[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2537":3,"related-2537":120},{"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":32,"secondary_topics":38,"classification":45,"search_phrases":90,"slug":93,"view_count":94,"doi":95,"paper":96,"created_at":119},2537,"A Counterfactual-Enabled Agricultural Decision Support Framework for Sustainability-Aware Groundnut Yield Prediction Using Bayesian-Optimized XGBoost","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fai7090364","Sustainable agricultural planning requires predictive frameworks that can capture spatiotemporal variability, sustainability dynamics, and the potential outcomes of alternative management scenarios. The research proposes TSAFI-DT, a retrospectively validated, data-driven Digital Twin prototype integrating spatiotemporal data reconstruction, sustainability-state representation, hierarchical yield forecasting, counterfactual analysis, and scenario simulation. The framework operates on historical district-level APY observations and therefore represents a retrospective approximation of Digital Twin operation rather than a continuously synchronized cyber-physical agricultural Digital Twin. The Extended Regenerative Agriculture Index (eRAI) combines crop diversity, productivity–stability, land-use efficiency, and yield-trend information to characterize district-level sustainability states. A Bayesian-optimized XGBoost model is employed for one-step-ahead yield forecasting under temporal validation, while fixed-effects and synthetic-control analyses provide complementary associational and intervention-associated evidence. Evaluation using district-level groundnut data from India during 1997–2023 demonstrates that the proposed predictor achieves an RMSE of 0.171 t\u002Fha and R2=0.92, outperforming the evaluated baselines with statistically significant differences (p\u003C0.05). The fully adjusted fixed-effects model identifies a positive association between higher sustainability states and yield, while retrospective Digital Twin replay demonstrates close temporal agreement between predicted and observed outcomes. Model-based scenario simulations indicate predicted yield increases of up to 12.4% under the evaluated sustainability-state perturbations; these estimates represent counterfactual sensitivity rather than guaranteed causal effects. TSAFI-DT provides a reproducible framework for sustainability-aware agricultural forecasting, comparative scenario exploration, and data-driven decision support.","可持续农业规划需要能够捕捉时空变异性、可持续性动态以及替代管理情景潜在结果的预测框架。本研究提出TSAFI-DT，一个经回溯验证的数据驱动数字孪生（Digital Twin）原型，集成了时空数据重建、可持续性状态表征、分层产量预测、反事实分析和情景模拟。该框架基于历史地区级APY观测数据运行，因此代表的是数字孪生运行的回溯近似，而非持续同步的网络-物理农业数字孪生。扩展再生农业指数（Extended Regenerative Agriculture Index, eRAI）综合了作物多样性、生产力-稳定性、土地利用效率和产量趋势信息，以刻画地区级可持续性状态。采用贝叶斯优化的XGBoost模型在时间验证下进行一步超前产量预测，同时固定效应和合成控制分析提供互补的关联性证据和干预关联性证据。利用印度1997—2023年地区级花生数据进行评估，结果表明所提出的预测器实现了0.171 t\u002Fha的RMSE和R²=0.92，优于所评估的基线模型且差异具有统计学显著性（p\u003C0.05）。完全调整的固定效应模型识别出较高可持续性状态与产量之间的正相关关系，而回溯性数字孪生重放表明预测结果与观测结果在时间上高度一致。基于模型的情景模拟显示，在所评估的可持续性状态扰动下，预测产量增幅最高可达12.4%；这些估计代表的是反事实敏感性而非保证的因果效应。