[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3050":3,"related-3050":45},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"search_phrases":30,"slug":33,"view_count":34,"doi":8,"paper":35,"created_at":44},3050,"面向再生农业与气候韧性食物系统的可解释数字孪生集成框架","https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F18\u002F9645","研究提出集成全球农业基础表征模型（GAFRM）、自演化可解释数字孪生（SEDT）、自演化进化基础优化器（SEEFO）与增强型绿色再生农业可持续性指数（GRASI）的统一计算框架，显式将作物微量营养素密度纳入农业决策架构并建模其与覆盖作物、生物炭施用、免耕等再生实践的关系。框架使用多源全球数据集评估全球约60个代表性国家6大洲15个气候带2000-2026年数据，SEDT达到RMSE 3.18\u002FMAE 2.29\u002FR² 0.972\u002FNSE 0.968；SEEFO获得最高Hypervolume 0.956与最低GD 0.028；通过XAI特征归因建立全透明、可解释决策支持环境。",null,"MDPI Sustainability 18(18):9645","2026-09-20T00:00:00Z","论文",10,false,80,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,22,13,9,1,"方法新颖、数据规模大且指标可靠，属农业人工智能与气候智慧农业前沿成果，值得进入每日精选。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","可解释AI","数字孪生","气候韧性","再生农业",[31,32],"GAFRM 数字孪生 再生农业","GRASI 气候韧性","GAFRM数字孪生再生农业-3050",0,{"doi":8,"openalex_id":8,"authors":36,"venue":8,"cited_by_count":34,"oa_url":8,"card":37,"direction":41,"ingested_from":43},[],{"tldr":38,"method":39,"finding":40,"direction":41,"opportunity":42},"提出集成数字孪生与进化优化的可解释框架，将微量营养素密度纳入再生农业决策。","全球60国2000-2026多源数据，GAFRM+SEDT+SEEFO+GRAS","SEDT预测精度高（R²0.972），SEEFO优化性能最优，实现透明可解释决策支持。","农业人工智能与决策模型","可探索将微量营养素密度与再生实践耦合的实时数字孪生，并验证跨气候带可迁移性。","agent","2026-09-21T00:04:39.490071Z",{"total":46,"page":20,"page_size":46,"items":47},6,[48,77,124,160,193,235],{"id":49,"title":50,"url":51,"summary":52,"summary_zh":8,"content":8,"source_name":53,"source_url":8,"published_at":54,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":55,"score_detail":56,"sources":59,"tags":61,"search_phrases":65,"slug":68,"view_count":34,"doi":8,"paper":69,"created_at":76},2902,"TSAFI-DT:可持续性感知花生产量预测数字孪生框架,登MDPI AI 7(9)364","https:\u002F\u002Fwww.mdpi.com\u002F2673-2688\u002F7\u002F9\u002F364","本研究提出TSAFI-DT可回顾验证的、数据驱动的Digital Twin原型,集成时空数据重建、可持续状态表征、分层产量预测、反事实分析与情景模拟。基于1997-2023年印度地区级花生数据,采用贝叶斯优化的XGBoost模型进行一步前瞻产量预测,RMSE=0.171 t\u002Fha,显著优于基线;结合固定效应与合成控制分析,eRAI扩展再生农业指数整合作物多样性、生产力稳定性、土地利用效率和产量趋势,预测产量在可持续性扰动下可提升12.4%。","MDPI AI","2026-09-14T00:00:00Z",78,{"impact":16,"substance":17,"depth":57,"authority":18,"freshness":46,"relevant":20,"comment":58},19,"方法新颖、数据规模扎实的农业数字孪生研究，对智慧农业与产量预测领域有参考价值，但属细分学术进展，非产业级事件。",[60],{"name":53,"url":51},[25,62,27,63,64],"农业人工智能","可持续农业","花生产量预测",[66,67],"TSAFI-DT 花生 数字孪生","印度 花生 产量预测","TSAFI-DT花生数字孪生-2902",{"doi":8,"openalex_id":8,"authors":70,"venue":8,"cited_by_count":34,"oa_url":8,"card":71,"direction":41,"ingested_from":43},[],{"tldr":72,"method":73,"finding":74,"direction":41,"opportunity":75},"提出可持续性感知数字孪生框架TSAFI-DT，用于印度花生产量预测与情景模拟。","基于1997-2023年印度地区级数据，用贝叶斯优化XGBoost和合成控制分析","XGBoost预测RMSE为0.171 t\u002Fha，可持续性扰动下产量可提升12.4%。","可探索将数字孪生与实时物联网数据结合，实现动态可持续性评估与决策支持。","2026-09-19T00:06:08.754978Z",{"id":78,"title":79,"url":80,"summary":81,"summary_zh":82,"content":8,"source_name":83,"source_url":80,"published_at":84,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":85,"score_detail":86,"sources":92,"tags":94,"search_phrases":99,"slug":102,"view_count":34,"doi":103,"paper":104,"created_at":123},2770,"Science and technology for food system transformation: Integrating edible biodiversity, grassland conservation, nutrition, and climate resilience in Ghana and Africa","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jafr.2026.103302","ABSTRACT