[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1278":3,"related-1278":107},{"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":24,"tags":26,"primary_topic":31,"secondary_topics":37,"classification":43,"search_phrases":72,"slug":75,"view_count":76,"doi":77,"paper":78,"created_at":106},1278,"ERG-Mask: An edge–region collaborative instance segmentation model for greenhouse table-grape clusters","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112369","ERG-Mask: An edge–region collaborative instance segmentation model for greenhouse table-grape clusters。Computers and Electronics in Agriculture","ERG-Mask：面向温室鲜食葡萄果串的边缘-区域协同实例分割模型。《Computers and Electronics in Agriculture》",null,"Computers and Electronics in Agriculture","2026-08-31T00:00:00Z","论文",10,false,62,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,18,16,14,2,1,"针对温室葡萄簇的实例分割新方法，发表于核心期刊，技术有创新性，但影响范围限于细分领域，时效性较低。",[25],{"name":10,"url":6},[27,28,29,30],"智慧农业","农业人工智能","设施农业","葡萄",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture",{"status":44,"locked":14,"version":45,"method":46,"evidence":47},"classified","2026-10-22.2","rules",{"candidates":48,"agricultural_evidence":51},[49,56,62,67],{"code":32,"score":50,"supported":51,"title":52,"abstract":54,"tags":55},15,true,[53],"grape",[30,53],[30],{"code":39,"score":20,"supported":51,"title":57,"abstract":59,"tags":61},[58],"greenhouse",[60,58],"温室",[],{"code":63,"score":22,"supported":14,"title":64,"abstract":65,"tags":66},"I1",[],[],[27],{"code":68,"score":22,"supported":14,"title":69,"abstract":70,"tags":71},"I2",[],[],[28],[73,74],"农业人工智能 智慧农业 设施农业 葡萄","农业人工智能 智慧农业","农业人工智能智慧农业设施农业葡萄-1278",3,"10.1016\u002Fj.compag.2026.112369",{"doi":77,"openalex_id":79,"authors":80,"venue":10,"cited_by_count":98,"oa_url":9,"card":99,"direction":103,"ingested_from":105},"W7204777248",[81,84,87,90,92,95],{"name":82,"orcid":83},"Zhengtong Ning","https:\u002F\u002Forcid.org\u002F0000-0002-9218-6425",{"name":85,"orcid":86},"Jian Li","https:\u002F\u002Forcid.org\u002F0000-0002-4187-317X",{"name":88,"orcid":89},"Haidong Li","https:\u002F\u002Forcid.org\u002F0000-0002-6634-9593",{"name":91,"orcid":9},"Liangkuan Zhu",{"name":93,"orcid":94},"Qingjie Wang","https:\u002F\u002Forcid.org\u002F0009-0001-9038-7821",{"name":96,"orcid":97},"Yi Wang","https:\u002F\u002Forcid.org\u002F0000-0002-0192-3195",0,{"tldr":100,"method":101,"finding":102,"direction":103,"opportunity":104},"提出ERG-Mask模型，用于温室鲜食葡萄串的实例分割，提升边缘与区域协同精度。","结合边缘与区域信息的协同实例分割模型，基于深度学习。","ERG-Mask在葡萄串分割上优于现有方法，精度更高。","农业人工智能与决策模型","可探索该模型在复杂遮挡、不同光照下的鲁棒性，或扩展至其他簇状果实（如番茄、蓝莓）的检测。","openalex","2026-09-01T23:30:01.626379Z",{"total":108,"page":22,"page_size":108,"items":109},6,[110,161,241,303,367,417],{"id":111,"title":112,"url":113,"summary":114,"summary_zh":9,"content":9,"source_name":115,"source_url":9,"published_at":116,"category":117,"cover_url":9,"hotness":13,"is_selected":14,"score":118,"score_detail":119,"sources":123,"tags":125,"primary_topic":128,"secondary_topics":129,"classification":135,"search_phrases":156,"slug":159,"view_count":76,"doi":9,"paper":9,"created_at":160},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":18,"substance":120,"depth":18,"authority":50,"freshness":121,"relevant":22,"comment":122},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。",[124],{"name":115,"url":113},[27,28,29,126,127],"温室番茄","生长预测",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[130],{"code":131,"slug":132,"name":133,"path":134,"parent_code":36},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables",{"status":44,"locked":14,"version":45,"method":46,"evidence":136},{"candidates":137,"agricultural_evidence":51},[138,142,148,152],{"code":39,"score":17,"supported":51,"title":139,"abstract":140,"tags":141},[60],[60],[126],{"code":131,"score":143,"supported":51,"title":144,"abstract":146,"tags":147},11,[145],"番茄",[145],[],