[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1596":3,"related-1596":130},{"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":38,"search_phrases":89,"slug":92,"view_count":68,"doi":93,"paper":94,"created_at":129},1596,"AgriPINN: A Process-Informed Neural Network for Interpretable and Scalable Crop Biomass Prediction under Water Stress","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102539","Accurate estimation of crop above-ground dry biomass (AGB) under water stress is important for assessing agricultural production, managing irrigation, and supporting water-security-oriented decision-making. Data-driven models enable efficient large-scale prediction but often provide limited physiological interpretation and can be sensitive to domain shifts, whereas process-based crop models require extensive calibration and detailed management inputs. We propose AgriPINN, a process-informed neural network that embeds a differentiable biomass-growth equation derived from LINTUL5 into neural-network training. AGB is the sole supervised target. The network simultaneously estimates leaf area index (LAI), photosynthetically active radiation (PAR), radiation-use efficiency (RUE), and the water-stress factor ( F W ); their predicted values enter the process residual, whereas the corresponding field measurements are held out from optimisation and used only for post hoc evaluation. AgriPINN is pretrained on 65 years of SIMPLACE simulations covering 397 German NUTS-3 regions and fine-tuned on three years of wheat and maize field experiments under sheltered, rainfed, and irrigated treatments. On the main benchmark, AgriPINN reduces RMSE by 29.7% for winter wheat and 22.8% for maize relative to CNN–Transformer. Relative to the strongest non-AgriPINN baseline for each crop, the corresponding reductions are 9.0% and 2.2%. Additional evaluations include four representative neural backbones, sensitivity analysis of the process-loss weight, an ablation without pretraining, and perturbation tests on simulated labels. The transferability demonstrated in this study is limited to the independent datasets and the German temperate agro-ecological setting evaluated here.","水分胁迫下作物地上部干生物量（AGB）的准确估算对于评估农业生产、管理灌溉以及支持以水安全为导向的决策至关重要。数据驱动模型能够实现高效的大尺度预测，但往往提供的生理学解释有限，且可能对领域偏移敏感；而基于过程的作物模型则需要大量校准和详细的管理输入。我们提出AgriPINN，一种过程信息神经网络，将源自LINTUL5的可微生物量增长方程嵌入神经网络训练中。AGB是唯一的监督目标。该网络同时估算叶面积指数（LAI）、光合有效辐射（PAR）、辐射利用效率（RUE）以及水分胁迫因子（F W ）；其预测值进入过程残差，而相应的田间实测值则从优化中排除，仅用于事后评估。AgriPINN在覆盖德国397个NUTS-3区域的65年SIMPLACE模拟数据上进行了预训练，并在三年遮雨、雨养和灌溉处理下的小麦和玉米田间试验数据上进行了微调。在主要基准测试中，相对于CNN–Transformer，AgriPINN使冬小麦的RMSE降低了29.7%，玉米降低了22.8%。相对于每种作物最强的非AgriPINN基线，相应的降幅分别为9.0%和2.2%。额外评估包括四种代表性神经骨干网络、过程损失权重的敏感性分析、无预训练的消融实验以及模拟标签的扰动测试。本研究所展示的可迁移性仅限于所评估的独立数据集和德国温带农业生态情境。",null,"Smart Agricultural Technology","2026-09-01T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,22,19,12,4,1,"提出过程信息神经网络AgriPINN，结合机理模型与深度学习，显著提升水分胁迫下作物生物量预测精度，方法新颖且数据扎实。",[25],{"name":10,"url":6},[27,28,29,30],"智慧农业","农业人工智能","作物模型","遥感估产",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51,59,66,73,79,84],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":50},9,true,[],[34,49,33],"winter 