n\n[关于我们](http:\u002F\u002Fwww.sciencenet.cn\u002Faboutus.aspx) | [网站声明](http:\u002F\u002Fwww.sciencenet.cn\u002Fshengming.aspx) | [服务条款](http:\u002F\u002Fwww.sciencenet.cn\u002Ftiaokuang.aspx) | [联系方式](http:\u002F\u002Fwww.sciencenet.cn\u002Fcontact.aspx) | [举报](https:\u002F\u002Fwww.sciencenet.cn\u002Fjubao.aspx) | [中国科学报社](https:\u002F\u002Fstimes.sciencenet.cn\u002F)  \n[京ICP备07017567号-12](http:\u002F\u002Fwww.beian.miit.gov.cn\u002F)互联网新闻信息服务许可证10120230008 京公网安备 11010802032783  \nCopyright @ 2007-2026 中国科学报社 All Rights Reserved  \n地址：北京市海淀区中关村南一条乙三号 电话：010-62580783","科学网 中国农业科学院 2026-10-04","2026-10-04T00:00:00Z","报道",10,false,80,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},22,21,17,14,6,1,"国家级科研机构在智慧农业领域的模型方法突破，方法新颖、结论可靠，对温室智能环控与产量预测有应用价值，值得进入每日精选。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","作物生长模型","设施番茄","温室环控",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture",{"status":45,"locked":14,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":52},[50,57,63,69],{"code":33,"score":51,"supported":52,"title":53,"abstract":55,"tags":56},12,true,[54],"番茄",[54],[30],{"code":40,"score":58,"supported":52,"title":59,"abstract":61,"tags":62},11,[60],"温室",[60],[],{"code":64,"score":65,"supported":14,"title":66,"abstract":67,"tags":68},"I2",2,[],[],[29,28],{"code":70,"score":22,"supported":14,"title":71,"abstract":72,"tags":73},"I1",[],[],[27],[75,76],"中国农科院环发所 温室番茄 生长预测","知识与数据双驱动 番茄 干物质","中国农科院环发所温室番茄生长预测-4459","2026-10-09T00:04:56.060677Z",{"total":21,"page":22,"page_size":21,"items":80},[81,129,231,273,312,362],{"id":82,"title":83,"url":84,"summary":85,"summary_zh":8,"content":86,"source_name":87,"source_url":8,"published_at":88,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":89,"score_detail":90,"sources":95,"tags":97,"primary_topic":100,"secondary_topics":101,"classification":103,"search_phrases":123,"slug":126,"view_count":127,"doi":8,"paper":8,"created_at":128},2082,"中国农科院农业环境与可持续发展研究所构建知识与数据双驱动模型赋能温室番茄生长精准预测","https:\u002F\u002Fieda.caas.cn\u002Fxwzx\u002Fkyjz\u002Faa737d68a96d4bbbb9e1dc8b8076592a.htm","中国农科院农业环境与可持续发展研究所设施植物环境工程创新团队联合荷兰瓦赫宁根大学,提出知识与数据双驱动的尺度自适应时序卷积混合模型(SATCN-Informer),根据番茄不同生育期动态调整时序特征提取尺度,结合机理模型(TOMSIM)合成数据预训练与生理阈值约束,实现番茄不同器官干物质及叶面积指数精准预测。研究成果发表于《农业计算机与电子学》(Computers and Electronics in Agriculture)。","近日，中国农业科学院农业环境与可持续发展研究所设施植物环境工程创新团队联合荷兰瓦赫宁根大学在温室番茄生长建模方面取得进展。相关研究成果发表在《农业计算机与电子学（Computers and Electronics in Agriculture）》上。\n\n![Image 1: image.png](https:\u002F\u002Fieda.caas.cn\u002Fimages\u002F2026-09\u002Feacc678d60c145b18d62aa0f87ba78b8.png)\n\n_知识与数据双驱动的SATCN-Informer模型框架_\n\n作物生长精准估算是温室智控的核心前提。然而，传统机理模型参数获取难、泛化有限，而纯数据驱动模型常因缺乏生理约束致预测失真。因此，如何融合环境时序数据与作物生长规律，实现高精度且生理合理的生长预测仍是关键挑战。\n\n研究团队以番茄为对象，提出一种知识与数据双驱动的尺度自适应时序卷积混合模型（SATCN-Informer）。该模型根据番茄不同生育期动态调整时序特征提取尺度，结合机理模型（TOMSIM）合成数据预训练与生理阈值约束，实现番茄不同器官干物质及叶面积指数的精准预测。该模型依靠常规温室环境数据即可无损估算生长态势，对推动温室智能环控、产量预测具有重要应用价值。\n\n该研究得到中央级公益性科研院所基本科研业务费资助。