[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3606":3,"related-3606":62},{"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,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":61},3606,"A decision-making method for light regulation of cucumber seedlings considering changes of temperature and CO2 in protected agriculture","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.biosystemseng.2026.104601","Light serves as the primary energy source for photosynthesis and significantly influences plant morphology and biomass accumulation. In protected agricultural systems, light environment parameters (intensity and spectral quality) critically determine crop productivity. A data-driven framework for light optimisation in cucumber seedlings was developed. First, an artificial neural network was established to predict net photosynthetic rate using empirical data spanning diverse environmental regimes. Then, to jointly maximise photosynthetic efficiency and minimise energy consumption, two key innovations were implemented. The U-chord method was employed to derive target light intensities, and a hybrid cubic spline-global Newton algorithm was designed for spectral quality optimisation. These subsystems were fused through support vector regression to construct adaptive decision-making models for real-time light management. The results showed that the artificial neural network model achieved exceptional Pn prediction accuracy with coefficient of determination > 0.95, root mean square error \u003C 1.6 μmol m −2 s −1 , and mean absolute error ≤ 1.2 μmol m −2 s −1 . The target light intensity points effectively delineated the light-limited phase of the Pn–PPFD response from the light-saturated phase. Final decision-making models showed high generalisability (coefficient of determination > 0.97 for both spectral and intensity decision-making models). Simulations quantified the advantages of the proposed strategy. Compared to conventional maximum Pn-oriented strategies, the proposed method reduced lighting energy consumption by 40-46%, while dynamic spectral adjustments enhanced photosynthetic efficiency by 5% relative to static controls. This study effectively improved the efficiency of light energy utilisation, and provided a theoretical method for light regulation in protected agriculture.","光作为光合作用的主要能量来源，显著影响植物形态和生物量积累。在设施农业系统中，光环境参数（光强与光谱质量）对作物生产力具有决定性作用。本研究开发了一种数据驱动的黄瓜幼苗光优化框架。首先，基于涵盖多种环境条件的经验数据，建立了人工神经网络（artificial neural network）以预测净光合速率。随后，为实现光合效率最大化与能耗最小化的协同优化，实施了两项关键创新：采用U弦法（U-chord method）确定目标光强，并设计了三次样条-全局牛顿混合算法（hybrid cubic spline-global Newton algorithm）用于光谱质量优化。通过支持向量回归（support vector regression）融合各子系统，构建了用于实时光管理的自适应决策模型。结果表明，人工神经网络模型实现了优异的净光合速率预测精度，决定系数>0.95，均方根误差\u003C1.6 μmol m⁻² s⁻¹，平均绝对误差≤1.2 μmol m⁻² s⁻¹。