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Han","https:\u002F\u002Forcid.org\u002F0000-0003-2491-7473",0,{"tldr":123,"method":124,"finding":125,"direction":126,"opportunity":127},"提出过程信息神经网络AgriPINN，嵌入作物生长方程，提升水分胁迫下生物量预测精度与可解释性。","将LINTUL5生长方程嵌入神经网络训练，预训练于SIMPLACE模拟数据，微调","相比CNN-Transformer，冬小麦RMSE降低29.7%，玉米降低22.8%，且具有生理可解","农业人工智能与决策模型","可探索将过程信息神经网络扩展到其他作物、气候区或胁迫类型，并验证其跨域泛化能力。","openalex","2026-09-04T23:30:04.364676Z",{"total":53,"page":22,"page_size":53,"items":131},[132,196,269,321,369,414],{"id":133,"title":134,"url":135,"summary":136,"summary_zh":9,"content":9,"source_name":137,"source_url":9,"published_at":138,"category":12,"cover_url":9,"hotness":139,"is_selected":14,"score":140,"score_detail":141,"sources":145,"tags":150,"primary_topic":153,"secondary_topics":154,"classification":155,"search_phrases":182,"slug":185,"view_count":75,"doi":9,"paper":186,"created_at":195},2998,"物候阶段渐进式多源数据融合的县域冬小麦产量预测","https:\u002F\u002Ffinance.sina.com.cn\u002Froll\u002F2026-09-18\u002Fdoc-inisheny8322115.shtml","西安财经大学王毅等联合江苏大学张立元副教授、西安理工大学西北旱区生态水利国家重点实验室、中国科学院重庆绿色智能技术研究院团队，以河南省100个冬小麦主产县为研究区融合2013—2022年遥感变量、气象变量和日光诱导叶绿素荧光光合变量，构建覆盖冬小麦分蘖期至成熟期的多源时序特征集。提出物候阶段渐进式多源数据融合方法明确不同物候阶段信息累积对县域冬小麦估产性能的影响。","智慧农业(中英文)2026,8(4):70-84","2026-09-18T00:00:00Z",25,80,{"impact":17,"substance":18,"depth":17,"authority":142,"freshness":143,"relevant":22,"comment":144},14,8,"以河南100个主产县2013—2022年遥感、气象与SIF数据构建物候阶段渐进式融合估产方法，数据规模与方法新颖性突出，对县域粮食产量预测有参考价值。",[146,147],{"name":137,"url":135},{"name":148,"url":149},"《智慧农业（中英文）》2026年第4期","https:\u002F\u002Ffinance.sina.cn\u002F2026-09-18\u002Fdetail-inisheny8322115.d.html",[27,28,30,151,152],"冬小麦","多源数据融合",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":156},{"candidates":157,"agricultural_evidence":46},[158,162,169,173,177],{"code":32,"score":20,"supported":46,"title":159,"abstract":160,"tags":161},[34],[34],[151],{"code":85,"score":163,"supported":46,"title":164,"abstract":165,"tags":168},7,[],[166,167],"遥感","估产",[30],{"code":80,"score":22,"supported":14,"title":170,"abstract":171,"tags":172},[],[],[27],{"code":74,"score":22,"supported":14,"title":174,"abstract":175,"tags":176},[],[],[28],{"code":178,"score":22,"supported":14,"title":179,"abstract":180,"tags":181},"I5",[],[],[152],[183,184],"河南 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wheat","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104983","Synergizing process-based modeling and data-driven learning for precision nitrogen optimization in winter wheat。Agricultural Systems","Agricultural Systems","2026-09-16T00:00:00Z",79,{"impact":17,"substance":205,"depth":206,"authority":142,"freshness":13,"relevant":22,"comment":207},20,17,"将过程模型与数据驱动学习融合用于冬小麦精准氮肥优化，方法新颖、发表于核心期刊且时效性强，具备进入每日精选的价值。",[209],{"name":201,"url":199},[27,28,211,29,151],"精准施肥",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":215},{"candidates":216,"agricultural_evidence":46},[217,222,226,231],{"code":32,"score":218,"supported":46,"title":219,"abstract":220,"tags":221},23,[49,33],[49,33],[151],{"code":74,"score":75,"supported":14,"title":223,"abstract":224,"tags":225},[],[],[29,28],{"code":227,"score":22,"supported":14,"title":228,"abstract":229,"tags":230},"F3",[],[],[211],{"code":80,"score":22,"supported":14,"title":232,"abstract":233,"tags":234},[],[],[27],[236,91],"农业人工智能 作物模型 智慧农业 精准施肥","农业人工智能作物模型智慧农业精准施肥-2622","10.1016\u002Fj.agsy.2026.104983",{"doi":238,"openalex_id":240,"authors":241,"venue":201,"cited_by_count":121,"oa_url":199,"card":9,"direction":9,"ingested_from":128},"W7213352238",[242,244,247,250,252,254,256,258,260,262,264,266],{"name":243,"orcid":9},"Yuru