[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2638":3,"related-2638":100},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":71,"slug":74,"view_count":75,"doi":76,"paper":77,"created_at":99},2638,"Full shuffle and p-rectified semi-inner powerful IoU-based chili pepper flower recognition with environment-aware YOLO11","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1935123","As a common greenhouse-grown commercial crop, chili pepper yields have always been a focus of attention. Effective management is essential for both environmental sustainability and high productivity in greenhouses. Whether for growth monitoring, yield estimates, and automated production, precise identification of chili flowers is critical. To this goal, this paper works upon the YOLO11 object detection model, focusing on the precise detection of chili flowers. The diversity of samples across various scenarios is increased by utilizing a self-built dataset of chili flowers in greenhouses and integrating data augmentation. To address YOLO11’s limitations in this task, this paper proposes a unified framework that integrates three modules: (1) full shuffle to enhance information exchange between model channels and optimize weights; (2) Since YOLO11 cannot incorporate external environmental data during training, the environment-aware C3k2 is introduced; and (3) The proposed p-rectified Semi-inner Powerful IoU accelerates model convergence and flexibility. Experiments show that our method obtains 84.8% mAP50, 50.2% mAP50-95, 81.1% Precision, and 75.6% Recall, outperforming the baseline YOLO11’s 77.3% by 7.5 percentage points, as well as YOLOv8, YOLO12, RTDETR, and Faster R-CNN under the same experimental conditions. Therefore, in terms of performance, our method generally surpasses existing algorithms for chili flower detection. Research on detecting chili flowers in greenhouses remains limited. The study offers valuable insights for the advancement of smart agriculture. Future work will further explore the model’s generalization capabilities across multiple varieties and growth stages, as well as deploy it on embedded devices for practical application.","作为常见的温室商业化种植作物，辣椒的产量一直备受关注。有效的管理对于温室的環境可持续性和高生产力都至关重要。无论是生长监测、产量估算还是自动化生产，辣椒花朵的精准识别都至关重要。为实现这一目标，本文基于YOLO11目标检测模型，聚焦辣椒花朵的精准检测。通过利用自建的温室辣椒花朵数据集并结合数据增强，增加了不同场景下样本的多样性。针对YOLO11在此任务中的局限性，本文提出了一个集成三个模块的统一框架：（1）完全混洗（full shuffle），以增强模型通道间的信息交换并优化权重；（2）由于YOLO11在训练过程中无法纳入外部环境数据，引入了环境感知C3k2（environment-aware C3k2）；（3）提出的p-rectified Semi-inner Powerful IoU加速了模型收敛并提升了灵活性。实验表明，本方法取得了84.8%的mAP50、50.2%的mAP50-95、81.1%的精确率和75.6%的召回率，较基线YOLO11的77.3%提升了7.5个百分点，并在相同实验条件下优于YOLOv8、YOLO12、RTDETR和Faster R-CNN。因此，在性能方面，本方法总体上超越了现有的辣椒花朵检测算法。目前关于温室辣椒花朵检测的研究仍然有限。本研究为智慧农业的发展提供了有价值的见解。未来工作将进一步探索模型在多个品种和生长阶段上的泛化能力，并将其部署到嵌入式设备上以实现实际应用。",null,"Frontiers in Plant Science","2026-09-16T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":21,"comment":22},15,21,17,13,1,"基于YOLO11的辣椒花检测新方法，mAP50提升7.5个百分点，对设施农业智能监测有参考价值，但属细分技术进展，影响范围有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","设施农业","目标检测","辣椒",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":47},[44,52,59,65],{"code":45,"score":46,"supported":47,"title":48,"abstract":49,"tags":51},"I1",7,true,[],[26,50],"smart 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Li","https:\u002F\u002Forcid.org\u002F0000-0001-5571-0518",0,{"tldr":92,"method":93,"finding":94,"direction":95,"opportunity":96},"基于YOLO11改进，实现温室辣椒花精准检测。","自建数据集+数据增强，引入全混洗、环境感知C3k2和p校正IoU。","mAP50达84.8%，比基线YOLO11提升7.5个百分点，优于多个对比模型。","农业人工智能与决策模型","温室辣椒花检测研究少，可探索多品种、多生长期泛化及嵌入式部署。","智慧农业 \u002F 