Grassland ecosystems in Ghana and comparable African savanna regions support diverse plant resources that contribute to food security, ecosystem functioning, and climate adaptation. However, many edible plant species remain underutilized due to limited scientific characterization, weak integration into food systems, inadequate conservation attention, and insufficient application of emerging technologies for biodiversity assessment and management. This review evaluates the role of science and technology in supporting edible biodiversity conservation, nutritional utilization, grassland monitoring, and climate resilience within African grassland contexts. The review further examines processing technologies, nutritional composition, and food-safety considerations for underutilized edible species, linking conservation outcomes to food manufacturing applications and dietary relevance. A structured narrative review was conducted using records retrieved from Web of Science and Google Scholar, covering studies published from 2000 to 2026. Following the PRISMA-based identification, screening, eligibility assessment, and inclusion process, 74 studies were selected for qualitative synthesis. The included literature was examined across three interconnected thematic domains: (i) science and technology applications, including remote sensing, geographic information systems, ecological monitoring approaches, and molecular techniques; (ii) edible biodiversity conservation and utilization, focusing on plant diversity, nutritional potential, indigenous food resources, and sustainable management; and (iii) climate resilience and policy integration, addressing ecosystem adaptation, governance, and conservation planning. The review demonstrates that technological approaches provide valuable tools for documenting biodiversity patterns, improving ecological assessments, and strengthening evidence-based conservation decisions, although the extent of their application remains uneven across African grassland systems. The findings further highlight that underutilized edible species are constrained not only by limited consumption and market integration but also by inadequate agronomic research, conservation prioritization, and policy recognition. Based on the synthesized evidence, a science–technology integration framework is proposed to connect biodiversity monitoring, nutritional assessment, sustainable utilization, and climate adaptation strategies. This framework provides an evidence-informed pathway for advancing the conservation and sustainable use of edible plant diversity. The framework also recognizes the current limitations in empirical validation and implementation across different grassland regions.","摘要 加纳及类似非洲稀树草原地区的草地生态系统支撑着多样化的植物资源，这些资源对粮食安全、生态系统功能和气候适应具有重要贡献。然而，许多可食用植物物种仍未得到充分利用，原因包括科学表征有限、与粮食系统的整合薄弱、保护关注不足，以及新兴技术在生物多样性评估和管理中的应用不充分。本综述评估了科学技术在非洲草地背景下支持可食用生物多样性保护、营养利用、草地监测和气候韧性方面的作用。综述进一步考察了未充分利用可食用物种的加工技术、营养成分和食品安全考量，将保护成果与食品制造应用及膳食相关性联系起来。本研究采用结构化叙述性综述方法，使用从Web of Science和Google Scholar检索的记录，涵盖2000年至2026年发表的研究。遵循基于PRISMA的识别、筛选、资格评估和纳入流程，最终选取74项研究进行定性综合。纳入文献在三个相互关联的主题领域进行了考察：(i) 科学技术应用，包括遥感、地理信息系统、生态监测方法和分子技术；(ii) 可食用生物多样性保护与利用，聚焦植物多样性、营养潜力、本土食物资源和可持续管理；(iii) 气候韧性与政策整合，涉及生态系统适应、治理和保护规划。综述表明，技术方法为记录生物多样性模式、改善生态评估和加强基于证据的保护决策提供了有价值的工具，尽管其在非洲草地系统中的应用程序仍不均衡。研究结果进一步强调，未充分利用的可食用物种不仅受到消费和市场整合有限的制约，还受到农艺研究不足、保护优先度低和政策认可不够的限制。