[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3751":3,"related-3751":38},{"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":23,"tags":25,"search_phrases":32,"slug":35,"view_count":36,"doi":8,"paper":8,"created_at":37},3751,"设施技术国产化让每公斤番茄综合生产成本降至5元","https:\u002F\u002Fnync.yn.gov.cn\u002Fhtml\u002F2026\u002Fyunnongkuanxun-new_0923\u002F1428950.html","云南首个AI+农业全链路示范基地位于昆明市宜良县匡远街道温泉社区。嘉措（云南）农业科技有限公司联合云南指月科技有限公司打造国产化AI种植模式，通过核心算法本地化适配与全链条国产化硬件深度协同，基地里每公斤番茄综合生产成本降至5元，仅为海外高端温室模式的三分之一。",null,"位于昆明市宜良县匡远街道温泉社区的番茄示范基地即将在国庆假期进入采收期。记者近日来到该基地，探访这座云南首个“AI+农业全链路”示范基地。\n\n清晨，基地里一串串番茄在简易薄膜温室内泛着诱人的青红光泽。没有泥泞的土地，没有汗流浃背的弯腰劳作，眼前这座“番茄工厂”安静得只听见蜜蜂授粉时的嗡鸣声和水肥滴灌的微响。\n\n上午8时，有工人陆续进场，偌大的基地只需三五位工人就能打理完毕。物联网感知终端静静矗立，实时采集光照强度、空气温湿度、水肥浓度和作物长势。种苗定植、枝蔓生长、开花坐果到成熟采收，每一个环节都有“数字管家”全程值守。嘉措（云南）农业科技有限公司农场场长李良坐在棚外，掏出手机轻点屏幕，棚内温湿度曲线、土壤含水量、水肥配比参数一目了然。指尖滑动间，灌溉阀自动开启，水肥顺着滴灌带精准送达番茄根部。\n\n“过去种番茄靠经验，现在靠算法。”云南指月科技有限公司项目经理尹文鑫指着棚内的国产化智慧灌溉设备说。这套系统由嘉措（云南）农业科技有限公司联合云南指月科技有限公司打造，创新构建“全域监测+AI算法+智慧灌溉”三位一体的数字化种植模式。\n\n“基地里的全套设备都是国产的。”尹文鑫提高音量信心十足地介绍。长期以来，国内高端设施农业赛道被海外技术路线主导。参照国外植物工厂及高端玻璃温室模式，依托全套进口水肥设备与闭环控制系统虽能实现稳定高产，但每公斤番茄综合生产成本高达13元至15元，加之进口设备造价高、配件周期长、操作门槛高等痛点，技术落地难度极大。而该基地探索的国产化AI种植模式，正在彻底改写这一成本结构。\n\n国产化的意义，最终落在基地每一颗番茄的身价上。通过核心算法本地化适配与全链条国产化硬件深度协同，基地里每公斤番茄综合生产成本降至5元，仅为海外高端温室模式的三分之一。同时，国产化硬件的使用，进一步压缩了采购、部署与运维成本，凸显整套方案规模化落地的性价比。\n\n“云”上管棚，“数”里种田。从传感器、灌溉阀到核心控制系统，基地核心硬件已实现全部国产配套；从数据采集、模型训练到智能决策输出，AI技术真正扎根田间。对种植户而言，AI不再是遥远的科技概念，而是手机里能调水肥、棚里能提产量、年底能多增收的实用“新农具”。（记者：王淑娟）","云南省农业农村厅","2026-09-23T12:00:00Z","报道",10,false,78,{"impact":17,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},22,17,11,6,1,"云南首个AI+农业全链路示范基地，以国产化硬件与算法将番茄综合成本从13-15元\u002F公斤降至5元，数据具体、多方信源，具备行业示范价值。",[24],{"name":10,"url":6},[26,27,28,29,30,31],"智慧农业","农业人工智能","设施农业","云南","番茄","国产化替代",[33,34],"云南 宜良 AI番茄 示范基地","嘉措农业 指月科技 智慧灌溉","云南宜良AI番茄示范基地-3751",0,"2026-09-29T00:08:25.735852Z",{"total":20,"page":21,"page_size":20,"items":39},[40,72,118,164,206,256],{"id":41,"title":42,"url":43,"summary":44,"summary_zh":8,"content":8,"source_name":45,"source_url":8,"published_at":46,"category":47,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":48,"sources":54,"tags":56,"search_phrases":58,"slug":61,"view_count":36,"doi":8,"paper":62,"created_at":71},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","论文",{"impact":49,"substance":17,"depth":50,"authority":51,"freshness":52,"relevant":21,"comment":53},16,18,13,9,"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。",[55],{"name":45,"url":43},[26,27,28,30,57],"病害识别",[59,60],"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":8,"openalex_id":8,"authors":63,"venue":8,"cited_by_count":36,"oa_url":8,"card":64,"direction":68,"ingested_from":70},[],{"tldr":65,"method":66,"finding":67,"direction":68,"opportunity":69},"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%。","农业人工智能与决策模型","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":73,"title":74,"url":75,"summary":76,"summary_zh":77,"content":8,"source_name":78,"source_url":75,"published_at":79,"category":47,"cover_url":8,"hotness":13,"is_selected":14,"score":80,"score_detail":81,"sources":84,"tags":86,"search_phrases":89,"slug":92,"view_count":36,"doi":93,"paper":94,"created_at":117},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。","Biosystems