\n\n原文链接：https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112281","中国农业科学院","2026-09-09T00:00:00Z",84,{"impact":17,"substance":18,"depth":91,"authority":92,"freshness":93,"relevant":22,"comment":94},18,15,8,"国家级科研机构联合瓦赫宁根大学提出知识与数据双驱动的温室番茄生长预测模型，方法新颖、应用价值明确，值得进入每日精选。",[96],{"name":87,"url":84},[27,28,98,99,29],"设施农业","温室番茄",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[102],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"status":45,"locked":14,"version":46,"method":47,"evidence":104},{"candidates":105,"agricultural_evidence":52},[106,110,115,119],{"code":33,"score":58,"supported":52,"title":107,"abstract":108,"tags":109},[54],[54],[],{"code":40,"score":111,"supported":52,"title":112,"abstract":113,"tags":114},9,[60],[],[99],{"code":64,"score":65,"supported":14,"title":116,"abstract":117,"tags":118},[],[],[29,28],{"code":70,"score":22,"supported":14,"title":120,"abstract":121,"tags":122},[],[],[27],[124,125],"作物生长模型 农业人工智能 智慧农业 温室番茄","作物生长模型 农业人工智能","作物生长模型农业人工智能智慧农业温室番茄-2082",4,"2026-09-11T00:04:19.821301Z",{"id":130,"title":131,"url":132,"summary":133,"summary_zh":134,"content":8,"source_name":135,"source_url":132,"published_at":136,"category":137,"cover_url":8,"hotness":13,"is_selected":14,"score":138,"score_detail":139,"sources":141,"tags":143,"primary_topic":147,"secondary_topics":148,"classification":156,"search_phrases":205,"slug":208,"view_count":209,"doi":8,"paper":210,"created_at":230},5313,"Real-Time IoT-Integrated Ensemble LSTM–Bi-LSTM Framework for Greenhouse Microclimate Forecasting","https:\u002F\u002Fwasrpublication.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F512","Accurate prediction of greenhouse microclimates is essential for optimizing crop growth and resource utilization in precision agriculture. The research presents an IoT-integrated ensemble deep learning framework for real-time greenhouse microclimate forecasting using Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks. Environmental variables, including temperature, light intensity, soil moisture, and humidity, were acquired through IoT sensor nodes deployed in a controlled greenhouse environment. A benchmark greenhouse dataset was first used for model development and hyperparameter optimization, while an original IoT-based dataset collected during cucumber, tomato, and pepper cultivation at the Federal University Wukari, Nigeria, was used for validation and real-world deployment. The data were preprocessed through noise filtering, missing value imputation, Z-score-based outlier removal, and Min–Max normalization. A weighted ensemble strategy was then applied to combine LSTM and Bi-LSTM predictions for improved robustness. Experimental results show that the Bi-LSTM model outperformed the LSTM model, achieving R² = 0.9981, MAE = 0.0088, RMSE = 0.0110, and MSE = 0.0001, compared to LSTM results of R² = 0.9956, MAE = 0.0133, RMSE = 0.0167, and MSE = 0.0003. Statistical validation using paired t-test, Wilcoxon signed-rank test, confidence intervals, and multi-run analysis confirmed that the performance improvement is statistically significant and robust. The proposed framework enables accurate real-time microclimate forecasting and intelligent environmental monitoring, providing a scalable solution for precision greenhouse management, improved crop productivity, optimized energy utilization, and efficient irrigation scheduling.","准确预测温室微气候对于精准农业中优化作物生长和资源利用至关重要。本研究提出了一种集成物联网的集成深度学习框架，利用长短期记忆网络（LSTM）和双向长短期记忆网络（Bi-LSTM）进行实时温室微气候预测。温度、光照强度、土壤湿度和湿度等环境变量通过部署在受控温室环境中的物联网传感器节点采集。研究首先使用基准温室数据集进行模型开发和超参数优化，随后使用在尼日利亚武卡里联邦大学黄瓜、番茄和辣椒种植期间采集的原始物联网数据集进行验证和实际部署。