TSAFI-DT为可持续性感知的农业预测、比较情景探索和数据驱动决策支持提供了一个可复现的框架。",null,"AI","2026-09-14T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,22,18,12,8,1,"提出融合数字孪生、反事实分析与贝叶斯优化XGBoost的花生产量预测框架，方法新颖、数据跨度长且验证充分，对可持续农业决策支持有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","产量预测","数字孪生","花生种植",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"H8","sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture","H",{"status":46,"locked":14,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":52},[51,57,65,71,78,85],{"code":33,"score":20,"supported":52,"title":53,"abstract":55,"tags":56},true,[54],"decision support",[54],[28],{"code":40,"score":58,"supported":52,"title":59,"abstract":60,"tags":64},9,[],[61,62,63],"可持续农业","再生农业","regenerative agriculture",[],{"code":66,"score":21,"supported":52,"title":67,"abstract":68,"tags":70},"I1",[],[30,69],"digital twin",[30,27],{"code":72,"score":73,"supported":52,"title":74,"abstract":75,"tags":77},"A4",4,[],[76],"花生",[31],{"code":79,"score":80,"supported":52,"title":81,"abstract":82,"tags":84},"J1",3,[],[83],"农业规划",[],{"code":86,"score":22,"supported":14,"title":87,"abstract":88,"tags":89},"I3",[],[],[29],[91,92],"农业人工智能 产量预测 数字孪生 智慧农业","农业人工智能 产量预测","农业人工智能产量预测数字孪生智慧农业-2537",2,"10.3390\u002Fai7090364",{"doi":95,"openalex_id":97,"authors":98,"venue":10,"cited_by_count":110,"oa_url":6,"card":111,"direction":117,"ingested_from":118},"W7212812029",[99,102,105,107],{"name":100,"orcid":101},"Rekha R Nair","https:\u002F\u002Forcid.org\u002F0000-0002-7207-2877",{"name":103,"orcid":104},"Tina Babu","https:\u002F\u002Forcid.org\u002F0000-0001-7846-3679",{"name":106,"orcid":9},"Sumendra Yogarayan",{"name":108,"orcid":109},"Abdul Razak","https:\u002F\u002Forcid.org\u002F0000-0002-6108-3183",0,{"tldr":112,"method":113,"finding":114,"direction":115,"opportunity":116},"提出TSAFI-DT数字孪生框架，用贝叶斯优化XGBoost预测印度花生产量并做反事实情景分析。","基于1997-2023年印度县级花生产量数据，构建eRAI可持续性指数，采用贝叶","模型RMSE为0.171 t\u002Fha、R²=0.92，高可持续状态与产量正相关，情景模拟产量最高提升1","农业人工智能与决策模型","可延伸至实时物联网数据驱动的数字孪生，并验证反事实情景的因果效应与跨作物泛化能力。","数字乡村与农业信息化","openalex","2026-09-15T23:30:26.594281Z",{"total":121,"page":22,"page_size":121,"items":122},6,[123,244,316,401,454,500],{"id":124,"title":125,"url":126,"summary":127,"summary_zh":128,"content":9,"source_name":129,"source_url":126,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":130,"sources":135,"tags":137,"primary_topic":140,"secondary_topics":141,"classification":148,"search_phrases":228,"slug":230,"view_count":94,"doi":231,"paper":232,"created_at":243},2518,"AI-Driven Transformation in Sustainable Agriculture: A Systematic Review","https:\u002F\u002Fdoi.org\u002F10.18805\u002Fijare.a-6627","The agriculture sector is facing unprecedented pressure from a rapidly growing global population, erratic weather conditions and declining water availability. Traditional farming methods are increasingly proving insufficient in meeting the rising food demand, necessitating a shift towards technology-driven solutions. This paper provides a critical and systematic review of the application of Artificial Intelligence (AI) in agriculture, analysing its impact on productivity and sustainability, while identifying potential barriers hindering adoption. Employing a Systematic Literature Review approach, this study synthesizes recent research organized around the core domains of agricultural production - crop, soil and nutrient, water and irrigation and crop-protection (pest, disease and weed) management, alongside yield forecasting - examining how data-driven tools such as machine learning, the Internet of Things, robotics and computer vision are applied within each and prioritizing studies that reported agronomically meaningful outcomes such as yield gains and resource-use efficiency. The strongest gains identified across the review fall within three agricultural areas - crop and soil monitoring, yield forecasting and robotic field operations such as targeted detection, weeding