基于综合证据，提出了一个科学技术整合框架，以连接生物多样性监测、营养评估、可持续利用和气候适应策略。该框架为","Journal of Agriculture and Food Research","2026-09-16T00:00:00Z",75,{"impact":87,"substance":88,"depth":89,"authority":18,"freshness":90,"relevant":20,"comment":91},16,21,17,8,"基于74项研究的PRISMA综述，提出科技整合框架，对非洲草原可食用生物多样性保护与利用具参考价值，但属区域外研究、应用落地尚不均衡。",[93],{"name":83,"url":80},[25,28,95,96,97,98],"生物多样性","遥感监测","营养安全","草原保护",[100,101],"生物多样性 智慧农业 气候韧性 草原保护","生物多样性 智慧农业","生物多样性智慧农业气候韧性草原保护-2770","10.1016\u002Fj.jafr.2026.103302",{"doi":103,"openalex_id":105,"authors":106,"venue":83,"cited_by_count":34,"oa_url":114,"card":115,"direction":121,"ingested_from":122},"W7213261782",[107,109,112],{"name":108,"orcid":8},"Kwame Anokye",{"name":110,"orcid":111},"Huang Rong","https:\u002F\u002Forcid.org\u002F0000-0003-1039-0914",{"name":113,"orcid":8},"Zhen-fen Zhang","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2666154326006733\u002Fpdf",{"tldr":116,"method":117,"finding":118,"direction":119,"opportunity":120},"综述非洲草地可食用生物多样性保护与利用，提出科技整合框架连接监测、营养与气候适应。","PRISMA系统综述，检索Web of Science和Google Schol","技术应用不均衡，未充分利用的可食用物种受农艺研究、保护优先和政策认可不足制约。","农业绿色发展与碳","可研究遥感与分子技术如何具体支撑非洲草地可食用物种的营养评估与保护决策。","智慧农业 \u002F 农业物联网","openalex","2026-09-17T23:30:10.652175Z",{"id":125,"title":126,"url":127,"summary":128,"summary_zh":129,"content":8,"source_name":130,"source_url":127,"published_at":131,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":132,"score_detail":133,"sources":136,"tags":138,"search_phrases":142,"slug":145,"view_count":34,"doi":146,"paper":147,"created_at":159},2648,"Horticulture-Voltaic: The Earth's Solar Horticulture Turns Every Ray into Food and Power - A Comprehensive Review","https:\u002F\u002Fdoi.org\u002F10.25125\u002Fijoear-sep-2026-8","Horticulture-voltaic systems - the co-location of photovoltaic (PV) electricity generation with horticultural crop production on the same land - are emerging as a central strategy for climate-resilient horticulture. This in-depth review examines the technology, engineering, intercultural operations and empirical evidence for horticulture-voltaics applied to fruit, vegetable and flower crops. We cover solar panel technologies, system architectures, the full design-component and engineering stack (mounting heights of 2.5-5 m, panel spacing and tilt, deep-buried armoured cables, structural safety under ASCE-7-16 wind loads, smart irrigation integration), the electrical architecture (module\u002Fstring voltage, inverter, battery, transformer, grid connection), and intercultural operations adapted for sub-array horticulture. Empirical evidence shows that shade relieves heat and drought stress, with grape +277% (Mediterranean, LER 3.54), tomato and chiltepin doubled or tripled (Arizona), and partial shade increasing floral abundance in flowers. Shading must stay below ~30% for fruit crops and 20-40% for most vegetables and flower crops. With continued research and engineering, horticulture-voltaics can deliver climate-adaptive, resource-efficient and energy-positive horticultural systems.","园艺光伏系统——在同一土地上将光伏（PV）发电与园艺作物生产相结合——正逐渐成为气候适应性园艺的核心策略。这篇深度综述考察了园艺光伏应用于水果、蔬菜和花卉作物的技术、工程、行间作业及实证证据。我们涵盖太阳能电池板技术、系统架构、完整的设计-组件与工程体系（安装高度2.5-5米、板间距与倾角、深埋铠装电缆、ASCE-7-16风荷载下的结构安全、智能灌溉集成）、电气架构（组件\u002F组串电压、逆变器、电池、变压器、电网连接），以及针对子阵列园艺适配的行间作业。实证证据表明，遮阴可缓解高温和干旱胁迫，葡萄增产+277%（地中海地区，LER 3.54），番茄和野生辣椒产量翻倍或增至三倍（亚利桑那州），部分遮阴增加了花卉的花量。