数据经过噪声滤波、缺失值插补、基于Z分数的异常值去除和最小—最大归一化预处理。然后采用加权集成策略融合LSTM和Bi-LSTM的预测结果以提高鲁棒性。实验结果表明，Bi-LSTM模型优于LSTM模型，其R² = 0.9981、MAE = 0.0088、RMSE = 0.0110、MSE = 0.0001，而LSTM的结果为R² = 0.9956、MAE = 0.0133、RMSE = 0.0167、MSE = 0.0003。采用配对t检验、Wilcoxon符号秩检验、置信区间和多轮运行分析进行的统计验证证实，性能提升具有统计显著性和鲁棒性。所提出的框架能够实现准确的实时微气候预测和智能环境监测，为精准温室管理、提高作物生产力、优化能源利用和高效灌溉调度提供了可扩展的解决方案。","World Journal of Multidisciplinary Studies","2026-10-16T00:00:00Z","论文",67,{"impact":51,"substance":18,"depth":19,"authority":111,"freshness":93,"relevant":22,"comment":140},"方法扎实、验证充分，但属单点应用研究，公共影响有限，适合作为技术前沿条目而非头条精选。",[142],{"name":135,"url":132},[27,28,144,145,146],"农业物联网","温室环境","小气候预测",{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},[149,155],{"code":150,"slug":151,"name":152,"path":153,"parent_code":154},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":45,"locked":14,"version":46,"method":47,"evidence":157},{"candidates":158,"agricultural_evidence":52},[159,165,170,178,185,191,198],{"code":150,"score":92,"supported":52,"title":160,"abstract":162,"tags":164},[161],"IoT",[163,161],"物联网",[144],{"code":40,"score":20,"supported":52,"title":166,"abstract":168,"tags":169},[167],"greenhouse",[60,167],[],{"code":33,"score":111,"supported":52,"title":171,"abstract":172,"tags":177},[],[54,173,174,175,176],"辣椒","黄瓜","tomato","cucumber",[],{"code":70,"score":179,"supported":52,"title":180,"abstract":181,"tags":184},7,[],[182,183],"精准农业","precision agriculture",[27],{"code":64,"score":179,"supported":52,"title":186,"abstract":187,"tags":190},[],[188,189],"深度学习","deep learning",[28],{"code":192,"score":21,"supported":52,"title":193,"abstract":194,"tags":197},"H2",[],[195,196],"土壤","soil",[],{"code":199,"score":21,"supported":52,"title":200,"abstract":201,"tags":204},"H3",[],[202,203],"灌溉","irrigation",[],[206,207],"温室 小气候 LSTM 预测","尼日利亚 温室 IoT 黄瓜 番茄","温室小气候LSTM预测-5313",0,{"doi":8,"openalex_id":211,"authors":212,"venue":135,"cited_by_count":209,"oa_url":8,"card":223,"direction":227,"ingested_from":229},"W7222909837",[213,215,217,219,221],{"name":214,"orcid":8},"Siman Emmanuel*",{"name":216,"orcid":8},"Edward Onyebueke Agu",{"name":218,"orcid":8},"Musa Gani",{"name":220,"orcid":8},"Oladunjoye John Abiodun",{"name":222,"orcid":8},"Anagu Emmanuel John",{"tldr":224,"method":225,"finding":226,"direction":227,"opportunity":228},"提出物联网集成LSTM与Bi-LSTM加权集成框架，实时预测温室微气候。","IoT传感器采集温光湿数据，经预处理后用LSTM-Bi-LSTM加权集成建模。","Bi-LSTM优于LSTM，R²达0.9981，统计检验证实性能提升显著稳健。","智慧农业 \u002F 农业物联网","可探索边缘端轻量化部署与多作物跨温室迁移学习，提升实时预测泛化性。","openalex","2026-10-19T23:30:13.295236Z",{"id":232,"title":233,"url":234,"summary":235,"summary_zh":8,"content":236,"source_name":237,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":238,"score_detail":239,"sources":241,"tags":243,"primary_topic":246,"secondary_topics":247,"classification":249,"search_phrases":268,"slug":271,"view_count":22,"doi":8,"paper":8,"created_at":272},4259,"中国农科院构建知识与数据双驱动模型精准预测温室番茄生长","https:\u002F\u002Fwap.sciencenet.cn\u002Fmobile.php?cat=T&id=572492&mobile=1&type=detail","中国农业科学院农业环境与可持续发展研究所设施植物环境工程创新团队提出知识与数据双驱动的尺度自适应时序卷积混合模型，根据番茄不同生育期动态调整时序特征提取尺度，结合机理模型合成数据预训练与生理阈值约束，实现番茄不同器官干物质及叶面积指数的精准预测。