农业物联网","openalex","2026-09-16T23:30:09.868848Z",{"total":53,"page":21,"page_size":53,"items":101},[102,154,220,271,315,388],{"id":103,"title":104,"url":105,"summary":106,"summary_zh":9,"content":9,"source_name":107,"source_url":9,"published_at":108,"category":109,"cover_url":9,"hotness":13,"is_selected":14,"score":110,"score_detail":111,"sources":116,"tags":118,"primary_topic":121,"secondary_topics":122,"classification":127,"search_phrases":149,"slug":152,"view_count":75,"doi":9,"paper":9,"created_at":153},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","报道",82,{"impact":112,"substance":113,"depth":112,"authority":17,"freshness":114,"relevant":21,"comment":115},18,22,9,"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。",[117],{"name":107,"url":105},[26,27,28,119,120],"温室番茄","生长预测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[123],{"code":66,"slug":124,"name":125,"path":126,"parent_code":36},"vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables",{"status":39,"locked":14,"version":40,"method":41,"evidence":128},{"candidates":129,"agricultural_evidence":47},[130,135,141,145],{"code":32,"score":131,"supported":47,"title":132,"abstract":133,"tags":134},12,[56],[56],[119],{"code":66,"score":136,"supported":47,"title":137,"abstract":139,"tags":140},11,[138],"番茄",[138],[],{"code":45,"score":21,"supported":14,"title":142,"abstract":143,"tags":144},[],[],[26],{"code":60,"score":21,"supported":14,"title":146,"abstract":147,"tags":148},[],[],[27],[150,151],"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 模型","中国农科院环发所温室番茄生长预测-3935","2026-10-02T11:53:11.319506Z",{"id":155,"title":156,"url":157,"summary":158,"summary_zh":159,"content":9,"source_name":160,"source_url":157,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":161,"score_detail":162,"sources":165,"tags":167,"primary_topic":170,"secondary_topics":171,"classification":177,"search_phrases":204,"slug":207,"view_count":75,"doi":208,"paper":209,"created_at":219},2682,"Machine Learning-Based Decision Support System for Greenhouse Crop Management Under Mite Infestation Conditions","https:\u002F\u002Fdoi.org\u002F10.36099\u002Fjess.v1i3.001","This paper presents an integrated machine learning-based Decision Support System (DSS) for greenhouse crop management under mite infestation conditions, specifically designed for Sri Lankan agricultural contexts. The research addresses critical challenges faced by greenhouse farmers regarding pest management and crop productivity optimization through a comprehensive system combining NetLogo simulation for synthetic data generation, ensemble machine learning models, and a web-based interface. Field research with Sri Lankan greenhouse farmers revealed that mite infestations cause up to 40% crop losses, driving panic-induced pesticide overuse and knowledge gaps in pest management timing. The system integrates environmental monitoring, pest prediction, and crop yield forecasting to provide actionable recommendations for farmers. The mite infestation prediction model achieved 86% accuracy, while the system successfully addresses data scarcity challenges through agent-based modeling. The DSS demonstrates potential for transforming reactive farming practices into predictive, data-driven approaches while accommodating the technological constraints of developing agricultural contexts.","本文提出了一种基于机器学习的集成决策支持系统（DSS），用于螨虫侵染条件下的温室作物管理，专为斯里兰卡农业情境设计。该研究针对温室农户在害虫管理和作物生产力优化方面面临的关键挑战，通过一个综合系统加以解决，该系统结合了用于合成数据生成的NetLogo仿真、集成机器学习模型以及基于网络的界面。针对斯里兰卡温室农户的实地研究表明，螨虫侵染可导致高达40%的作物损失，进而引发恐慌性农药过度使用以及害虫管理时机方面的知识缺口。该系统整合了环境监测、害虫预测和作物产量预测，为农户提供可操作的推荐建议。螨虫侵染预测模型达到了86%的准确率，同时该系统通过基于智能体的建模成功应对了数据稀缺的挑战。