and harvesting. The technologies enabling them, chiefly machine-learning models such as convolutional neural networks combined with field and remote sensors, have delivered high in-field precision, with reported object-detection precision of up to 95.78% in field conditions. In practical terms, this translates into more efficient use of water, fertilizer and other inputs and into improved yield and crop quality. The review concludes that prioritizing Explainable AI and interoperable farm systems is central to building farmer trust and safeguarding food security under a changing climate.","农业部门正面临着来自全球人口快速增长、天气条件不稳定以及水资源日益减少的前所未有的压力。传统耕作方法在满足不断增长的粮食需求方面日益显得力不从心，因此有必要转向技术驱动的解决方案。本文对人工智能（Artificial Intelligence, AI）在农业中的应用进行了批判性和系统性的综述，分析了其对生产力和可持续性的影响，同时识别了阻碍其采用的潜在障碍。本研究采用系统性文献综述方法，围绕农业生产的核心领域——作物、土壤与养分、水资源与灌溉、作物保护（病虫害和杂草）管理以及产量预测——对近期研究进行了综合梳理，考察了机器学习、物联网、机器人技术和计算机视觉等数据驱动工具在各领域中的应用方式，并优先关注报告了具有农学意义成果（如产量提升和资源利用效率）的研究。综述中发现的最显著收益集中在三个农业领域——作物与土壤监测、产量预测以及机器人田间作业（如靶向检测、除草和收获）。实现这些收益的技术，主要是卷积神经网络等机器学习模型与田间及遥感传感器的结合，已在田间条件下实现了高精度，所报告的目标检测精度在田间条件下高达95.78%。在实际应用中，这意味着水、肥料及其他投入品的更高效利用，以及产量和作物品质的提升。综述得出结论：优先发展可解释人工智能（Explainable AI）和可互操作的农场系统，对于在气候变化背景下建立农民信任和保障粮食安全至关重要。","Indian Journal of Agricultural Research",{"impact":19,"substance":131,"depth":132,"authority":133,"freshness":21,"relevant":22,"comment":134},20,17,13,"系统综述梳理AI在作物土壤监测、产量预测与机器人田间作业中的应用成效与推广障碍，结论扎实，对智慧农业方向有参考价值。",[136],{"name":129,"url":126},[27,28,29,138,139],"可解释AI","精准农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[142,147],{"code":143,"slug":144,"name":145,"path":146,"parent_code":44},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":149},{"candidates":150,"agricultural_evidence":52},[151,162,168,173,180,187,194,201,206,212,218,224],{"code":33,"score":152,"supported":52,"title":153,"abstract":154,"tags":161},11,[],[155,156,157,158,159,160],"目标检测","机器学习","计算机视觉","machine learning","artificial intelligence","computer vision",[28,138],{"code":143,"score":58,"supported":52,"title":163,"abstract":164,"tags":167},[],[165,166,144],"灌溉","水资源",[],{"code":40,"score":21,"supported":52,"title":169,"abstract":171,"tags":172},[170],"sustainable agriculture",[],[],{"code":174,"score":121,"supported":52,"title":175,"abstract":176,"tags":179},"A8",[],[177,178],"粮食安全","food security",[],{"code":181,"score":121,"supported":52,"title":182,"abstract":183,"tags":186},"F3",[],[184,185],"肥料","fertilizer",[],{"code":188,"score":121,"supported":52,"title":189,"abstract":190,"tags":193},"H2",[],[191,192],"土壤","soil",[],{"code":195,"score":121,"supported":52,"title":196,"abstract":197,"tags":200},"I5",[],[198,199],"物联网","internet of things",[],{"code":86,"score":73,"supported":52,"title":202,"abstract":203,"tags":205},[],[204],"遥感",[29],{"code":207,"score":80,"supported":52,"title":208,"abstract":209,"tags":211},"A7",[],[210],"间作",[],{"code":213,"score":80,"supported":52,"title":214,"abstract":215,"tags":217},"F4",[],[216],"除草",[],{"code":219,"score":80,"supported":52,"title":220,"abstract":221,"tags":223},"H7",[],[222],"气候变化",[],{"code":66,"score":94,"supported":14,"title":225,"abstract":226,"tags":227},[],[],[27,139],[229,92],"农业人工智能 产量预测 智慧农业 精准农业","农业人工智能产量预测智慧农业精准农业-2518","10.18805\u002Fijare.a-6627",{"doi":231,"openalex_id":233,"authors":234,"venue":129,"cited_by_count":110,"oa_url":126,"card":237,"direction":242,"ingested_from":118},"W7212616413",[235],{"name":236,"orcid":9},"Manish