成果发表在《Computers and Electronics in Agriculture》。","գйũҵѧԺũҵɳչоʩֲﻷ̴Ŷӹ֪ʶ˫ģͣʵҷĸ߾Ԥ⣬оɹڡũҵѧComputers and Electronics in Agricultureϡ\n\n׼ܿƵĺǰᡣȻͳģͲȡѡޣģͳȱԼԤʧ档ˣںϻʱɣʵָ߾Ԥǹؼս","科学网 2026-10-04",82,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":93,"relevant":22,"comment":240},"国家级科研机构在智慧种植领域提出知识数据双驱动模型，方法新颖、结论可靠，对温室作物精准管理有参考价值。",[242],{"name":237,"url":234},[27,28,99,244,245],"知识数据双驱动","生长预测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[248],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"status":45,"locked":14,"version":46,"method":47,"evidence":250},{"candidates":251,"agricultural_evidence":52},[252,256,260,264],{"code":33,"score":58,"supported":52,"title":253,"abstract":254,"tags":255},[54],[54],[],{"code":40,"score":111,"supported":52,"title":257,"abstract":258,"tags":259},[60],[],[99],{"code":70,"score":22,"supported":14,"title":261,"abstract":262,"tags":263},[],[],[27],{"code":64,"score":22,"supported":14,"title":265,"abstract":266,"tags":267},[],[],[28],[269,270],"中国农科院 温室番茄 生长预测","知识数据双驱动 模型 番茄","中国农科院温室番茄生长预测-4259","2026-10-06T00:05:01.795653Z",{"id":274,"title":275,"url":276,"summary":277,"summary_zh":8,"content":8,"source_name":278,"source_url":8,"published_at":279,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":238,"score_detail":280,"sources":282,"tags":284,"primary_topic":285,"secondary_topics":286,"classification":288,"search_phrases":307,"slug":309,"view_count":310,"doi":8,"paper":8,"created_at":311},3935,"知识与数据双驱动赋能温室番茄生长精准预测——中国农科院环发所研究成果发表","https:\u002F\u002Fwww.caas.cn\u002Fxwzx\u002Fkyhd\u002F613bca00d9e84cfeb977251a606af346.htm","近日，中国农业科学院农业环境与可持续发展研究所设施植物环境工程创新团队构建了知识与数据双驱动模型，实现温室番茄生长的高精度无损预测，研究成果发表在《农业计算机与电子学（Computers and Electronics in Agriculture）》上。科研团队提出一种尺度自适应时序卷积混合模型，根据番茄不同生育期动态调整时序特征提取尺度，结合机理模型合成数据预训练与生理阈值约束，实现番茄不同器官干物质及叶面积指数的精准预测。","中国农业科学院·2026年10月","2026-10-01T00:00:00Z",{"impact":91,"substance":17,"depth":91,"authority":92,"freshness":111,"relevant":22,"comment":281},"国家级科研机构在核心期刊发表的知识与数据双驱动温室番茄生长预测模型，方法新颖、结论可靠，对智慧农业与设施种植有实质参考价值。",[283],{"name":278,"url":276},[27,28,98,99,245],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},[287],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":45,"locked":14,"version":46,"method":47,"evidence":289},{"candidates":290,"agricultural_evidence":52},[291,295,299,303],{"code":40,"score":51,"supported":52,"title":292,"abstract":293,"tags":294},[60],[60],[99],{"code":33,"score":58,"supported":52,"title":296,"abstract":297,"tags":298},[54],[54],[],{"code":70,"score":22,"supported":14,"title":300,"abstract":301,"tags":302},[],[],[27],{"code":64,"score":22,"supported":14,"title":304,"abstract":305,"tags":306},[],[],[28],[75,308],"知识与数据双驱动 番茄 模型","中国农科院环发所温室番茄生长预测-3935",3,"2026-10-02T11:53:11.319506Z",{"id":313,"title":314,"url":315,"summary":316,"summary_zh":8,"content":8,"source_name":317,"source_url":8,"published_at":318,"category":137,"cover_url":8,"hotness":13,"is_selected":14,"score":319,"score_detail":320,"sources":322,"tags":324,"primary_topic":326,"secondary_topics":327,"classification":329,"search_phrases":348,"slug":351,"view_count":22,"doi":8,"paper":352,"created_at":361},2215,"中国农业大学马韫韬教授团队:攻克温室黄瓜冠层点云分割难题 助力设施数字化精准管理","https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F569056.html","马韫韬教授团队构建Cuc-HiSeg分割框架,通过尺度变换、角度旋转、边缘扰动等策略扩充数据集,显著缓解三维标注样本稀缺难题,使深度学习模型分割F1值提升5.6%-6.4%;研发结合形态学优化与K-Means++二次分割的改进区域生长算法,有效克服叶片重叠与点云缺失干扰,叶片实例分割F1值最高达95.0%,在结果期复杂场景下仍保持93.9%的高精度。