该决策支持系统展现出将被动应对式耕作实践转变为预测性、数据驱动方法的潜力，同时兼顾了发展中农业情境的技术约束。","Journal of Environmental and Sustainability Science",74,{"impact":17,"substance":163,"depth":19,"authority":131,"freshness":13,"relevant":21,"comment":164},20,"将机器学习与智能体仿真结合用于温室螨害预测与决策支持，方法新颖、数据翔实，对设施农业植保信息化有参考价值。",[166],{"name":160,"url":157},[26,27,28,168,169],"决策支持系统","病虫害预警",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[172],{"code":60,"slug":173,"name":174,"path":175,"parent_code":176},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":178},{"candidates":179,"agricultural_evidence":47},[180,188,193,200],{"code":60,"score":181,"supported":47,"title":182,"abstract":185,"tags":187},27,[183,184],"machine learning","decision support",[186,183,184],"机器学习",[27,168],{"code":32,"score":189,"supported":47,"title":190,"abstract":191,"tags":192},14,[57],[56,57],[],{"code":194,"score":53,"supported":47,"title":195,"abstract":196,"tags":199},"F4",[],[197,198],"农药","pesticide",[],{"code":45,"score":21,"supported":14,"title":201,"abstract":202,"tags":203},[],[],[26],[205,206],"农业人工智能 决策支持系统 病虫害预警 智慧农业","农业人工智能 决策支持系统","农业人工智能决策支持系统病虫害预警智慧农业-2682","10.36099\u002Fjess.v1i3.001",{"doi":208,"openalex_id":210,"authors":211,"venue":160,"cited_by_count":90,"oa_url":157,"card":214,"direction":95,"ingested_from":98},"W7213235680",[212],{"name":213,"orcid":9},"S. Nasiketha",{"tldr":215,"method":216,"finding":217,"direction":95,"opportunity":218},"为斯里兰卡温室农户开发基于机器学习的决策支持系统，预测螨害并优化作物管理。","NetLogo仿真生成合成数据，集成机器学习模型与网页界面。","螨害预测准确率达86%，可缓解数据稀缺并减少农药滥用。","可探索小样本下合成数据与迁移学习结合，提升发展中国家温室病虫害预测泛化能力。","2026-09-16T23:30:51.573509Z",{"id":221,"title":222,"url":223,"summary":224,"summary_zh":9,"content":9,"source_name":225,"source_url":9,"published_at":226,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":227,"score_detail":228,"sources":230,"tags":232,"primary_topic":235,"secondary_topics":236,"classification":238,"search_phrases":258,"slug":260,"view_count":21,"doi":9,"paper":261,"created_at":270},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":112,"substance":113,"depth":112,"authority":189,"freshness":53,"relevant":21,"comment":229},"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。",[231],{"name":225,"url":223},[26,27,28,233,234],"黄瓜","点云分割",{"code":66,"slug":124,"name":125,"path":126,"parent_code":36},[237],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":239},{"candidates":240,"agricultural_evidence":47},[241,245,249,254],{"code":66,"score":131,"supported":47,"title":242,"abstract":243,"tags":244},[233],[233],[233],{"code":32,"score":136,"supported":47,"title":246,"abstract":247,"tags":248},[56],[56],[],{"code":60,"score":67,"supported":47,"title":250,"abstract":251,"tags":253},[],[252],"深度学习",[27],{"code":45,"score":21,"supported":14,"title":255,"abstract":256,"tags":257},[],[],[26],[259,73],"农业人工智能 智慧农业 点云分割 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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":113,"substance":18,"depth":112,"authority":17,"freshness":281,"relevant":21,"comment":282},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。",[284],{"name":277,"url":274},[26,27,28,119,286],"作物生长模型",{"code":66,"slug":124,"name":125,"path":126,"parent_code":36},[289],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":291},{"candidates":292,"agricultural_evidence":47},[293,297,301,306],{"code":66,"score":136,"supported":47,"title":294,"abstract":295,"tags":296},[138],[138],[],{"code":32,"score":114,"supported":47,"title":298,"abstract":299,"tags":300},[56],[],[119],{"code":60,"score":302,"supported":14,"title":303,"abstract":304,"tags":305},2,[],[],[286,27],{"code":45,"score":21,"supported":14,"title":307,"abstract":308,"tags":309},[],[],[26],[311,312],"作物生长模型 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