目标光强点有效划分了净光合速率-光合光子通量密度响应曲线中的光限制阶段与光饱和阶段。最终决策模型表现出良好的泛化能力（光谱决策模型与光强决策模型的决定系数均>0.97）。仿真量化了所提策略的优势。与传统最大净光合速率导向策略相比，该方法将补光能耗降低了40%~46%，同时动态光谱调节使光合效率较静态对照提高了5%。本研究有效提升了光能利用效率，为设施农业光调控提供了理论方法。",null,"Biosystems Engineering","2026-09-26T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,9,1,"该研究提出数据驱动的设施黄瓜育苗光调控决策方法，节能40-46%且提升光合效率，方法新颖、数据可靠，对智慧农业光环境管理有较高参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","设施农业","黄瓜育苗","光环境调控",[32,33],"设施农业 光环境 黄瓜育苗","光合速率 光强 光谱 优化","设施农业光环境黄瓜育苗-3606",0,"10.1016\u002Fj.biosystemseng.2026.104601",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":6,"card":54,"direction":58,"ingested_from":60},"W7214421951",[40,43,46,48,51],{"name":41,"orcid":42},"Pan Gao","https:\u002F\u002Forcid.org\u002F0000-0002-5184-5674",{"name":44,"orcid":45},"Huimin Li","https:\u002F\u002Forcid.org\u002F0009-0001-9124-7628",{"name":47,"orcid":9},"Jinghua Xu",{"name":49,"orcid":50},"Miao Lu","https:\u002F\u002Forcid.org\u002F0000-0001-6539-2170",{"name":52,"orcid":53},"Jin Ping Hu","https:\u002F\u002Forcid.org\u002F0000-0001-5532-6890",{"tldr":55,"method":56,"finding":57,"direction":58,"opportunity":59},"构建数据驱动光调控决策框架，优化黄瓜幼苗光强与光谱以提升光合效率并降低能耗。","人工神经网络预测净光合速率，U弦法确定光强，三次样条-全局牛顿法优化光谱，支持向","模型预测精度高，较传统策略降低能耗40-46%，动态光谱调整提升光合效率5%。","农业人工智能与决策模型","可探索多环境因子耦合的实时闭环光调控，并迁移至其他设施作物验证泛化性。","openalex","2026-09-27T23:30:09.843641Z",{"total":63,"page":21,"page_size":63,"items":64},6,[65,107,137,161,193,233],{"id":66,"title":67,"url":68,"summary":69,"summary_zh":70,"content":9,"source_name":71,"source_url":68,"published_at":72,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":73,"score_detail":74,"sources":79,"tags":81,"search_phrases":84,"slug":87,"view_count":21,"doi":88,"paper":89,"created_at":106},3017,"Automated seedling vigor estimation in cucumbers using digital image processing","https:\u002F\u002Fdoi.org\u002F10.65764\u002Ftjas.2026.267412","Background and Objective: Farmers often rely on experience rather than quantitative indicators to determine seedling quality, resulting in inefficiencies in resource allocation. This study aimed to (1) compare seedling vigor and growth characteristics between open-pollinated (OP) and F1 hybrid cucumber seed types, and (2) develop a non-destructive predictive model for seedling vigor estimation based on digital image analysis.Methodology: Seedling images were acquired under controlled LED lighting using a 5 MP digital camera positioned 30 cm above 14-day-old seedlings. A YOLOv8-based object detection model was applied to detect and isolate true leaf regions, from which pixel count and RGB color values were extracted as input features for a multiple linear regression model to predict the seedling vigor index (SVI).Main Results: The YOLOv8 object detection model