该方法在苗期、开花初期、结果期均表现出优异的稳定性与适应性,可高效完成日光温室黄瓜冠层点云的高精度解析。","农业信息网 2026年9月8日","2026-09-08T00:00:00Z",78,{"impact":91,"substance":17,"depth":91,"authority":20,"freshness":21,"relevant":22,"comment":321},"中国农大团队提出黄瓜冠层点云分割新框架，F1值提升显著且多生育期稳定，属设施农业数字化领域有实质增量的技术突破。",[323],{"name":317,"url":315},[27,28,98,174,325],"点云分割",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[328],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"status":45,"locked":14,"version":46,"method":47,"evidence":330},{"candidates":331,"agricultural_evidence":52},[332,336,340,344],{"code":33,"score":51,"supported":52,"title":333,"abstract":334,"tags":335},[174],[174],[174],{"code":40,"score":58,"supported":52,"title":337,"abstract":338,"tags":339},[60],[60],[],{"code":64,"score":127,"supported":52,"title":341,"abstract":342,"tags":343},[],[188],[28],{"code":70,"score":22,"supported":14,"title":345,"abstract":346,"tags":347},[],[],[27],[349,350],"农业人工智能 智慧农业 点云分割 设施农业","农业人工智能 智慧农业","农业人工智能智慧农业点云分割设施农业-2215",{"doi":8,"openalex_id":8,"authors":353,"venue":8,"cited_by_count":209,"oa_url":8,"card":354,"direction":358,"ingested_from":360},[],{"tldr":355,"method":356,"finding":357,"direction":358,"opportunity":359},"构建Cuc-HiSeg框架，实现温室黄瓜冠层点云高精度叶片实例分割。","数据增强扩充点云样本，结合改进区域生长与K-Means++二次分割。","叶片分割F1最高95.0%，结果期复杂场景仍达93.9%，各生育期稳定。","农业遥感与作物表型","可迁移至其他设施作物冠层解析，并探索点云分割与生长量、产量性状的自动关联。","agent","2026-09-12T00:06:40.512918Z",{"id":363,"title":364,"url":365,"summary":366,"summary_zh":367,"content":8,"source_name":368,"source_url":365,"published_at":369,"category":137,"cover_url":8,"hotness":13,"is_selected":14,"score":370,"score_detail":371,"sources":373,"tags":375,"primary_topic":378,"secondary_topics":379,"classification":386,"search_phrases":410,"slug":412,"view_count":65,"doi":413,"paper":414,"created_at":433},1599,"Continual Learning Model Evaluation for Hyperspectral-Based Tomato Bacterial Spot Detection in Greenhouse","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102535","Detection of tomato bacterial spot (TBS) at canopy level is critical in greenhouse seedling production, yet most previous hyperspectral studies have been conducted at leaf scale under tightly controlled conditions. This study evaluated a domain-incremental continual-learning framework for automated canopy-scale TBS detection using hyperspectral sensing across five greenhouse experiments. Three spectral feature sets were evaluated, and Full-memory and Bounded-memory Gradient Episodic Memory multilayer perceptrons (GEM-MLP) were compared with Sequential and Joint-training MLP models under two conditions: a future unseen-experiment generalization test (Scenario 1) and a same-distribution benchmark (Scenario 2). Genetic-algorithm feature selection reduced input dimensionality but did not identify a single feature subset that was stable across all folds and scenarios. Under Scenario 1, Full-memory GEM-MLP using the fixed 18-VI feature set achieved 58.1% asymptomatic recall and 81.2% symptomatic recall. Its clearest advantage was knowledge retention: backward transfer (BWT) was -1.4%, compared with -7.2% for Sequential MLP, while forward transfer was similar (7.4% versus 7.0%). Under