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This paper introduces a novel extension to the PLANT-GPK framework: Spatial Physiological Heatmaps and Predictive Digital Twins. By dividing the plant image into a 3x3 grid and solving the inverse ODE problem for each spatial patch, we reveal the hidden spatial heterogeneity of resource allocation. Furthermore, the recovered parameters are extrapolated to predict the exact day of harvest using a continuous smooth-physics model, providing a powerful prescriptive tool for precision agriculture.","传统的基于图像的植物表型分析假设空间同质性，即对整个植株估计单一组生理参数。本文提出了对PLANT-GPK框架的一种新颖扩展：空间生理热图与预测性数字孪生。通过将植物图像划分为3×3网格并针对每个空间区块求解逆常微分方程（ODE）问题，我们揭示了资源分配中隐藏的空间异质性。此外，利用连续光滑物理模型对所恢复的参数进行外推，以预测确切的收获日期，为精准农业提供了强有力的规范性工具。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-19T00:00:00Z",{"impact":92,"substance":14,"depth":92,"authority":42,"freshness":19,"relevant":20,"comment":146},"该论文提出植物表型空间异质性建模与预测性数字孪生方法，方法新颖、结论有实用价值，但属细分领域预印本成果，未达重大突破层级。",[148],{"name":143,"url":140},[25,26,150,98,151],"数字孪生","植物表型",{"code":29,"slug":30,"name":31,"path":32,"parent_code":33},[],{"status":36,"locked":13,"version":37,"method":38,"evidence":155},{"candidates":156,"agricultural_evidence":43},[157,163],{"code":29,"score":158,"supported":43,"title":159,"abstract":161,"tags":162},20,[160],"digital twin",[98,150,110],[150,25,98],{"code":49,"score":20,"supported":13,"title":164,"abstract":165,"tags":166},[],[],[26],[168,169],"PLANT-GPK 植物表型","空间生理热图 数字孪生","PLANT-GPK植物表型-5316","10.5281\u002Fzenodo.23460273",{"doi":171,"openalex_id":173,"authors":174,"venue":143,"cited_by_count":57,"oa_url":140,"card":177,"direction":181,"ingested_from":77},"W7222885652",[175],{"name":176,"orcid":8},"MOHAMMED TAHAR SAIM",{"tldr":178,"method":179,"finding":180,"direction":181,"opportunity":182},"将植物图像分3x3网格反演ODE参数，构建空间生理热图与预测数字孪生。","基于PLANT-GPK框架，分块求解逆ODE问题，并用连续平滑物理模型外推。","揭示了植物资源分配的空间异质性，可预测精确收获日。","农业遥感与作物表型","可探索更细粒度网格与多模态表型融合，提升数字孪生预测精度与田间适用性。","2026-10-19T23:30:19.414490Z",{"id":185,"title":186,"url":187,"summary":188,"summary_zh":8,"content":8,"source_name":189,"source_url":187,"published_at":190,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":191,"score_detail":192,"sources":196,"tags":198,"primary_topic":200,"secondary_topics":201,"classification":202,"search_phrases":219,"slug":222,"view_count":17,"doi":223,"paper":224,"created_at":238},5082,"Entomopathogenic nematode recent applications in precision agriculture: Methods, challenges, and future perspectives","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jip.2026.108765","Entomopathogenic nematode recent applications in precision agriculture: Methods, challenges, and future perspectives。Journal of Invertebrate Pathology","Journal of Invertebrate Pathology","2026-10-14T00:00:00Z",67,{"impact":42,"substance":90,"depth":193,"authority":93,"freshness":194,"relevant":20,"comment":195},17,8,"核心期刊综述，聚焦昆虫病原线虫在精准农业中的应用方法与挑战，专业性强但属细分领域，产业影响有限。",[197],{"name":189,"url":187},[25,26,98,199],"生物防治",{"code":29,"slug":30,"name":31,"path":32,"parent_code":33},[],{"status":36,"locked":13,"version":37,"method":38,"evidence":203},{"candidates":204,"agricultural_evidence":43},[205,210,215],{"code":29,"score":206,"supported":43,"title":207,"abstract":208,"tags":209},13,[110],[110],[25,98],{"code":211,"score":20,"supported":13,"title":212,"abstract":213,"tags":214},"F4",[],[],[199],{"code":49,"score":20,"supported":13,"title":216,"abstract":217,"tags":218},[],[],[26],[220,221],"昆虫病原线虫 精准农业","Journal of Invertebrate Pathology 线虫","昆虫病原线虫精准农业-5082","10.1016\u002Fj.jip.2026.108765",{"doi":223,"openalex_id":225,"authors":226,"venue":189,"cited_by_count":57,"oa_url":8,"card":8,"direction":76,"ingested_from":77},"W7221746877",[227,230,233,235],{"name":228,"orcid":229},"Alperen Kaan Bütüner","https:\u002F\u002Forcid.org\u002F0000-0002-2121-3529",{"name":231,"orcid":232},"Hilal Erdoğan","https:\u002F\u002Forcid.org\u002F0000-0002-0387-2600",{"name":234,"orcid":8},"İsmail Alper Susurluk",{"name":236,"orcid":237},"Edwin E. 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[预印本]","2026-10-05T00:00:00Z",71,{"impact":93,"substance":91,"depth":92,"authority":19,"freshness":19,"relevant":20,"comment":304},"arXiv预印本系统梳理植物病害数据集分类与不平衡问题，对农业AI数据建设有参考价值，但未经同行评议且偏学术，适合主题聚合而非头条精选。",[306],{"name":300,"url":298},[25,26,98,308,309],"植物病害","农业数据集",{"code":29,"slug":30,"name":31,"path":32,"parent_code":33},[],{"status":36,"locked":13,"version":37,"method":38,"evidence":313},{"candidates":314,"agricultural_evidence":43},[315,319,323],{"code":29,"score":92,"supported":43,"title":316,"abstract":317,"tags":318},[98,110],[],[25,98],{"code":49,"score":20,"supported":13,"title":320,"abstract":321,"tags":322},[],[],[26],{"code":324,"score":20,"supported":13,"title":325,"abstract":326,"tags":327},"I6",[],[],[309],[329,330],"植物病害 环境变异性 精准农业","农业人工智能 农业数据集 智慧农业 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Adoption)","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1865535\u002Ffull","基于中国农业与涉农组织452份时间滞后调查数据,运用PLS-SEM与必要条件分析。结果显示:AI采纳与变革型领导力均正向影响智慧农业与区域经济效益,智慧农业起部分中介;员工数字技能显著强化AI采纳对智慧农业的关联;必要条件分析表明AI采纳与变革领导力是更高效益的关键瓶颈条件。局限:样本集中、感知而非客观产出指标。","Frontiers in Sustainable Food Systems 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