{"code":63,"score":22,"supported":14,"title":149,"abstract":150,"tags":151},[],[],[27],{"code":68,"score":22,"supported":14,"title":153,"abstract":154,"tags":155},[],[],[28],[157,158],"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":162,"title":163,"url":164,"summary":165,"summary_zh":166,"content":9,"source_name":167,"source_url":164,"published_at":168,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":169,"score_detail":170,"sources":176,"tags":178,"primary_topic":181,"secondary_topics":182,"classification":189,"search_phrases":219,"slug":222,"view_count":21,"doi":223,"paper":224,"created_at":240},2874,"Laboratory Evaluation of Multispectral Grape Quality Grade Prediction with Compensation for Controlled Illumination Variation","https:\u002F\u002Fdoi.org\u002F10.1088\u002F2631-8695\u002Faea952","Abstract Accurate fruit quality assessment is important for selective harvesting in agricultural robotics, where multispectral sensing provides a non-destructive approach but remains sen- sitive to variability in illumination conditions. This study presents a controlled laboratory evaluation conducted in a dark box under halogen illumination with fixed sensor and sam- ple geometry, in which illumination variation was simulated by adjusting lamp intensity. Prediction models for visually assigned grape quality grades were first established under fixed illumination using an AS7265x multispectral sensor with 18 channels from 410 to 940 nm, covering the visible and part of the near-infrared range. Spectral data were prepro- cessed using standard normal variate transformation, standardization, variable importance in projection feature selection, and mixup interpolation. Three machine learning meth- ods, partial least squares regression, support vector regression, and random forest, were evaluated with nested cross-validation, out-of-fold prediction, and 2000-iteration bootstrap resampling. Support vector regression achieved the best performance, with R² = 0.9084 [95% CI: 0.851–0.945], RMSE = 0.3352, and MAE = 0.2374. To address illumination vari- ability, three compensation methods, direct standardization, extended multiplicative scatter correction, and a polynomial per-band fitting method, were implemented and compared across five lamp intensity levels from 20% to 100% of rated power. The three methods and the uncompensated condition reached similar mean accuracy across the five levels, with the exception of extended multiplicative scatter correction, which was lower at every level. Di- rect standardization reduced the spread of the five predictions obtained for the same berry by about 31% relative to no compensation, and gave the highest grade-level accuracy of the four conditions. These results provide laboratory feasibility evidence that direct standard- ization can improve the consistency of grade prediction across controlled illumination levels, and establish a basis for further validation under natural field illumination toward future robotic harvesting applications.","摘要 准确的水果品质评估对于农业机器人中的选择性采收至关重要，其中多光谱传感提供了一种非破坏性方法，但仍对光照条件的变化较为敏感。本研究提出了一种在暗箱中、卤素灯照明下、传感器与样品几何构型固定的受控实验室评估方法，通过调节灯强度来模拟光照变化。首先在固定光照条件下，使用AS7265x多光谱传感器（具有410至940 nm范围内的18个通道，覆盖可见光及部分近红外波段）建立了视觉 assigned 葡萄品质等级的预测模型。光谱数据采用标准正态变量变换、标准化、投影变量重要性特征选择以及mixup插值进行预处理。采用嵌套交叉验证、折外预测和2000次迭代自助重采样，对偏最小二乘回归、支持向量回归和随机森林三种机器学习方法进行了评估。支持向量回归取得了最佳性能，R² = 0.9084 [95% CI: 0.851–0.945]，RMSE = 0.3352，MAE = 0.2374。为解决光照变化问题，实施了直接标准化、扩展多元散射校正和逐波段多项式拟合三种补偿方法，并在额定功率20%至100%的五个灯强度水平下进行了比较。三种方法及未补偿条件在五个水平下达到了相似的平均准确率，但扩展多元散射校正除外，其在每个水平下均较低。与未补偿相比，直接标准化使同一浆果五次预测结果的离散度降低了约31%，并在四种条件中给出了最高的等级准确率。