Scenario 2, Full-memory GEM-MLP achieved 46.5% asymptomatic recall and 86.2% symptomatic recall, while Joint-training MLP was highly competitive and reached the highest mean asymptomatic recall (53.4%). Full-memory GEM-MLP again showed better retention, with BWT of +1.8% compared with -3.2% for Sequential MLP. These results indicate that GEM did not provide universally superior classification performance, but it consistently reduced forgetting during sequential learning. The persistent difficulty of asymptomatic detection, even when all experimental domains were represented during model development, further suggests that early canopy-scale disease detection is limited by factors beyond cross-experiment distribution shift.","在温室育苗生产中，冠层尺度上的番茄细菌性斑点病（TBS）检测至关重要，然而以往大多数高光谱研究都是在严格控制条件下于叶片尺度进行的。本研究评估了一种基于域增量持续学习框架的自动化冠层尺度TBS检测方法，该方法利用跨五个温室实验的高光谱传感数据。研究评估了三组光谱特征集，并在两种条件下将全记忆与有界记忆梯度情景记忆多层感知器（GEM-MLP）与顺序训练和联合训练MLP模型进行了比较：一种是对未来未见实验的泛化测试（情景1），另一种是同分布基准测试（情景2）。遗传算法特征选择降低了输入维度，但未能识别出在所有折和情景下均稳定的单一特征子集。在情景1下，使用固定18-VI特征集的全记忆GEM-MLP实现了58.1%的无症状召回率和81.2%的有症状召回率。其最显著的优势在于知识保持能力：后向迁移（BWT）为-1.4%，而顺序MLP为-7.2%，同时前向迁移相近（7.4%对7.0%）。在情景2下，全记忆GEM-MLP实现了46.5%的无症状召回率和86.2%的有症状召回率，而联合训练MLP表现极具竞争力，达到了最高的平均无症状召回率（53.4%）。全记忆GEM-MLP再次展现出更好的保持能力，其BWT为+1.8%，而顺序MLP为-3.2%。这些结果表明，GEM并未提供普遍优越的分类性能，但在顺序学习过程中始终减少了遗忘。即使在模型开发阶段涵盖了所有实验域，无症状检测仍持续存在困难，这进一步表明早期冠层尺度病害检测受到跨实验分布偏移之外因素的限制。","Smart Agricultural Technology","2026-09-01T00:00:00Z",64,{"impact":51,"substance":91,"depth":91,"authority":51,"freshness":127,"relevant":22,"comment":372},"研究评估持续学习模型在温室番茄细菌性斑点病高光谱检测中的应用，虽为细分领域进展，但方法新颖且数据详实，对农业AI落地有参考价值。",[374],{"name":368,"url":365},[27,28,54,376,377],"遥感","病害检测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[380,381],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"code":382,"slug":383,"name":384,"path":385,"parent_code":154},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":45,"locked":14,"version":46,"method":47,"evidence":387},{"candidates":388,"agricultural_evidence":52},[389,393,397,402,406],{"code":33,"score":92,"supported":52,"title":390,"abstract":391,"tags":392},[175],[54,175],[54],{"code":40,"score":20,"supported":52,"title":394,"abstract":395,"tags":396},[167],[60,167],[],{"code":382,"score":51,"supported":52,"title":398,"abstract":400,"tags":401},[399],"hyperspectral",[399],[376],{"code":70,"score":22,"supported":14,"title":403,"abstract":404,"tags":405},[],[],[27],{"code":64,"score":22,"supported":14,"title":407,"abstract":408,"tags":409},[],[],[28],[411,350],"农业人工智能 智慧农业 病害检测 番茄","农业人工智能智慧农业病害检测番茄-1599","10.1016\u002Fj.atech.2026.102535",{"doi":413,"openalex_id":415,"authors":416,"venue":368,"cited_by_count":209,"oa_url":365,"card":428,"direction":227,"ingested_from":229},"W7207729517",[417,419,421,424,426],{"name":418,"orcid":8},"Gaoshoutong Si",{"name":420,"orcid":8},"Peter Ling",{"name":422,"orcid":423},"Anna L. Testen","https:\u002F\u002Forcid.org\u002F0000-0002-6504-9599",{"name":425,"orcid":8},"Heping Zhu",{"name":427,"orcid":8},"Hongyoung Jeon",{"tldr":429,"method":430,"finding":431,"direction":227,"opportunity":432},"评估持续学习框架用于温室高光谱番茄细菌性斑点病冠层检测。","域增量持续学习，GEM-MLP与顺序\u002F联合训练MLP对比，遗传算法特征选择。","GEM-MLP减少遗忘但分类性能非最优，无症状检测仍困难。","可探索结合多模态数据或更先进持续学习方法，提升早期无症状病害检测精度。","2026-09-04T23:30:04.578607Z"]