Maan",{"tldr":238,"method":239,"finding":240,"direction":115,"opportunity":241},"系统综述AI在可持续农业中的应用，分析生产力与可持续性影响及推广障碍。","系统文献综述，围绕作物、土壤、水、植保和产量预测梳理ML、IoT、机器人、计算机","作物与土壤监测、产量预测和机器人田间作业收益最大，检测精度达95.78%，需可解释AI与互操作系统。","可解释AI与互操作农场系统在农户信任和气候适应中的作用尚缺实证，是值得深入的研究空白。","智慧农业 \u002F 农业物联网","2026-09-15T23:30:13.705421Z",{"id":245,"title":246,"url":247,"summary":248,"summary_zh":249,"content":9,"source_name":250,"source_url":247,"published_at":251,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":252,"score_detail":253,"sources":255,"tags":257,"primary_topic":261,"secondary_topics":262,"classification":265,"search_phrases":290,"slug":293,"view_count":21,"doi":294,"paper":295,"created_at":315},3355,"AI and Sustainable Agriculture Through Cost–Benefit Analysis of Smart Irrigation Systems","https:\u002F\u002Fdoi.org\u002F10.22004\u002Fag.econ.412813","The advancing role of Artificial Intelligence (AI) and its application in agriculture have disrupted traditional agricultural practices, with smart irrigation systems representing one of the leading technologies enabling sustainable agriculture. Smart irrigation systems utilize real–time data, machine learning algorithms, and predictive analytics to better optimize irrigation water use, limit wasted resources, and improve the yields of crop products. The proposed research will assess the economic and environmental impacts of AI smart irrigation systems with a full costs–benefits analysis. The proposed research considers both the capital cost and operating cost of smart irrigation systems and compares these traditional irrigation practices while also examining the long–term benefits of potential water savings from Smart Irrigation Systems, expanded agricultural production, and reduced human labour. This will give context for measuring the impacts of Smart Irrigation Systems on farm businesses, including both opportunities and barriers to adoption. Additionally, using a formal literature review to lock down existing research and surveys of irrigation farmers to collect a field data set will provide the proposed researchers a collective sample to measure the efficacy of AI smart irrigation systems, identify barriers, compare opportunities, and measure performance under differing climate and soil properties. The research will find high and substantial respective levels of benefits from the implementation of AI–based smart systems, particularly in water–stressed systems with positive impacts on farm profitability, private, and environmental conservation. This research is essential for informing stakeholders of actions and the delivery of AI–enabled solutions in support of more sustainable agricultural practices.","人工智能（Artificial Intelligence, AI）的不断发展及其在农业中的应用已经颠覆了传统的农业实践，其中智能灌溉系统是实现可持续农业的领先技术之一。智能灌溉系统利用实时数据、机器学习算法和预测分析，更好地优化灌溉用水、减少资源浪费并提高作物产量。拟议研究将通过全面的成本效益分析，评估AI智能灌溉系统的经济和环境影响。该研究将综合考虑智能灌溉系统的资本成本和运营成本，并将其与传统灌溉实践进行比较，同时考察智能灌溉系统在潜在节水、扩大农业生产和减少人力劳动方面的长期效益。这将为衡量智能灌溉系统对农场经营的影响提供背景，包括采用的机会和障碍。此外，通过正式文献综述锁定现有研究成果，并对灌溉农户进行调查以收集实地数据集，将为拟议研究者提供一个集体样本，用以衡量AI智能灌溉系统的效能、识别障碍、比较机会，并评估在不同气候和土壤条件下的表现。研究发现，实施基于AI的智能系统可带来显著且可观的效益，尤其是在水资源紧张的地区，对农场盈利能力、私人利益和环境保护均有积极影响。