achieved a mean average precision (mAP@0.5) of 96.00%, precision of 93.40%, and recall of 94.40% in detecting true leaves. Multiple linear regression analysis was conducted using the Scikit-learn library in Python. Scikit-learn provides regression-based machine learning algorithms, including multiple linear regression. The equation was then applied to the training and test sets, using the pixel values of true leaves as the independent variable and SVI as the dependent variable. Using multiple regression analysis, the trained model generated the equation SVI = -2.27 + (2.84 × 10-5 × pixel) + (3.48 × 10-5 × R) + (-7.49 × 10-2 × G) + (2.06 × 10-1 × B), R2 = 0.57 and RMSE = 1.10. The model achieved the test set, r = 0.79 and RMSE = 1.34. The model performance showed a correlation coefficient of 0.85 for OP data and 0.91 for F1 hybrid data.Conclusions: The results demonstrate that digital image processing combined with object detection provides a non-destructive and effective approach to estimate seedling vigor quality. The predictive models developed from OP and F1 hybrid datasets indicate potential application in precision agriculture for automated seedling quality assessment and transplanting decision support.","背景与目标：农民通常依赖经验而非定量指标来判断幼苗质量，导致资源配置效率低下。本研究旨在（1）比较开放授粉（OP）与F1杂交黄瓜种子类型之间的幼苗活力和生长特性，（2）基于数字图像分析开发一种用于幼苗活力评估的无损预测模型。方法：使用500万像素数码相机置于14日龄幼苗上方30 cm处，在受控LED光照下获取幼苗图像。应用基于YOLOv8的目标检测模型检测并分离真叶区域，从中提取像素计数和RGB颜色值作为多元线性回归模型的输入特征，以预测幼苗活力指数（SVI）。主要结果：YOLOv8目标检测模型在检测真叶时达到了96.00%的平均精度均值（mAP@0.5）、93.40%的精确率和94.40%的召回率。使用Python中的Scikit-learn库进行多元线性回归分析。Scikit-learn提供基于回归的机器学习算法，包括多元线性回归。随后将该方程应用于训练集和测试集，以真叶像素值作为自变量，SVI作为因变量。通过多元回归分析，训练模型生成的方程为SVI = -2.27 + (2.84 × 10-5 × 像素) + (3.48 × 10-5 × R) + (-7.49 × 10-2 × G) + (2.06 × 10-1 × B)，R2 = 0.57，RMSE = 1.10。该模型在测试集上达到r = 0.79，RMSE = 1.34。模型性能显示，OP数据的相关系数为0.85，F1杂交数据的相关系数为0.91。结论：结果表明，数字图像处理结合目标检测为评估幼苗活力质量提供了一种无损且有效的方法。基于OP和F1杂交数据集开发的预测模型表明，其在精准农业中具有用于自动化幼苗质量评估和移栽决策支持的潜在应用。","Thai Journal of Agricultural Science","2026-09-19T00:00:00Z",71,{"impact":75,"substance":76,"depth":77,"authority":75,"freshness":20,"relevant":21,"comment":78},12,21,17,"基于YOLOv8与多元回归的黄瓜幼苗活力无损估测，方法具体、指标完整，对智慧育苗有参考价值，但属细分作物研究，影响范围有限。",[80],{"name":71,"url":68},[26,27,82,83,29],"无损检测","图像识别",[85,86],"黄瓜 幼苗活力 图像处理","YOLOv8 幼苗 检测","黄瓜幼苗活力图像处理-3017","10.65764\u002Ftjas.2026.267412",{"doi":88,"openalex_id":90,"authors":91,"venue":71,"cited_by_count":35,"oa_url":68,"card":100,"direction":105,"ingested_from":60},"W7213631983",[92,94,96,98],{"name":93,"orcid":9},"Thanabodee Withunchettanan",{"name":95,"orcid":9},"Raksak Sermsak",{"name":97,"orcid":9},"Pichittra Kaewsorn",{"name":99,"orcid":9},"Kriengkri