Ye",{"name":245,"orcid":246},"Qian Wang","https:\u002F\u002Forcid.org\u002F0000-0003-0750-7843",{"name":248,"orcid":249},"Davide Cammarano","https:\u002F\u002Forcid.org\u002F0000-0003-0918-550X",{"name":251,"orcid":9},"Kang Yu",{"name":253,"orcid":9},"Siva K. Balasundram",{"name":255,"orcid":9},"Wei Li",{"name":257,"orcid":9},"Xiuli Li",{"name":259,"orcid":9},"Xiaojun Liu",{"name":261,"orcid":9},"Yongchao Tian",{"name":263,"orcid":9},"Yan Zhu",{"name":265,"orcid":9},"Weixing Cao",{"name":267,"orcid":9},"Qiang Cao","2026-09-16T23:30:05.340754Z",{"id":270,"title":271,"url":272,"summary":273,"summary_zh":9,"content":9,"source_name":274,"source_url":9,"published_at":275,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":276,"score_detail":277,"sources":280,"tags":282,"primary_topic":286,"secondary_topics":287,"classification":288,"search_phrases":309,"slug":312,"view_count":121,"doi":9,"paper":313,"created_at":320},5365,"基于经济型多光谱成像与知识蒸馏模型压缩的小麦线条花叶病毒早期检测","https:\u002F\u002Fwww.ebiotrade.com\u002Fnewsf\u002F2026-10\u002F20261017000601916.htm","研究人员利用配备六个光谱滤光片的消费级数码单反(DSLR)相机在自然环境条件下获取了一个新的小麦线条花叶病毒(WhSMV)多光谱数据集。评估十二个深度学习模型识别 WhSMV 感染不同阶段的性能,并首次将模型压缩技术应用于多光谱图像用于 WhSMV 感染的早期检测。比较了深度学习算法与压缩模型的性能,揭示压缩模型达到与大型深度学习模型相当的性能。WhSMV 是小麦土传媒介传播病毒,巴西与南非均有报道。","Smart Agricultural Technology 2026-10-17","2026-10-17T00:00:00Z",78,{"impact":17,"substance":18,"depth":206,"authority":278,"freshness":143,"relevant":22,"comment":279},13,"该研究构建了新的多光谱数据集，并首次将模型压缩用于小麦病毒早期检测，方法新颖、结论可靠，对智慧农业植保具有参考价值。",[281],{"name":274,"url":272},[27,28,283,284,285],"多光谱成像","小麦病害","模型压缩",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":289},{"candidates":290,"agricultural_evidence":46},[291,296,301,305],{"code":32,"score":292,"supported":46,"title":293,"abstract":294,"tags":295},11,[34],[34],[],{"code":74,"score":21,"supported":46,"title":297,"abstract":298,"tags":300},[],[299],"深度学习",[28],{"code":80,"score":22,"supported":14,"title":302,"abstract":303,"tags":304},[],[],[27],{"code":85,"score":22,"supported":14,"title":306,"abstract":307,"tags":308},[],[],[283],[310,311],"小麦线条花叶病毒 多光谱 检测","知识蒸馏 模型压缩 小麦","小麦线条花叶病毒多光谱检测-5365",{"doi":9,"openalex_id":9,"authors":314,"venue":9,"cited_by_count":121,"oa_url":9,"card":315,"direction":192,"ingested_from":194},[],{"tldr":316,"method":317,"finding":318,"direction":192,"opportunity":319},"用经济型多光谱成像结合知识蒸馏压缩模型，实现小麦线条花叶病毒早期检测。","消费级单反加六波段滤光片采集多光谱数据，评估12个深度学习模型并做模型压缩。","压缩模型性能与大型深度学习模型相当，可用于WhSMV感染早期识别。","可探索低成本多光谱设备与轻量模型在田间病害早期预警中的部署与跨区域泛化。","2026-10-20T02:47:54.487590Z",{"id":322,"title":323,"url":324,"summary":325,"summary_zh":9,"content":9,"source_name":326,"source_url":9,"published_at":327,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":203,"score_detail":328,"sources":330,"tags":332,"primary_topic":336,"secondary_topics":337,"classification":338,"search_phrases":357,"slug":360,"view_count":121,"doi":9,"paper":361,"created_at":368},5362,"深度语义分割模型的时序泛化评估:无人机小麦倒伏监测 5 年跨年测试","https:\u002F\u002Fbioengineer.org\u002Fai-models-trained-on-one-year-of-wheat-fail-the-next-five-year-drone-study-shows\u002F","Zhang G., Wang X., Wang D., Sun D., Zhang Y., Zhang D. (2026) 利用 5 年田间试验评估深度语义分割模型在无人机(UAV)小麦倒伏监测中的时序泛化能力。研究发现:同年内高精度不能保证跨年可靠性,模型从训练年份迁移到其他年份时性能显著下降,基于注意力的模型通常比全卷积网络更稳健,可能因为层级窗口自注意力能整合更长范围上下文,而卷积强调局部模式;即便最优架构在大时序漂移下也出现严重退化。强调时序泛化测试应成为农业 AI 模型部署前的必备环节。