这项研究对于向利益相关者通报行动方案以及推动AI赋能解决方案以支持更可持续的农业实践至关重要。","AgEcon Search (University of Minnesota, USA)","2026-09-23T00:00:00Z",77,{"impact":19,"substance":131,"depth":19,"authority":133,"freshness":21,"relevant":22,"comment":254},"该研究以成本效益分析评估AI智能灌溉的经济与环境效益，方法系统、结论具参考价值，但属学术论文而非政策或产业事件，适合作为智慧农业主题的深度补充。",[256],{"name":250,"url":247},[27,28,258,259,260],"智能灌溉","节水农业","成本效益分析",{"code":143,"slug":144,"name":145,"path":146,"parent_code":44},[263,264],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":266},{"candidates":267,"agricultural_evidence":52},[268,273,278,282,286],{"code":143,"score":269,"supported":52,"title":270,"abstract":271,"tags":272},19,[144],[165,166,144],[258,259],{"code":40,"score":274,"supported":52,"title":275,"abstract":276,"tags":277},14,[170],[61,170],[],{"code":33,"score":13,"supported":52,"title":279,"abstract":280,"tags":281},[],[156,158,159],[28],{"code":188,"score":121,"supported":52,"title":283,"abstract":284,"tags":285},[],[191,192],[],{"code":66,"score":22,"supported":14,"title":287,"abstract":288,"tags":289},[],[],[27],[291,292],"AI 智能灌溉 成本效益","AgEcon Search 智能灌溉","AI智能灌溉成本效益-3355","10.22004\u002Fag.econ.412813",{"doi":294,"openalex_id":296,"authors":297,"venue":250,"cited_by_count":110,"oa_url":247,"card":310,"direction":242,"ingested_from":118},"W7214115789",[298,300,302,304,306,308],{"name":299,"orcid":9},"Venkata Suman Jami",{"name":301,"orcid":9},"Purushotham Prasad Kalisetti",{"name":303,"orcid":9},"Sampath Dakshina Murthy A.",{"name":305,"orcid":9},"Gurunadha R.",{"name":307,"orcid":9},"Hema Mamidipaka",{"name":309,"orcid":9},"Gurrapu Omprakash",{"tldr":311,"method":312,"finding":313,"direction":242,"opportunity":314},"通过成本效益分析评估AI智能灌溉系统的经济与环境影响。","文献综述结合灌溉农户调查数据，进行成本效益分析。","AI智能灌溉在水资源紧张地区效益显著，提升利润并促进环保。","可针对不同气候土壤条件，量化AI灌溉的长期采纳障碍与推广机制。","2026-09-24T23:30:10.558578Z",{"id":317,"title":318,"url":319,"summary":320,"summary_zh":321,"content":9,"source_name":322,"source_url":319,"published_at":323,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":252,"score_detail":324,"sources":326,"tags":328,"primary_topic":329,"secondary_topics":330,"classification":336,"search_phrases":374,"slug":377,"view_count":73,"doi":378,"paper":379,"created_at":400},2659,"Application of Remote Sensing and Machine Learning in Sustainable Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu18189472","Global agriculture is undergoing a period of profound transformation, driven by the need to increase food production in a context characterized by climate change, the degradation of natural resources and increasing pressure on agricultural ecosystems [...]","全球农业正经历一场深刻变革，其驱动力是在气候变化、自然资源退化以及农业生态系统压力日益增大的背景下提高粮食产量的需求。[...]","Sustainability","2026-09-16T00:00:00Z",{"impact":19,"substance":131,"depth":132,"authority":133,"freshness":58,"relevant":22,"comment":325},"发表于核心期刊的遥感与机器学习综述，方法视角新颖、时效性强，对智慧农业技术路线有参考价值，值得进入每日精选。",[327],{"name":322,"url":319},[27,28,156,61,204],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[331,332],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":86,"slug":333,"name":334,"path":335,"parent_code":37},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":46,"locked":14,"version":47,"method":48,"evidence":337},{"candidates":338,"agricultural_evidence":52},[339,343,347,352,357,364,370],{"code":33,"score":13,"supported":52,"title":340,"abstract":341,"tags":342},[158],[],[28,156],{"code":40,"score":58,"supported":52,"title":344,"abstract":345,"tags":346},[170],[],[61],{"code":86,"score":58,"supported":52,"title":348,"abstract":350,"tags":351},[349],"remote sensing",[],[204],{"code":219,"score":121,"supported":52,"title":353,"abstract":354,"tags":356},[],[222,355],"climate change",[],{"code":358,"score":121,"supported":52,"title":359,"abstract":360,"tags":363},"J3",[],[361,362],"全球农业","global agriculture",[],{"code":365,"score":80,"supported":52,"title":366,"abstract":367,"tags":369},"H6",[],[368],"农业生态",[],{"code":66,"score":22,"supported":14,"title":371,"abstract":372,"tags":373},[],[],[27],[375,376],"农业人工智能 