水果作物的遮阴率须保持在约30%以下，大多数蔬菜和花卉作物为20-40%。通过持续的研究与工程实践，园艺光伏能够提供气候适应性、资源高效且能量正输出的园艺系统。","International Journal of Environmental and Agriculture Research","2026-09-15T00:00:00Z",85,{"impact":17,"substance":134,"depth":57,"authority":18,"freshness":90,"relevant":20,"comment":135},23,"系统综述农光互补园艺的技术架构与实证数据，提出遮光阈值等可操作结论，对设施农业与新能源融合具参考价值。",[137],{"name":130,"url":127},[25,28,139,140,141],"农光互补","设施园艺","光伏农业",[143,144],"光伏农业 农光互补 智慧农业 气候韧性","光伏农业 农光互补","光伏农业农光互补智慧农业气候韧性-2648","10.25125\u002Fijoear-sep-2026-8",{"doi":146,"openalex_id":148,"authors":149,"venue":130,"cited_by_count":34,"oa_url":153,"card":154,"direction":121,"ingested_from":122},"W7213346284",[150],{"name":151,"orcid":152},"Jadala Shankaraswamy","https:\u002F\u002Forcid.org\u002F0000-0002-5623-6384","https:\u002F\u002Fijoear.com\u002Fassets\u002Farticles_menuscripts\u002Ffile\u002FIJOEAR-SEP-2026-8.pdf",{"tldr":155,"method":156,"finding":157,"direction":119,"opportunity":158},"综述园艺光伏系统，即同一土地上光伏发电与园艺作物共址，涵盖技术、工程与实证。","综述光伏技术、系统架构、工程设计与间作操作，汇总多地实证数据。","适度遮阴缓解热旱胁迫，葡萄增产277%，番茄等倍增，遮阴需低于30%。","可探索不同气候区园艺光伏最优遮阴阈值与作物-光伏协同设计模型。","2026-09-16T23:30:12.350469Z",{"id":161,"title":162,"url":163,"summary":164,"summary_zh":165,"content":8,"source_name":166,"source_url":163,"published_at":131,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":167,"score_detail":168,"sources":171,"tags":173,"search_phrases":176,"slug":179,"view_count":34,"doi":180,"paper":181,"created_at":192},2641,"Smart Agriculture and Sustainable Development in Agro-Ecosystems: Innovative Computational Modeling for Digital Twins","https:\u002F\u002Fdoi.org\u002F10.37394\u002F232015.2026.22.80","By boosting yields, improving efficiency, and reducing costs (while managing resources), digital technologies have driven innovation in agro-ecosystems in recent years. As a means to overcome ever-limited resources, Smart Agriculture – sometimes also referred to as Agriculture 4.0 – has increasingly leveraged digital solutions to achieve efficiency and sustainability. Digital twins in rural systems perform as computational (in silico) replicas of food production lines, which can drive innovation by enabling real-time optimization as well as predictive decision-making. Considering that both non-human and human actors are directly involved, this paper discusses (from a strategic alignment viewpoint) a prospective symbiotic ethos that farmers and ranchers may pursue when it comes to integrated, participatory and efficient transfer and usage of digital technology in rural activities. Bearing particularly in mind smallholders, challenges and opportunities for Smart Agriculture include: (i) implementation of hybrid in-house simulators of agro-ecosystems by suitably combining mechanistic modeling with data-driven simulation; (ii) use of dimensionless mathematical modeling to expedite scale-up, optimization, and translation of innovative digital technologies; and (iii) validating as well as transferring novel digital solutions in consideration of strategic issues identified by rural end-users.","近年来，数字技术通过提高产量、提升效率、降低成本（同时管理资源），推动了农业生态系统的创新。作为克服日益有限的资源的一种手段，智慧农业（有时也被称为农业4.0）越来越多地利用数字解决方案来实现效率和可持续性。农村系统中的数字孪生作为食品生产线的计算（计算机模拟）副本，通过实现实时优化和预测性决策来推动创新。考虑到非人类和人类参与者都直接参与其中，本文（从战略协同的视角）探讨了农民和牧场主在将数字技术整合、参与式和高效地转移及应用于农村活动时可能追求的一种前瞻性共生理念。