Engineering","2026-09-26T00:00:00Z",81,{"impact":50,"substance":17,"depth":50,"authority":82,"freshness":52,"relevant":21,"comment":83},14,"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。",[85],{"name":78,"url":75},[26,27,28,87,88],"黄瓜育苗","光环境调控",[90,91],"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","10.1016\u002Fj.biosystemseng.2026.104601",{"doi":93,"openalex_id":95,"authors":96,"venue":78,"cited_by_count":36,"oa_url":75,"card":111,"direction":68,"ingested_from":116},"W7214421951",[97,100,103,105,108],{"name":98,"orcid":99},"Pan Gao","https:\u002F\u002Forcid.org\u002F0000-0002-5184-5674",{"name":101,"orcid":102},"Huimin Li","https:\u002F\u002Forcid.org\u002F0009-0001-9124-7628",{"name":104,"orcid":8},"Jinghua Xu",{"name":106,"orcid":107},"Miao Lu","https:\u002F\u002Forcid.org\u002F0000-0001-6539-2170",{"name":109,"orcid":110},"Jin Ping Hu","https:\u002F\u002Forcid.org\u002F0000-0001-5532-6890",{"tldr":112,"method":113,"finding":114,"direction":68,"opportunity":115},"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%。","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",{"id":119,"title":120,"url":121,"summary":122,"summary_zh":123,"content":8,"source_name":124,"source_url":121,"published_at":125,"category":47,"cover_url":8,"hotness":13,"is_selected":14,"score":126,"score_detail":127,"sources":130,"tags":132,"search_phrases":135,"slug":138,"view_count":36,"doi":139,"paper":140,"created_at":163},3356,"Simultaneous Maturity Recognition and Yield Counting of Truss and Individual Tomatoes Using Improved YOLOv8-EME and Optimized ByteTrack Tracker","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fhorticulturae12101199","Crop maturity detection and yield estimation are critical components of protected agriculture, supporting optimized harvest timing, fruit quality control, and coordination of production and marketing. Existing studies on detection and counting predominantly address a single category—either truss or individual fruits. To advance automation and intelligence in production management, this study proposes a tomato detection and counting system that integrates an improved YOLOv8-EME model with the ByteTrack algorithm, enabling simultaneous detection and counting of truss and individual fruits with maturity classification. The improved YOLOv8-EME model combines the EfficientNet architecture with the EffectiveSE attention mechanism, improving feature extraction and computational efficiency. In addition, the optimized network structure yields a lightweight design, reducing FLOPs to 6.9 G. The model attains a mean Average Precision (mAP) of 0.942, 0.883, 0.850, and 0.956 for the Ripe, Raw, Medium-Raw, and Truss categories, respectively. A proposed cross-line counting method integrated with an improved ByteTrack algorithm mitigates target loss, ID drift, and duplicate counting through ID drift association, trajectory fusion, historical trajectory cues, and a cooldown scheme. These designs significantly improve detection and counting accuracy for truss and fruit maturity, achieving a counting accuracy of 94%. The system offers efficient and accurate technical support for tomato detection and counting in smart agriculture.","作物成熟度检测与产量估测是设施农业的关键环节，可为优化采收时机、果实品质控制及产销协调提供支撑。