可持续农业 智慧农业 机器学习","农业人工智能 可持续农业","农业人工智能可持续农业智慧农业机器学习-2659","10.3390\u002Fsu18189472",{"doi":378,"openalex_id":380,"authors":381,"venue":322,"cited_by_count":110,"oa_url":319,"card":394,"direction":398,"ingested_from":118},"W7213301703",[382,385,388,391],{"name":383,"orcid":384},"Mihai Valentin Herbei","https:\u002F\u002Forcid.org\u002F0000-0002-3884-3658",{"name":386,"orcid":387},"Ana-Cornelia Badea","https:\u002F\u002Forcid.org\u002F0000-0003-4521-5403",{"name":389,"orcid":390},"Aleksandar Ristić","https:\u002F\u002Forcid.org\u002F0000-0003-0979-3345",{"name":392,"orcid":393},"Paul Sestraș","https:\u002F\u002Forcid.org\u002F0000-0002-8554-0924",{"tldr":395,"method":396,"finding":397,"direction":398,"opportunity":399},"综述遥感与机器学习在可持续农业中的应用现状与前景。","综述遥感数据与机器学习方法在农业中的应用。","遥感结合机器学习可提升农业监测与可持续管理能力。","农业遥感与作物表型","可探索多源遥感与可解释机器学习融合，用于小农户精准决策与碳核算。","2026-09-16T23:30:28.640661Z",{"id":402,"title":403,"url":404,"summary":405,"summary_zh":406,"content":9,"source_name":407,"source_url":404,"published_at":408,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":409,"score_detail":410,"sources":412,"tags":414,"primary_topic":416,"secondary_topics":417,"classification":418,"search_phrases":434,"slug":437,"view_count":110,"doi":438,"paper":439,"created_at":453},5587,"Calibration-Shift Inference for Computer Vision Agriculture Yield Models Under Retrofit Sensor Replacement: An Artificial Intelligence Post-Deployment Recalibration Guarantee","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23544195","Computer vision yield models deployed in agricultural fields are routinely paired with retrofit sensor packages that replace or supplement the original imaging hardware, and the resulting calibration shift can silently invalidate the model's predictive guarantees. The analysis characterises the operating characteristics of the design under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64, using 2000 replications per design cell across cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The design attains 80% power at a 5 percentage point effect with 54 clusters. The paper states a formal guarantee: under stated assumptions on the stability of the calibration map, the before-after-control-impact estimator yields a consistent and asymptotically normal estimate of the calibration shift, and the associated confidence interval covers the true shift at the nominal rate. The contribution is a design-theoretic account of how retrofit-induced calibration shift can be detected and bounded without observational data, together with the sample-size requirements that follow.","部署在农田中的计算机视觉产量模型通常与改装传感器组件配合使用，这些组件用于替换或补充原始成像硬件，由此产生的校准偏移可能悄然使模型的预测保证失效。该分析在声明的方差结构下刻画了设计的工作特性，其中组间标准差为6.0、残差标准差为4.5、组内相关系数为0.64，每个设计单元采用2000次重复，聚类数分别为8、16、24和32，效应量为3、5和8个百分点。该设计在54个聚类、5个百分点效应下达到80%的检验力。论文给出了一项形式化保证：在关于校准映射稳定性的既定假设下，前后对照影响（before-after-control-impact）估计量给出校准偏移的一致且渐近正态的估计，且相关置信区间以名义覆盖率覆盖真实偏移。其贡献在于从设计理论角度说明了如何在没有观测数据的情况下检测并界定改装引起的校准偏移，以及随之而来的样本量要求。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",65,{"impact":21,"substance":19,"depth":132,"authority":20,"freshness":13,"relevant":22,"comment":411},"方法学扎实、统计设计严谨的预印本论文，但偏理论设计层面，缺乏田间实证数据，产业影响有限，适合作为技术参考而非每日精选头条。",[413],{"name":407,"url":404},[27,28,157,29,415],"传感器校准",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":419},{"candidates":420,"agricultural_evidence":52},[421,426,430],{"code":33,"score":422,"supported":52,"title":423,"abstract":424,"tags":425},24,[159,160],[157,160],[28,157],{"code":66,"score":22,"supported":14,"title":427,"abstract":428,"tags":429},[],[],[27],{"code":86,"score":22,"supported":14,"title":431,"abstract":432,"tags":433},[],[],[29],[435,436],"计算机视觉 