Kaewtrakulpong",{"tldr":101,"method":102,"finding":103,"direction":58,"opportunity":104},"用YOLOv8检测黄瓜真叶并结合多元回归，实现幼苗活力指数无损预测。","LED下拍摄14天幼苗，YOLOv8分割真叶，提取像素与RGB做多元线性回归。","YOLOv8检测mAP@0.5达96%，模型测试r=0.79，F1杂交种相关性达0.91。","可扩展多品种、多环境数据，融合时序图像与深度学习提升活力预测泛化性。","数字乡村与农业信息化","2026-09-20T23:30:26.929607Z",{"id":108,"title":109,"url":110,"summary":111,"summary_zh":9,"content":9,"source_name":112,"source_url":9,"published_at":113,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":114,"score_detail":115,"sources":119,"tags":121,"search_phrases":124,"slug":127,"view_count":35,"doi":9,"paper":128,"created_at":136},2904,"Decoupled Foundation Models:基于YOLO26m+SAM2+DINOv2的湿度诱导番茄叶坏死实例分割与检测,登MDPI Agriculture 16(18)1997","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F18\u002F1997","本研究针对温室番茄相对湿度过高引发的非生物胁迫(生理性叶坏死,与生物感染症状相似),提出多步AI管道自动化分割与分类坏死叶斑。采集218张RGB图像、3218个标注(棕色坏死斑\u002F黄色坏死斑\u002F无坏死),系统评估6种端到端实例分割管道(YOLO26m检测+SAM2零样本分割+微调DINOv2或EfficientNet-B3分类);微调DINOv2宏F1达0.926,优于EfficientNet-B3、ResNet-50、Swin-Small基线(0.886-0.901);最佳配置mAP@50=0.828,较YOLO26m单模型提升约8%。","MDPI Agriculture","2026-09-17T00:00:00Z",78,{"impact":116,"substance":18,"depth":17,"authority":117,"freshness":20,"relevant":21,"comment":118},16,13,"方法组合新颖、数据规模与对比基线扎实，对温室番茄生理性叶坏死自动识别有实用价值，值得进入每日精选。",[120],{"name":112,"url":110},[26,27,28,122,123],"番茄","病害识别",[125,126],"番茄叶坏死 实例分割","农业人工智能 智慧农业 病害识别 设施农业","番茄叶坏死实例分割-2904",{"doi":9,"openalex_id":9,"authors":129,"venue":9,"cited_by_count":35,"oa_url":9,"card":130,"direction":58,"ingested_from":135},[],{"tldr":131,"method":132,"finding":133,"direction":58,"opportunity":134},"用YOLO26m+SAM2+DINOv2多步管道分割并分类高湿诱导的番茄叶坏死斑。","218张RGB图像、3218个标注，评估6种实例分割管道并微调DINOv2分类。","微调DINOv2宏F1达0.926，最佳配置mAP@50=0.828，较单模型提升约8%。","可探索零样本基础模型在多种非生物胁迫症状上的泛化与轻量化温室部署。","agent","2026-09-19T00:06:09.021594Z",{"id":138,"title":139,"url":140,"summary":141,"summary_zh":9,"content":142,"source_name":143,"source_url":9,"published_at":144,"category":145,"cover_url":9,"hotness":13,"is_selected":14,"score":114,"score_detail":146,"sources":151,"tags":153,"search_phrases":156,"slug":159,"view_count":21,"doi":9,"paper":9,"created_at":160},2729,"WAFI2026世界农业科技创新大会在京举行——辽宁省农科院等亮相","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7686105927610630691\u002F","2026世界农业科技创新大会(WAFI2026)9月16—19日在北京平谷举行。中国农业大学校长孙其信指出,人工智能已经从过去的示范,变成了部分大型新型经营主体的主要生产方式。中国农业大学王耀君副教授介绍神农大模型3.0已在非洲落地。北京数智京园智慧设施管控技术体系亮相。","潮新闻 记者 沈爱群 侴雪妍\n\n2026世界农业科技创新大会，正在北京举行。\n\n9月16日上午，在大会举行的“人工智能与农业论坛”上，潮新闻记者捕捉到了一个话题：人工智能如何造福农民？\n\n这个话题，由中国农业大学全球食物经济与政策研究院院长樊胜根教授在论坛致辞中提出。\n\n会上，与会国内外专家学者、业界代表见仁见智给出了答案：通过农业人工智能，可以让农民种得好、种得起、种得稳、种得赚。其中，深谙东方智慧的“中国方案”得到了与会嘉宾的点赞与关注。\n\n世界农业科技创新大会（英文缩写WAFI），是以“创新农业 共享未来”为宗旨的世界农业盛会，致力于打造农业“达沃斯”。自2023年成功举办以来，WAFI赢得了国内外同行的高度认可，被誉为世界三大农业盛会之一。\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F59a79b9b009545259d6d2d50486c2167~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1790208715&x-signature=Mbp32uTVtyQxX7rWipDc9RKtXnU%3D)\n活动现场（记者 沈爱群 摄）\n\n在这个世界级农业盛会上，与会嘉宾为何特别关注“人工智能与农民”？