DOI: 10.1016\u002Fj.atech.2026.102631。","Smart Agricultural Technology Vol.15 Article 102631 2026-10-16","2026-10-16T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":278,"freshness":143,"relevant":22,"comment":329},"基于5年跨年田间试验揭示农业AI模型时序泛化瓶颈，结论扎实且具部署指导价值，值得入选。",[331],{"name":326,"url":324},[27,28,333,334,335],"无人机遥感","小麦倒伏","模型泛化",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":339},{"candidates":340,"agricultural_evidence":46},[341,345,349,353],{"code":32,"score":292,"supported":46,"title":342,"abstract":343,"tags":344},[34],[34],[],{"code":80,"score":22,"supported":14,"title":346,"abstract":347,"tags":348},[],[],[27],{"code":74,"score":22,"supported":14,"title":350,"abstract":351,"tags":352},[],[],[28],{"code":85,"score":22,"supported":14,"title":354,"abstract":355,"tags":356},[],[],[333],[358,359],"无人机 小麦倒伏 监测","深度语义分割 时序泛化","无人机小麦倒伏监测-5362",{"doi":9,"openalex_id":9,"authors":362,"venue":9,"cited_by_count":121,"oa_url":9,"card":363,"direction":192,"ingested_from":194},[],{"tldr":364,"method":365,"finding":366,"direction":192,"opportunity":367},"用5年田间试验评估深度语义分割模型在无人机小麦倒伏监测中的跨年泛化能力。","5年无人机小麦倒伏影像，对比注意力模型与全卷积网络的跨年迁移表现。","同年内高精度不能保证跨年可靠，注意力模型比全卷积网络更稳健，但大时序漂移下仍严重退化。","可研究时序域自适应与跨年增量学习，建立农业AI模型部署前的时序泛化评测基准。","2026-10-20T02:47:54.289085Z",{"id":370,"title":371,"url":372,"summary":373,"summary_zh":9,"content":9,"source_name":374,"source_url":9,"published_at":375,"category":376,"cover_url":9,"hotness":13,"is_selected":14,"score":377,"score_detail":378,"sources":381,"tags":383,"primary_topic":387,"secondary_topics":388,"classification":389,"search_phrases":409,"slug":412,"view_count":121,"doi":9,"paper":9,"created_at":413},4859,"机器人变身小麦育种专家——山东农大小麦育种具身机器人原型机公开亮相","https:\u002F\u002Fnews.sciencenet.cn\u002Fsbhtmlnews\u002F2026\u002F10\u002F391582.shtm","近日在山东泰安举行的小麦智能育种研讨会上,小麦育种具身机器人原型机首次公开亮相。该机器人由山东农业大学教授王宏伟、张亮团队联合研发,采用实时动态差分定位与视觉融合导航技术,可在试验田小区间自主移动作业;搭载滑台高清相机,可对田间小麦进行连续影像采集,并利用视觉算法自动量化株高、穗部性状等表型参数。系统结合智能决策模型对田间单株进行高通量初筛,自主识别具有进一步考察价值的候选植株。研究团队与山东极智生物科技有限公司达成合作意向。","中国科学报 科学网 2026 年 10 月 13 日 第 1 版","2026-10-13T00:00:00Z","报道",81,{"impact":18,"substance":205,"depth":379,"authority":278,"freshness":13,"relevant":22,"comment":380},16,"国内小麦育种具身机器人原型机首次公开亮相，技术路线与产学研合作信息具体，具备行业示范价值，值得进入每日精选。",[382],{"name":374,"url":372},[27,28,384,385,386],"种业振兴","小麦育种","具身机器人",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":390},{"candidates":391,"agricultural_evidence":46},[392,396,401,405],{"code":32,"score":20,"supported":46,"title":393,"abstract":394,"tags":395},[34],[34],[385],{"code":397,"score":22,"supported":14,"title":398,"abstract":399,"tags":400},"F1",[],[],[384],{"code":80,"score":22,"supported":14,"title":402,"abstract":403,"tags":404},[],[],[27],{"code":74,"score":22,"supported":14,"title":406,"abstract":407,"tags":408},[],[],[28],[410,411],"山东农业大学 小麦育种 具身机器人","王宏伟 小麦 表型采集","山东农业大学小麦育种具身机器人-4859","2026-10-14T00:04:08.166418Z",{"id":415,"title":416,"url":417,"summary":418,"summary_zh":419,"content":9,"source_name":420,"source_url":417,"published_at":421,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":377,"score_detail":422,"sources":424,"tags":426,"primary_topic":430,"secondary_topics":431,"classification":437,"search_phrases":454,"slug":457,"view_count":22,"doi":458,"paper":459,"created_at":487},4806,"A study on the identification of wheat powdery mildew using chlorophyll fluorescence imaging","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1837843","Introduction Wheat