产量预测 传感器校准","农业人工智能 传感器校准 计算机视觉 产量预测","计算机视觉产量预测传感器校准-5587","10.5281\u002Fzenodo.23544195",{"doi":438,"openalex_id":440,"authors":441,"venue":407,"cited_by_count":110,"oa_url":404,"card":448,"direction":115,"ingested_from":118},"W7223104380",[442,444,446],{"name":443,"orcid":9},"Tigist Alemu Gebremariam",{"name":445,"orcid":9},"Hawi Tesfaye Desta",{"name":447,"orcid":9},"Mulugeta Bekele Wolde",{"tldr":449,"method":450,"finding":451,"direction":115,"opportunity":452},"提出改造传感器替换后视觉产量模型校准漂移的检测与置信区间保证方法。","用前后对照影响估计量，基于方差结构做2000次重复的模拟设计分析。","5个百分点效应下54个集群可达80%检验力，估计量一致且渐近正态。","可将该设计理论扩展到多传感器异构替换与在线校准监测，填补真实田间验证空白。","2026-10-22T11:21:01.554995Z",{"id":455,"title":456,"url":457,"summary":458,"summary_zh":459,"content":9,"source_name":407,"source_url":457,"published_at":408,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":460,"score_detail":461,"sources":463,"tags":465,"primary_topic":467,"secondary_topics":468,"classification":470,"search_phrases":485,"slug":487,"view_count":110,"doi":488,"paper":489,"created_at":499},5570,"Evaluation of Counterfactual Explanation Robustness for Computer Vision Agriculture Yield-Forecast Models Under Satellite Cloud-Mask Gaps: An Interpretability Reliability Bound for Cooperative Storage Decisions","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545079","Counterfactual explanations are increasingly proposed as the interface through which cooperative storage managers interrogate computer-vision yield-forecast models, yet their reliability under the satellite cloud-mask gaps that routinely afflict agricultural remote sensing has not been bounded. The bound is derived from a Lipschitz continuity assumption on the forecast model and a masking-operator perturbation analysis, and it is expressed as a function of the cloud-mask gap fraction, the model's local Lipschitz constant, and the counterfactual search tolerance. A replication-based evaluation of the before-after-control-impact estimator was conducted to characterise the operating characteristics of the design under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64), with power estimated across 2000 design cells. The reliability bound degrades non-linearly as the gap fraction rises, and that the intraclass correlation of 0.64 implies that cooperative-level aggregation of counterfactual recourse is necessary to preserve explanation stability. The contribution is a decision-relevant reliability bound that storage cooperatives can use to set a maximum tolerable cloud-mask gap before counterfactual recourse is suspended.","反事实解释日益被提出作为合作式仓储管理者质询计算机视觉产量预测模型的接口，然而在农业遥感中经常出现的卫星云掩膜缺口条件下，其可靠性尚未得到界定。该界基于预测模型上的Lipschitz连续性假设和掩膜算子扰动分析推导得出，并表示为云掩膜缺口比例、模型局部Lipschitz常数和反事实搜索容差的函数。对前后对照影响估计量进行了基于重复的评估，以表征该设计在给定方差结构（组间标准差=6.0，残差标准差=4.5，组内相关系数=0.64）下的运行特征，并在2000个设计单元中估计了检验效能。可靠性界随缺口比例上升呈非线性退化，且组内相关系数0.64意味着需要对反事实救济进行合作社层面的聚合，以保持解释的稳定性。