这些结果为直接标准化能够提高受控光照水平下等级预测的一致性提供了实验室可行性证据，并为未来机器人采收应用中在自然田间光照下进一步验证奠定了基础。","Engineering Research Express","2026-09-17T00:00:00Z",66,{"impact":171,"substance":172,"depth":173,"authority":174,"freshness":171,"relevant":22,"comment":175},8,20,17,13,"实验室条件下多光谱葡萄品质分级与光照补偿研究，方法扎实、结论可靠，但属实验室可行性验证，产业影响有限。",[177],{"name":167,"url":164},[27,28,30,179,180],"多光谱检测","水果品质分级",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[183],{"code":184,"slug":185,"name":186,"path":187,"parent_code":188},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":44,"locked":14,"version":45,"method":46,"evidence":190},{"candidates":191,"agricultural_evidence":51},[192,196,201,208,215],{"code":32,"score":50,"supported":51,"title":193,"abstract":194,"tags":195},[53],[30,53],[30],{"code":184,"score":143,"supported":51,"title":197,"abstract":199,"tags":200},[198],"multispectral",[198],[],{"code":68,"score":202,"supported":51,"title":203,"abstract":204,"tags":207},7,[],[205,206],"机器学习","machine learning",[28],{"code":209,"score":108,"supported":51,"title":210,"abstract":211,"tags":214},"F6",[],[212,213],"农业机器人","robotic harvesting",[],{"code":63,"score":22,"supported":14,"title":216,"abstract":217,"tags":218},[],[],[27],[220,221],"多光谱 葡萄 品质分级","AS7265x 多光谱传感器","多光谱葡萄品质分级-2874","10.1088\u002F2631-8695\u002Faea952",{"doi":223,"openalex_id":225,"authors":226,"venue":167,"cited_by_count":98,"oa_url":164,"card":234,"direction":103,"ingested_from":105},"W7213509882",[227,229,231],{"name":228,"orcid":9},"Guodong Xiu",{"name":230,"orcid":9},"Sára Strakošová",{"name":232,"orcid":233},"Ikuo Mizuuchi","https:\u002F\u002Forcid.org\u002F0000-0003-4657-2613",{"tldr":235,"method":236,"finding":237,"direction":238,"opportunity":239},"在暗箱中评估多光谱葡萄品质分级预测，并比较三种光照补偿方法。","AS7265x多光谱传感器、SNV与VIP预处理、SVR\u002FRF建模、直接标准化等","SVR预测最佳（R²=0.9084）；直接标准化使同果预测离散度降低约31%。","农业遥感与作物表型","可延伸至自然田间光照下的验证，并融合机器人采摘的实时多光谱分级系统。","2026-09-18T23:30:52.790038Z",{"id":242,"title":243,"url":244,"summary":245,"summary_zh":246,"content":9,"source_name":247,"source_url":244,"published_at":248,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":249,"score_detail":250,"sources":252,"tags":254,"primary_topic":257,"secondary_topics":258,"classification":263,"search_phrases":287,"slug":290,"view_count":76,"doi":291,"paper":292,"created_at":302},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","2026-09-16T00:00:00Z",74,{"impact":50,"substance":172,"depth":173,"authority":17,"freshness":13,"relevant":22,"comment":251},"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。",[253],{"name":247,"url":244},[27,28,29,255,256],"决策支持系统","病虫害预警",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[259],{"code":68,"slug":260,"name":261,"path":262,"parent_code":188},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":44,"locked":14,"version":45,"method":46,"evidence":264},{"candidates":265,"agricultural_evidence":51},[266,272,276,283],{"code":68,"score":267,"supported":51,"title":268,"abstract":270,"tags":271},27,[206,269],"decision support",[205,206,269],[28,255],{"code":39,"score":20,"supported":51,"title":273,"abstract":274,"tags":275},[58],[60,58],[],{"code":277,"score":108,"supported":51,"title":278,"abstract":279,"tags":282},"F4",[],[280,281],"农药","pesticide",[],{"code":63,"score":22,"supported":14,"title":284,"abstract":285,"tags":286},[],[],[27],[288,289],"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":291,"openalex_id":293,"authors":294,"venue":247,"cited_by_count":98,"oa_url":244,"card":297,"direction":103,"ingested_from":105},"W7213235680",[295],{"name":296,"orcid":9},"S. Nasiketha",{"tldr":298,"method":299,"finding":300,"direction":103,"opportunity":301},"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":304,"title":305,"url":306,"summary":307,"summary_zh":308,"content":9,"source_name":309,"source_url":306,"published_at":248,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":310,"score_detail":311,"sources":314,"tags":316,"primary_topic":319,"secondary_topics":320,"classification":321,"search_phrases":343,"slug":345,"view_count":76,"doi":346,"paper":347,"created_at":366},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。","Frontiers