\n\n答案，可以从人工智能时代全球农民、小农户面临的挑战找到。\n\n农业人工智能论坛上，中国农业大学校长、中国工程院院士陈卫就谈到：当前全球农食系统面临前所未有的挑战，气候变化加剧了农业生产的风险，土地、水资源和生物多样性承受着越来越大的压力。“我们必须生产更多更有营养的食物，降低农业对环境的影响，为农村地区创造更好的发展机会。然而这些挑战在不同地区的分布并不均衡，一端是资本和技术高度密集的现代化农场，另一边是数以亿计、以有限的资源支撑全球重要粮食供给的农民与小农户。”\n\n挑战面前，人工智能为农业转型注入了新动能。正如与会嘉宾在本次论坛上提及，人工智能正在推动智能育种、精准种植和农业生产全产业链系统发生新的变化，以卫星遥感数据、气象预警数据、土壤变化数据、种子种植数据以及营销数据分析等，帮助农民实现种植方案自动生成、无人机出苗率检测、卫星遥感旱涝、摄像头自动巡田、智能拼车等农业生产及农事经营。\n\n推动这些新变化的“中国方案”中，有着力农业教育的中国高校，有从事农业生产的中国农业企业，也有站在消费端的中国城市设施农业。\n\n先看中国高校。会上，中国农业大学信息与电子工程学院副教授王耀君和中国农业大学全球食物经济与政策研究院教授张玉梅，分别在主旨演讲中提到了“神农大模型”以及“农业食物经济与政策AI模型”。\n\n神农大模型，去年就已做到了3.0版。作为国内首个实现农业系统智能的大模型，神农1.0版于2023年问世，可以实现农业专业知识的精准查询与问答。2024年迭代的神农2.0版，拓展了技术边界，可以整合文本、图像等多模态数据进行分析决策。2025年全球首发的神农3.0版，打破了农业学科壁垒，让AI成为汇聚农业智慧的载体。\n\n神农大模型3.0是“小麦育种智能助手”。它融合了国家级种质资源与专家知识，可以赋能育种决策，实现从“田间试错”到“精准育种”。\n\n神农3.0还是个农业病虫害智能体。可以识别70类、600余种病虫害，实现用一部手机就能获得如同多个专业植保专家的指导。\n\n值得一提的是，神农3.0还可以让全球农业科技人员和从业者自主构建，以最低成本推动AI应用，让农业AI在科研院所和田间地头普惠落地。“经过过去一年的推广，神农大模型已经跨越千山万水，在非洲落地了。”神农大模型团队负责人王耀君说。\n\n农业食物经济与政策AI模型，不仅面向政府、科研机构和企业，也面向广大农业生产者，旨在将数据、经济模型与人工智能相结合，为农业市场研判和科学决策提供支持。为此，张玉梅教授提醒：“对于农民和小农户来说，这个模型提供的国内外农产品价格监测与异常预警、农业生产成本收益分析、膳食营养评价和国际市场动态分析，可以帮助他们及时了解市场变化 、比较政策方案、评估生产经营收益与营养状况。”\n\n其次，看看农业企业端。会上，北大荒信息有限公司总经理任荣荣向大家介绍了“未来农场”这个各项农艺技术集成平台。以深耕智慧农业培育发展新动能为己任的北大荒信息有限公司，既自主研发了智能装备管理平台覆盖111个农场、接入8.4万台智能装备；也让“未来农场”为60万种植户提供产前、产中、产后服务，实现农业资金交易1000亿元。\n\n再看城市设施农业。会上，北京市数字农业农村促进中心副主任、正高级农艺师芦天罡，向大家展示了北京市“数智京园”智慧设施管控技术体系和连栋温室“赛马制”中试熟化场景。\n\n据芦天罡介绍，北京市目前正通过AI+城市设施农业，实现了连栋温室的国产化技术攻关和日光温室的数智场景改造，快速推动农业产业智能化。“从系统到装备到模型，人工智能可以帮助农户解放劳动力、提高精细化生产水平，还可以调节生产周期，助推农户增收。”芦天罡表示。\n\n“转载请注明出处”","今日头条 2026年9月16日","2026-09-16T00:00:00Z","报道",{"impact":147,"substance":148,"depth":77,"authority":149,"freshness":20,"relevant":21,"comment":150},24,20,8,"世界级农业盛会现场报道，汇聚神农大模型3.0、未来农场、数智京园等多方实质进展，信息增量足，值得进入每日精选。",[152],{"name":143,"url":140},[26,27,28,154,155],"神农大模型","智能育种",[157,158],"农业人工智能 神农大模型 智慧农业 智能育种","农业人工智能 神农大模型","农业人工智能神农大模型智慧农业智能育种-2729","2026-09-17T00:04:39.330857Z",{"id":162,"title":163,"url":164,"summary":165,"summary_zh":166,"content":9,"source_name":167,"source_url":164,"published_at":144,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":168,"score_detail":169,"sources":172,"tags":174,"search_phrases":177,"slug":180,"view_count":35,"doi":181,"paper":182,"created_at":192},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":170,"substance":148,"depth":77,"authority":75,"freshness":13,"relevant":21,"comment":171},15,"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。",[173],{"name":167,"url":164},[26,27,28,175,176],"决策支持系统","病虫害预警",[178,179],"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":181,"openalex_id":183,"authors":184,"venue":167,"cited_by_count":35