现有检测与计数研究大多仅针对单一类别，即串收番茄或单个果实。为推动生产管理的自动化与智能化，本研究提出了一种融合改进YOLOv8-EME模型与ByteTrack算法的番茄检测与计数系统，可实现串收番茄与单个果实的同步检测与计数，并进行成熟度分类。改进后的YOLOv8-EME模型将EfficientNet架构与EffectiveSE注意力机制相结合，提升了特征提取能力与计算效率。此外，优化后的网络结构实现了轻量化设计，浮点运算次数（FLOPs）降至6.9 G。该模型在成熟、未成熟、半熟和串收四个类别上的平均精度均值（mAP）分别为0.942、0.883、0.850和0.956。所提出的跨线计数方法与改进的ByteTrack算法相结合，通过ID漂移关联、轨迹融合、历史轨迹线索和冷却机制，缓解了目标丢失、ID漂移和重复计数问题。这些设计显著提升了串收番茄和果实成熟度的检测与计数精度，计数准确率达到94%。该系统为智慧农业中的番茄检测与计数提供了高效、准确的技术支持。","Horticulturae","2026-09-23T00:00:00Z",77,{"impact":128,"substance":17,"depth":50,"authority":51,"freshness":52,"relevant":21,"comment":129},15,"该研究提出改进YOLOv8-EME与优化ByteTrack的番茄检测计数系统，可同时识别串收与单果成熟度并计数，方法新颖、数据详实，对智慧农业采摘自动化有实用价值。",[131],{"name":124,"url":121},[26,27,133,30,134],"目标检测","产量估测",[136,137],"YOLOv8 番茄 成熟度检测","番茄 串收 产量计数","YOLOv8番茄成熟度检测-3356","10.3390\u002Fhorticulturae12101199",{"doi":139,"openalex_id":141,"authors":142,"venue":124,"cited_by_count":36,"oa_url":121,"card":157,"direction":162,"ingested_from":116},"W7214123905",[143,145,148,150,152,154],{"name":144,"orcid":8},"Liying Shi",{"name":146,"orcid":147},"Sen Lin","https:\u002F\u002Forcid.org\u002F0000-0001-6521-3152",{"name":149,"orcid":8},"Haihang Zhao",{"name":151,"orcid":8},"Tianlong Sun",{"name":153,"orcid":8},"Dongdong Sun",{"name":155,"orcid":156},"Yuchen Yang","https:\u002F\u002Forcid.org\u002F0000-0001-5977-1617",{"tldr":158,"method":159,"finding":160,"direction":68,"opportunity":161},"提出改进YOLOv8-EME与ByteTrack结合的番茄检测计数系统，可同时识别串收与单果成熟度并","改进YOLOv8-EME（EfficientNet+EffectiveSE）结合","模型mAP达0.942\u002F0.883\u002F0.850\u002F0.956，计数准确率94%，FLOPs仅6.9G。","可探索多作物、多生长阶段通用模型，并融合边缘部署与产量预测决策。","智慧农业 \u002F 农业物联网","2026-09-24T23:30:10.615639Z",{"id":165,"title":166,"url":167,"summary":168,"summary_zh":169,"content":8,"source_name":170,"source_url":167,"published_at":171,"category":47,"cover_url":8,"hotness":13,"is_selected":14,"score":126,"score_detail":172,"sources":175,"tags":177,"search_phrases":180,"slug":183,"view_count":36,"doi":184,"paper":185,"created_at":205},2774,"SpatioFormer: spatial perception enhancement for lightweight agricultural pest and disease detection","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1925867","Introduction In precision agriculture, accurate and efficient detection of crop pests and diseases is crucial. However, existing models in complex environments are prone to insufficient spatial perception and attenuation of disease texture features, making it difficult to balance recognition accuracy and lightweighting. Methods To address this, this study proposes a lightweight spatial perception enhancement hybrid architecture, SpatioFormer. First, a