本文的贡献在于提供了一个与决策相关的可靠性界，仓储合作社可据此设定在暂停反事实救济之前可容忍的最大云掩膜缺口。",67,{"impact":21,"substance":131,"depth":132,"authority":20,"freshness":13,"relevant":22,"comment":462},"该论文提出反事实解释在卫星云掩膜缺口下的可靠性边界，方法新颖且结论可用于合作社决策，但属细分方法学研究，产业影响有限。",[464],{"name":407,"url":457},[27,28,29,204,466],"可解释性",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[469],{"code":86,"slug":333,"name":334,"path":335,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":471},{"candidates":472,"agricultural_evidence":52},[473,477,481],{"code":33,"score":20,"supported":52,"title":474,"abstract":475,"tags":476},[160],[157],[28],{"code":86,"score":21,"supported":52,"title":478,"abstract":479,"tags":480},[],[204,349],[29,204],{"code":66,"score":22,"supported":14,"title":482,"abstract":483,"tags":484},[],[],[27],[486,92],"农业人工智能 产量预测 可解释性 智慧农业","农业人工智能产量预测可解释性智慧农业-5570","10.5281\u002Fzenodo.23545079",{"doi":488,"openalex_id":490,"authors":491,"venue":407,"cited_by_count":110,"oa_url":457,"card":494,"direction":398,"ingested_from":118},"W7223114002",[492],{"name":493,"orcid":9},"Kadiatou Barry",{"tldr":495,"method":496,"finding":497,"direction":398,"opportunity":498},"推导卫星云掩膜缺口下反事实解释可靠性的理论界，并评估其对合作社储粮决策的影响。","基于Lipschitz连续性假设与掩膜扰动分析推导可靠性界，并用2000个设计单","缺口比例上升使可靠性非线性下降；ICC=0.64表明需合作社层级聚合以保持解释稳定。","可研究不同作物区云掩膜统计特性下的自适应解释暂停阈值与聚合策略。","2026-10-22T11:20:39.240791Z",{"id":501,"title":502,"url":503,"summary":504,"summary_zh":505,"content":9,"source_name":407,"source_url":503,"published_at":506,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":507,"score_detail":508,"sources":510,"tags":512,"primary_topic":515,"secondary_topics":516,"classification":517,"search_phrases":536,"slug":539,"view_count":110,"doi":540,"paper":541,"created_at":551},5315,"PLANT-GPK v4.0: Multi-Stage Plant Phenotyping with Time-Discovery and Harvest Prediction","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23461121","This paper presents a comprehensive extension to the PLANT-GPK framework, demonstrating its robust capability to analyze plants across entirely different developmental stages from single images. By formulating an inverse problem that simultaneously optimizes physiological parameters and the hidden time dimension (t), the model successfully discovered the exact age of two distinct wheat samples: a vegetative (green) stage and a senescent (golden) stage. The framework bridges computer vision, spatial heterogeneity analysis, and ODE-based physics to provide a complete digital twin capable of forecasting the exact day of harvest regardless of the plant's current state.","本文对PLANT-GPK框架进行了全面扩展，展示了其仅凭单张图像即可分析处于完全不同发育阶段植物的稳健能力。通过构建一个同时优化生理参数与隐藏时间维度（t）的反问题，该模型成功推断出两个不同小麦样本的精确年龄：一个处于营养期（绿色阶段），另一个处于衰老期（金色阶段）。该框架融合了计算机视觉、空间异质性分析以及基于常微分方程（ODE）的物理建模，提供了一个完整的数字孪生，无论植物当前处于何种状态，均能预测确切的收获日期。","2026-10-19T00:00:00Z",81,{"impact":19,"substance":18,"depth":19,"authority":133,"freshness":13,"relevant":22,"comment":509},"该研究提出从单张图像反演植物生育阶段与收获日的数字孪生框架，方法新颖且具产业应用潜力，值得进入每日精选。",[511],{"name":407,"url":503},[27,28,30,513,514],"植物表型","收获预测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":518},{"candidates":519,"agricultural_evidence":52},[520,524,529],{"code":66,"score":21,"supported":52,"title":521,"abstract":522,"tags":523},[],[30,69],[30,27],{"code":33,"score":525,"supported":52,"title":526,"abstract":527,"tags":528},7,[],[157,160],[28],{"code":530,"score":121,"supported":52,"title":531,"abstract":532,"tags":535},"A2",[],[533,534],"小麦","wheat",[],[537,538],"PLANT-GPK 植物表型","小麦 收获期 预测","PLANT-GPK植物表型-5315","10.5281\u002Fzenodo.23461121",{"doi":540,"openalex_id":542,"authors":543,"venue":407,"cited_by_count":110,"oa_url":503,"card":546,"direction":398,"ingested_from":118},"W7222875391",[544],{"name":545,"orcid":9},"MOHAMMED TAHAR SAIM",{"tldr":547,"method":548,"finding":549,"direction":398,"opportunity":550},"扩展PLANT-GPK框架，从单张图像反演植物生理参数与隐藏时间维度，实现跨生育期表型分析与收获期预","基于ODE物理模型与逆问题优化，联合估计生理参数和隐藏时间t，用两个不同生育期小","模型能准确发现两个不同生育期小麦样本的精确年龄，并预测确切收获日期。","可将单图时间反演扩展到多作物多生育期，结合时序遥感构建大尺度数字孪生与收获期预报。","2026-10-19T23:30:19.267920Z"]