powdery mildew poses a serious threat to global wheat production, and its accurate identification is key to smart agriculture. Methods This study combines chlorophyll fluorescence imaging with deep learning algorithms to investigate the temporal dynamics of key fluorescence parameters (such as maximum photochemical efficiency, QYmax) during the progression of powderymildew. A QYmax-based chlorophyll fluorescence image dataset for wheat powdery mildew was constructed. Building on this, the LCRNet model was proposed, which effectively processes fluorescence image data through a collaborative mechanism integrating the Large Selective Adaptive Fusion (LSAF) and Coordinated Attention (CA) modules. Results On an independent test set, the model achieved an accuracy of 94% and an F1 score of 94.1%, outperforming comparison models. Discussion This study proposes a method for classifying the severity of powdery mildew in wheat that reduces the computational cost of fluorescence data processing, providing important methodological support and technical insights for advancing the intelligent, precise, and green transformation of crop disease prevention and control systems.","引言 小麦白粉病对全球小麦生产构成严重威胁，其准确识别是实现智慧农业的关键。方法 本研究将叶绿素荧光成像与深度学习算法相结合，探究白粉病发展过程中关键荧光参数（如最大光化学效率QYmax）的时间动态变化，构建了基于QYmax的小麦白粉病叶绿素荧光图像数据集。在此基础上，提出了LCRNet模型，该模型通过融合大选择性自适应融合（LSAF）模块与协调注意力（CA）模块的协同机制，有效处理荧光图像数据。结果 在独立测试集上，该模型达到了94%的准确率和94.1%的F1分数，优于对比模型。讨论 本研究提出了一种小麦白粉病严重程度分类方法，降低了荧光数据处理的计算成本，为推动作物病害防控体系向智能化、精准化和绿色化转型提供了重要的方法学支撑和技术启示。","Frontiers in Plant Science","2026-10-12T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":142,"freshness":45,"relevant":22,"comment":423},"将叶绿素荧光成像与深度学习结合用于小麦白粉病严重度识别，方法新颖、数据可靠，对智慧植保有实质参考价值。",[425],{"name":420,"url":417},[27,28,427,428,429],"病害识别","叶绿素荧光","小麦白粉病",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[432],{"code":74,"slug":433,"name":434,"path":435,"parent_code":436},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":438},{"candidates":439,"agricultural_evidence":46},[440,444,449],{"code":32,"score":142,"supported":46,"title":441,"abstract":442,"tags":443},[33],[34,33],[],{"code":74,"score":143,"supported":46,"title":445,"abstract":446,"tags":448},[],[299,447],"deep learning",[28,427],{"code":80,"score":163,"supported":46,"title":450,"abstract":451,"tags":453},[],[27,452],"smart agriculture",[27],[455,456],"小麦白粉病 叶绿素荧光 深度学习","LCRNet 小麦病害识别","小麦白粉病叶绿素荧光深度学习-4806","10.3389\u002Ffpls.2026.1837843",{"doi":458,"openalex_id":460,"authors":461,"venue":420,"cited_by_count":121,"oa_url":480,"card":481,"direction":486,"ingested_from":128},"W7221549180",[462,464,467,470,472,474,476,478],{"name":463,"orcid":9},"Yijia Chen",{"name":465,"orcid":466},"Chengyuan Hao","https:\u002F\u002Forcid.org\u002F0000-0002-6002-3963",{"name":468,"orcid":469},"Zhiyu Li","https:\u002F\u002Forcid.org\u002F0000-0003-3771-0199",{"name":471,"orcid":9},"Xupeng An",{"name":473,"orcid":9},"Jingxiang Huang",{"name":475,"orcid":9},"Xinchang Chen",{"name":477,"orcid":9},"Shidong Wang",{"name":479,"orcid":9},"ZhiChao Chen","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1837843\u002Fpdf",{"tldr":482,"method":483,"finding":484,"direction":126,"opportunity":485},"该研究利用叶绿素荧光成像与深度学习识别小麦白粉病并分级。","构建QYmax荧光图像数据集，提出融合LSAF与CA模块的LCRNet模型。","模型在独立测试集上准确率达94%、F1为94.1%，优于对比模型。","可探索多病害、多作物荧光图像通用模型，并融合田间时序数据实现早期预警。","智慧农业 \u002F 农业物联网","2026-10-13T23:30:09.060156Z"]