in Plant Science",76,{"impact":50,"substance":312,"depth":173,"authority":174,"freshness":13,"relevant":22,"comment":313},21,"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。",[315],{"name":309,"url":306},[27,28,29,317,318],"目标检测","辣椒",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[],{"status":44,"locked":14,"version":45,"method":46,"evidence":322},{"candidates":323,"agricultural_evidence":51},[324,329,333,338],{"code":63,"score":202,"supported":51,"title":325,"abstract":326,"tags":328},[],[27,327],"smart agriculture",[27],{"code":39,"score":108,"supported":51,"title":330,"abstract":331,"tags":332},[],[60,58],[],{"code":68,"score":334,"supported":51,"title":335,"abstract":336,"tags":337},5,[],[317],[28,317],{"code":131,"score":339,"supported":51,"title":340,"abstract":341,"tags":342},4,[],[318],[318],[344,74],"农业人工智能 智慧农业 目标检测 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农业物联网","2026-09-16T23:30:09.868848Z",{"id":368,"title":369,"url":370,"summary":371,"summary_zh":9,"content":9,"source_name":372,"source_url":9,"published_at":373,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":374,"score_detail":375,"sources":377,"tags":379,"primary_topic":382,"secondary_topics":383,"classification":385,"search_phrases":405,"slug":407,"view_count":22,"doi":9,"paper":408,"created_at":416},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":18,"substance":120,"depth":18,"authority":20,"freshness":108,"relevant":22,"comment":376},"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。",[378],{"name":372,"url":370},[27,28,29,380,381],"黄瓜","点云分割",{"code":131,"slug":132,"name":133,"path":134,"parent_code":36},[384],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":386},{"candidates":387,"agricultural_evidence":51},[388,392,396,401],{"code":131,"score":17,"supported":51,"title":389,"abstract":390,"tags":391},[380],[380],[380],{"code":39,"score":143,"supported":51,"title":393,"abstract":394,"tags":395},[60],[60],[],{"code":68,"score":339,"supported":51,"title":397,"abstract":398,"tags":400},[],[399],"深度学习",[28],{"code":63,"score":22,"supported":14,"title":402,"abstract":403,"tags":404},[],[],[27],[406,74],"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点云分割设施农业-2215",{"doi":9,"openalex_id":9,"authors":409,"venue":9,"cited_by_count":98,"oa_url":9,"card":410,"direction":238,"ingested_from":415},[],{"tldr":411,"method":412,"finding":413,"direction":238,"opportunity":414},"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。","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":418,"title":419,"url":420,"summary":421,"summary_zh":9,"content":422,"source_name":423,"source_url":9,"published_at":424,"category":117,"cover_url":9,"hotness":13,"is_selected":14,"score":425,"score_detail":426,"sources":428,"tags":430,"primary_topic":432,"secondary_topics":433,"classification":435,"search_phrases":454,"slug":457,"view_count":339,"doi":9,"paper":9,"created_at":458},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":120,"substance":312,"depth":18,"authority":50,"freshness":171,"relevant":22,"comment":427},"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。",[429],{"name":423,"url":420},[27,28,29,126,431],"作物生长模型",{"code":131,"slug":132,"name":133,"path":134,"parent_code":36},[434],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":436},{"candidates":437,"agricultural_evidence":51},[438,442,446,450],{"code":131,"score":143,"supported":51,"title":439,"abstract":440,"tags":441},[145],[145],[],{"code":39,"score":121,"supported":51,"title":443,"abstract":444,"tags":445},[60],[],[126],{"code":68,"score":21,"supported":14,"title":447,"abstract":448,"tags":449},[],[],[431,28],{"code":63,"score":22,"supported":14,"title":451,"abstract":452,"tags":453},[],[],[27],[455,456],"作物生长模型 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