Pixel-level Detail Retrieval (PDR) mechanism is designed. This mechanism leverages cross-layer dynamic routing to facilitate the fusion of deep semantic features with shallow texture features, significantly enhancing the capability to capture disease features. Second, we design a Spatially Adaptive Modulation Attention (SA-SHMA) mechanism, which utilizes large-kernel depthwise convolution to capture contextual information and combines dynamic modulation maps for fine-grained focusing, efficiently recovering spatial details, and suppressing background noise. Furthermore, this paper introduces a Context-Guided Asymmetric Gated Linear Unit (CGA-GLU), which utilizes an asymmetric design focusing on the gating branch and incorporates contextual information for guidance, enhancing the inter-channel representation capability with minimal computational overhead. Results Finally, extensive experiments on the PDDD and Tomato-Village datasets validated the effectiveness of the proposed model. The proposed model achieves a Top-1 accuracy of 81.05% on the PDDD dataset and an AP 50 of 61.53% on the Tomato-Village dataset, with testing latency on edge devices being highly competitive among existing models. Discussion Compared to existing lightweight hybrid models, SpatioFormer effectively recovers shallow spatial details and precisely suppresses complex background noise under an extremely low parameter budget. Consequently, it achieves a superior balance between practical disease localization capability and inference latency on resource-constrained edge devices.","引言 在精准农业中，准确高效地检测作物病虫害至关重要。然而，复杂环境下的现有模型容易出现空间感知不足和病害纹理特征衰减的问题，难以兼顾识别精度与轻量化。方法 为解决这一问题，本研究提出了一种轻量级空间感知增强混合架构——SpatioFormer。首先，设计了像素级细节检索（Pixel-level Detail Retrieval，PDR）机制。该机制利用跨层动态路由，促进深层语义特征与浅层纹理特征的融合，显著增强了对病害特征的捕捉能力。其次，设计了空间自适应调制注意力（Spatially Adaptive Modulation Attention，SA-SHMA）机制，该机制利用大核深度卷积捕获上下文信息，并结合动态调制图进行细粒度聚焦，高效恢复空间细节并抑制背景噪声。此外，本文引入了上下文引导非对称门控线性单元（Context-Guided Asymmetric Gated Linear Unit，CGA-GLU），其采用聚焦门控分支的非对称设计，并融入上下文信息进行引导，以极小的计算开销增强了通道间表征能力。结果 最后，在PDDD和Tomato-Village数据集上的大量实验验证了所提模型的有效性。所提模型在PDDD数据集上取得了81.05%的Top-1准确率，在Tomato-Village数据集上取得了61.53%的AP 50，其在边缘设备上的测试延迟在现有模型中极具竞争力。讨论 与现有轻量级混合模型相比，SpatioFormer在极低的参数预算下有效恢复了浅层空间细节，并精确抑制了复杂背景噪声。因此，它在实际病害定位能力与资源受限边缘设备上的推理延迟之间实现了更优的平衡。","Frontiers in Plant Science","2026-09-16T00:00:00Z",{"impact":49,"substance":173,"depth":50,"authority":51,"freshness":52,"relevant":21,"comment":174},21,"提出轻量化空间感知增强架构，在边缘设备上兼顾检测精度与推理延迟，方法新颖、实验扎实，对农业病虫害智能识别有参考价值。",[176],{"name":170,"url":167},[26,27,178,30,179],"边缘计算","病虫害检测",[181,182],"农业人工智能 病虫害检测 智慧农业 边缘计算","农业人工智能 病虫害检测","农业人工智能病虫害检测智慧农业边缘计算-2774","10.3389\u002Ffpls.2026.1925867",{"doi":184,"openalex_id":186,"authors":187,"venue":170,"cited_by_count":36,"oa_url":199,"card":200,"direction":162,"ingested_from":116},"W7213437661",[188,190,193,195,197],{"name":189,"orcid":8},"Wenbo Ma",{"name":191,"orcid":192},"Hao Sun","https:\u002F\u002Forcid.org\u002F0000-0002-6983-8149",{"name":194,"orcid":8},"Kun Zhou",{"name":196,"orcid":8},"Meichun