特别考虑到小农户，智慧农业面临的挑战和机遇包括：（i）通过将机理建模与数据驱动模拟适当结合，实施农业生态系统的混合内部模拟器；（ii）使用无量纲数学建模来加速创新数字技术的规模化、优化和转化；（iii）在考虑农村终端用户所识别的战略问题的基础上，验证和转移新型数字解决方案。","WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT",74,{"impact":87,"substance":169,"depth":89,"authority":18,"freshness":90,"relevant":20,"comment":170},20,"核心期刊论文，提出农业生态数字孪生的混合建模与无量纲化方法，对小农户数字化转型有参考价值，但偏理论、时效略滞后。",[172],{"name":166,"url":163},[25,62,27,174,175],"小农户","农业建模",[177,178],"农业人工智能 农业建模 数字孪生 智慧农业","农业人工智能 农业建模","农业人工智能农业建模数字孪生智慧农业-2641","10.37394\u002F232015.2026.22.80",{"doi":180,"openalex_id":182,"authors":183,"venue":166,"cited_by_count":34,"oa_url":186,"card":187,"direction":121,"ingested_from":122},"W7213267978",[184],{"name":185,"orcid":8},"Jose Rabi","https:\u002F\u002Fwseas.com\u002Fjournals\u002Fead\u002F2026\u002Fb625115-032(2026).pdf",{"tldr":188,"method":189,"finding":190,"direction":121,"opportunity":191},"探讨数字孪生与计算建模在智慧农业可持续发展中的应用，聚焦小农户的挑战与机遇。","混合机理与数据驱动模拟、无量纲数学建模、数字孪生计算副本。","提出农民与牧场主共生的数字技术转移理念，强调小农户的参与式整合。","可研究小农户场景下混合模拟器的轻量化与低成本部署，以及数字孪生技术的参与式验证方法。","2026-09-16T23:30:10.078180Z",{"id":194,"title":195,"url":196,"summary":197,"summary_zh":198,"content":8,"source_name":199,"source_url":196,"published_at":54,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":200,"score_detail":201,"sources":204,"tags":206,"search_phrases":209,"slug":212,"view_count":34,"doi":213,"paper":214,"created_at":234},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。","AI",76,{"impact":87,"substance":17,"depth":16,"authority":202,"freshness":90,"relevant":20,"comment":203},12,"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。",[205],{"name":199,"url":196},[25,62,207,27,208],"产量预测","花生种植",[210,211],"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","10.3390\u002Fai7090364",{"doi":213,"openalex_id":215,"authors":216,"venue":199,"cited_by_count":34,"oa_url":196,"card":228,"direction":233,"ingested_from":122},"W7212812029",[217,220,223,225],{"name":218,"orcid":219},"Rekha R Nair","https:\u002F\u002Forcid.org\u002F0000-0002-7207-2877",{"name":221,"orcid":222},"Tina Babu","https:\u002F\u002Forcid.org\u002F0000-0001-7846-3679",{"name":224,"orcid":8},"Sumendra Yogarayan",{"name":226,"orcid":227},"Abdul Razak","https:\u002F\u002Forcid.org\u002F0000-0002-6108-3183",{"tldr":229,"method":230,"finding":231,"direction":41,"opportunity":232},"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","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","2026-09-15T23:30:26.594281Z",{"id":236,"title":237,"url":238,"summary":239,"summary_zh":240,"content":8,"source_name":241,"source_url":238,"published_at":54,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":200,"score_detail":242,"sources":244,"tags":246,"search_phrases":248,"slug":250,"view_count":34,"doi":251,"paper":252,"created_at":262},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":16,"substance":169,"depth":89,"authority":18,"freshness":90,"relevant":20,"comment":243},"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。",[245],{"name":241,"url":238},[25,62,207,26,247],"精准农业",[249,211],"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":251,"openalex_id":253,"authors":254,"venue":241,"cited_by_count":34,"oa_url":238,"card":257,"direction":121,"ingested_from":122},"W7212616413",[255],{"name":256,"orcid":8},"Manish Maan",{"tldr":258,"method":259,"finding":260,"direction":41,"opportunity":261},"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。","2026-09-15T23:30:13.705421Z"]