,"oa_url":164,"card":187,"direction":58,"ingested_from":60},"W7213235680",[185],{"name":186,"orcid":9},"S. Nasiketha",{"tldr":188,"method":189,"finding":190,"direction":58,"opportunity":191},"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":194,"title":195,"url":196,"summary":197,"summary_zh":198,"content":9,"source_name":199,"source_url":196,"published_at":144,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":200,"score_detail":201,"sources":203,"tags":205,"search_phrases":208,"slug":211,"view_count":21,"doi":212,"paper":213,"created_at":232},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":170,"substance":76,"depth":77,"authority":117,"freshness":13,"relevant":21,"comment":202},"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。",[204],{"name":199,"url":196},[26,27,28,206,207],"目标检测","辣椒",[209,210],"农业人工智能 智慧农业 目标检测 设施农业","农业人工智能 智慧农业","农业人工智能智慧农业目标检测设施农业-2638","10.3389\u002Ffpls.2026.1935123",{"doi":212,"openalex_id":214,"authors":215,"venue":199,"cited_by_count":35,"oa_url":196,"card":226,"direction":231,"ingested_from":60},"W7213351973",[216,218,220,223],{"name":217,"orcid":9},"Cui-Ping Zhang",{"name":219,"orcid":9},"Zhi-Yong Wang",{"name":221,"orcid":222},"Xuewei Wang","https:\u002F\u002Forcid.org\u002F0000-0001-9604-3045",{"name":224,"orcid":225},"Zhi Li","https:\u002F\u002Forcid.org\u002F0000-0001-5571-0518",{"tldr":227,"method":228,"finding":229,"direction":58,"opportunity":230},"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。","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","2026-09-16T23:30:09.868848Z",{"id":234,"title":235,"url":236,"summary":237,"summary_zh":238,"content":9,"source_name":239,"source_url":236,"published_at":240,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":168,"score_detail":241,"sources":243,"tags":245,"search_phrases":248,"slug":251,"view_count":35,"doi":252,"paper":253,"created_at":280},2289,"High-throughput phenotyping platform for facility crops based on optical sensing technology: A review","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102545","High-throughput acquisition of crop phenotypic information is one of the key technologies for achieving intelligent facility agriculture and precision breeding. Traditional phenotypic data collection methods suffer from low efficiency and strong subjectivity, making it difficult to achieve multi-scale continuous monitoring and meet the demands of modern research and production. This paper systematically reviews the technological framework and development trajectory of optical sensing technology-driven phenotypic platforms for facility crops. First, starting from optical sensing technologies, a comparative analysis highlights the advantages and limitations of RGB, multi-\u002Fhyperspectral, thermal