Wang",{"name":198,"orcid":8},"Rui Fu","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1925867\u002Fpdf",{"tldr":201,"method":202,"finding":203,"direction":68,"opportunity":204},"提出轻量混合架构SpatioFormer，提升复杂环境下农作物病虫害检测的空间感知能力。","设计PDR跨层动态路由、SA-SHMA大核注意力与CGA-GLU门控，在PDDD","在极低参数量下恢复浅层空间细节并抑制背景噪声，边缘设备延迟具竞争力。","可探索将空间感知增强机制迁移至多作物多病害场景，并研究边缘端实时部署的能效优化。","2026-09-17T23:30:14.148727Z",{"id":207,"title":208,"url":209,"summary":210,"summary_zh":211,"content":8,"source_name":212,"source_url":209,"published_at":171,"category":47,"cover_url":8,"hotness":13,"is_selected":14,"score":213,"score_detail":214,"sources":218,"tags":220,"search_phrases":222,"slug":225,"view_count":36,"doi":226,"paper":227,"created_at":255},2741,"A tomato maturity detection method against occlusion and variable illumination","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112423","A tomato maturity detection method against occlusion and variable illumination。Computers and Electronics in Agriculture","一种抗遮挡和可变光照的番茄成熟度检测方法","Computers and Electronics in Agriculture",68,{"impact":215,"substance":50,"depth":49,"authority":82,"freshness":216,"relevant":21,"comment":217},12,8,"核心期刊论文，针对遮挡与光照变化下的番茄成熟度检测提出新方法，属农业人工智能细分领域实质进展，但应用范围有限，未达重大突破层级。",[219],{"name":212,"url":209},[26,27,133,30,221],"作物表型",[223,224],"农业人工智能 作物表型 智慧农业 目标检测","农业人工智能 作物表型","农业人工智能作物表型智慧农业目标检测-2741","10.1016\u002Fj.compag.2026.112423",{"doi":226,"openalex_id":228,"authors":229,"venue":212,"cited_by_count":36,"oa_url":8,"card":250,"direction":68,"ingested_from":116},"W7213429689",[230,233,236,239,242,245,247],{"name":231,"orcid":232},"Hao Meng","https:\u002F\u002Forcid.org\u002F0000-0001-7511-2910",{"name":234,"orcid":235},"Wenzhe Li","https:\u002F\u002Forcid.org\u002F0009-0008-1630-4697",{"name":237,"orcid":238},"Di Wang","https:\u002F\u002Forcid.org\u002F0000-0002-3911-8159",{"name":240,"orcid":241},"Hui Zhao","https:\u002F\u002Forcid.org\u002F0009-0005-4192-5776",{"name":243,"orcid":244},"Ximing Li","https:\u002F\u002Forcid.org\u002F0000-0003-4022-1273",{"name":246,"orcid":8},"Dongdong Cui",{"name":248,"orcid":249},"Fernando Auat Cheein","https:\u002F\u002Forcid.org\u002F0000-0002-6347-7696",{"tldr":251,"method":252,"finding":253,"direction":68,"opportunity":254},"提出一种抗遮挡和光照变化的番茄成熟度检测方法。","基于深度学习的图像检测，针对遮挡与光照变化优化。","该方法在遮挡和变光照下仍能准确检测番茄成熟度。","可探索多模态融合与轻量化部署，提升田间复杂场景实时检测鲁棒性。","2026-09-17T23:30:01.491379Z",{"id":257,"title":258,"url":259,"summary":260,"summary_zh":8,"content":261,"source_name":262,"source_url":8,"published_at":171,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":263,"sources":267,"tags":269,"search_phrases":272,"slug":275,"view_count":21,"doi":8,"paper":8,"created_at":276},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",{"impact":264,"substance":265,"depth":18,"authority":216,"freshness":52,"relevant":21,"comment":266},24,20,"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。",[268],{"name":262,"url":259},[26,27,28,270,271],"神农大模型","智能育种",[273,274],"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"]