infrared, and LiDAR sensors in phenotypic perception. Second, the characteristics and applicable scenarios of stationary, rail-mounted, mobile robot, and unmanned aerial vehicle (UAV) platform architectures are summarized. Furthermore, the evolution of phenotypic data processing methods is examined, focusing on the shift from traditional feature engineering to deep learning-driven approaches. Finally, key challenges such as multimodal data fusion, system cost, and real-time performance are discussed, along with the future direction of phenotypic platforms toward intelligent closed-loop decision-making systems. This article systematically reviews the facility agriculture phenotyping platforms driven by optical sensing technology, and also incorporates representative research progress in field phenotyping studies. These advances provide transferable sensing technologies, methodological frameworks, and platform design concepts that can facilitate the development of phenotyping platforms for controlled-environment agriculture.","高通量获取作物表型信息是实现智能设施农业和精准育种的关键技术之一。传统表型数据采集方式效率低、主观性强，难以实现多尺度连续监测，无法满足现代研究与生产需求。本文系统综述了光学传感技术驱动的设施作物表型平台的技术框架与发展脉络。首先，从光学传感技术出发，对比分析了RGB、多光谱\u002F高光谱、热红外和激光雷达传感器在表型感知中的优势与局限。其次，总结了固定式、轨道式、移动机器人和无人机平台架构的特点与适用场景。进而，梳理了表型数据处理方法的演进，重点分析了从传统特征工程到深度学习驱动方法的转变。最后，讨论了多模态数据融合、系统成本和实时性等关键挑战，并展望了表型平台向智能闭环决策系统发展的未来方向。本文系统综述了光学传感技术驱动的设施农业表型平台，同时纳入了田间表型研究中的代表性进展。这些进展提供了可迁移的传感技术、方法框架和平台设计理念，有助于推动受控环境农业表型平台的发展。","Smart Agricultural Technology","2026-09-10T00:00:00Z",{"impact":17,"substance":148,"depth":77,"authority":117,"freshness":63,"relevant":21,"comment":242},"系统综述光学传感驱动的设施作物高通量表型平台，涵盖传感器对比、平台架构与深度学习数据处理演进，方法框架清晰、可迁移性强，对智慧设施农业与精准育种有参考价值。",[244],{"name":239,"url":236},[26,27,28,246,247],"高通量表型","光学传感",[249,250],"农业人工智能 高通量表型 光学传感 智慧农业","农业人工智能 高通量表型","农业人工智能高通量表型光学传感智慧农业-2289","10.1016\u002Fj.atech.2026.102545",{"doi":252,"openalex_id":254,"authors":255,"venue":239,"cited_by_count":35,"oa_url":273,"card":274,"direction":278,"ingested_from":60},"W7212130452",[256,258,260,262,264,266,269,271],{"name":257,"orcid":9},"Xiaodong Zhang",{"name":259,"orcid":9},"Zhaowei Li",{"name":261,"orcid":9},"Yi Zhang",{"name":263,"orcid":9},"Chuandong Guo",{"name":265,"orcid":9},"Zonghua Leng",{"name":267,"orcid":268},"Xiangyu Han","https:\u002F\u002Forcid.org\u002F0000-0003-0412-9859",{"name":270,"orcid":9},"Hanping Mao",{"name":272,"orcid":9},"Yixue Zhang","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2772375526007707\u002Fpdf",{"tldr":275,"method":276,"finding":277,"direction":278,"opportunity":279},"综述光学传感驱动的设施作物高通量表型平台，涵盖传感器、平台架构与数据处理。","综述RGB、多\u002F高光谱、热红外、LiDAR及固定\u002F轨道\u002F机器人\u002F无人机平台与深度","表型平台正从传统特征工程转向深度学习，并迈向智能闭环决策系统。","农业遥感与作物表型","多模态数据融合、低成本实时表型平台及闭环决策在设施农业中仍待突破。","2026-09-13T23:30:04.259059Z"]