[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3531":3,"related-3531":56},{"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":23,"tags":25,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":55},3531,"Extreme climate shocks and agricultural net carbon sinks: the moderating role of supply chain resilience in China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1940685","Introduction Extreme climate shocks increasingly challenge the ability of agricultural systems to sustain crop carbon fixation while controlling production-related emissions. Methods Using panel data for 240 Chinese cities from 2002 to 2023, this study examines the relationships between four climate shocks—extreme high temperature (HTD), extreme low temperature (LTD), extreme rainfall (ERD), and extreme drought (EED)—and agricultural net carbon sinks (NCS), together with the moderating role of agricultural supply chain resilience. NCS is measured as annual crop-biomass carbon fixation net of selected agricultural production emissions, thereby providing an integrated, production-based indicator of agricultural carbon performance. Results Two-way fixed-effects estimates show that all four climate shocks are negatively associated with NCS, and the direction of these relationships remains stable across alternative accounting boundaries, sample adjustments, leave-one-province-out tests, and double machine learning specifications. Agricultural supply chain resilience significantly conditions the climate–carbon relationship. Its resistance, recovery, and reorientation capacities attenuate the adverse associations of high-temperature, extreme-rainfall, and drought shocks with NCS. The low-temperature result further indicates that the effectiveness of resilience depends on matching supply chain functions with the biological mechanisms and intervention windows of specific climate hazards. Heterogeneity analyses confirm that climate sensitivity and resilience requirements vary with regional location, agricultural productivity, modernization, and irrigation conditions. Complementary machine-learning analysis identifies nonlinear predictive patterns and reinforces the importance of differentiated adaptation. Discussion These findings extend agricultural climate research from production and emissions to an integrated carbon-balance perspective and demonstrate that hazard-specific supply chain resilience can support both climate adaptation and low-carbon agricultural development.","引言 极端气候冲击日益挑战农业系统在维持作物碳固定的同时控制生产相关排放的能力。方法 本研究利用2002年至2023年中国240个城市的面板数据，考察四种气候冲击——极端高温（HTD）、极端低温（LTD）、极端降雨（ERD）和极端干旱（EED）——与农业净碳汇（NCS）之间的关系，以及农业供应链韧性的调节作用。NCS以年度作物生物量碳固定量扣除部分农业生产排放量来衡量，从而提供一个基于生产的综合性农业碳绩效指标。结果 双向固定效应估计表明，四种气候冲击均与NCS呈负相关，且这些关系的方向在替代核算边界、样本调整、逐一剔除省份检验和双重机器学习设定下均保持稳定。农业供应链韧性显著调节气候—碳关系。其抵抗能力、恢复能力和重新定向能力减弱了高温、极端降雨和干旱冲击与NCS之间的不利关联。低温结果进一步表明，韧性的有效性取决于供应链功能与特定气候灾害的生物机制及干预窗口的匹配。异质性分析证实，气候敏感性和韧性需求因区域位置、农业生产率、现代化水平和灌溉条件而异。补充性机器学习分析识别出非线性预测模式，并强化了差异化适应的重要性。讨论 这些发现将农业气候研究从生产和排放拓展至综合碳平衡视角，并表明针对特定灾害的供应链韧性能够同时支持气候适应和农业低碳发展。",null,"Frontiers in Sustainable Food Systems","2026-09-25T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,9,1,"基于240城22年面板数据的实证研究，方法扎实、结论稳健，对农业气候适应与低碳发展有参考价值，但属学术论文，公共传播性有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业供应链","低碳农业","气候韧性","农业碳汇",[32,33],"中国 农业净碳汇 极端气候","农业供应链韧性 气候适应","中国农业净碳汇极端气候-3531",0,"10.3389\u002Ffsufs.2026.1940685",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":6,"card":48,"direction":52,"ingested_from":54},"W7214310732",[40,42,45],{"name":41,"orcid":9},"Chunyan Zhao",{"name":43,"orcid":44},"Jiajie Xia","https:\u002F\u002Forcid.org\u002F0000-0003-3225-0154",{"name":46,"orcid":47},"Guoping Ding","https:\u002F\u002Forcid.org\u002F0000-0002-4865-5044",{"tldr":49,"method":50,"finding":51,"direction":52,"opportunity":53},"基于240个城市2002-2023年面板数据，检验四类极端气候冲击对农业净碳汇的影响及供应链韧性的调","双向固定效应模型、双重机器学习，240城市面板数据，农业净碳汇指标。","四类极端气候冲击均降低农业净碳汇，供应链韧性可显著缓解高温、暴雨和干旱的负面影响。","农业绿色发展与碳","可探究不同气候灾害下供应链韧性功能与生物机制匹配的差异化适应策略及非线性预测。","openalex","2026-09-26T23:30:07.569768Z",{"total":57,"page":21,"page_size":57,"items":58},6,[59,87,126,154,196,233],{"id":60,"title":61,"url":62,"summary":63,"summary_zh":9,"content":9,"source_name":64,"source_url":9,"published_at":65,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":66,"score_detail":67,"sources":70,"tags":72,"search_phrases":74,"slug":77,"view_count":35,"doi":9,"paper":78,"created_at":86},1819,"农业低碳转型视角下的数字农技服务体系：基于大晓智能无人农技服务平台的实证分析","https:\u002F\u002Fwww.cet.com.cn\u002Fitpd\u002Fitxw\u002F10537121.shtml","以上海大晓智能无人农技服务平台为研究载体，依托天-空-地-人-机多源感知体系与农业垂直大模型底层架构，系统梳理平台助力农业降碳、土壤碳平衡测算的技术路径与分层服务模式。结合大田生产、农场管理多场景实证数据量化分析平台减污降碳成效，数字农技服务平台构建起\"监测—测算—决策—减排\"闭环管理模式，既能为各地县域、规模化种植基地打造数字化低碳农业提供落地参考，也能够丰富农业碳循环与智慧农业交叉学科的实证研究样本。","中国经济新闻网2026年9月","2026-09-04T00:00:00Z",72,{"impact":17,"substance":68,"depth":17,"authority":13,"freshness":57,"relevant":21,"comment":69},20,"实证分析数字农技平台减碳成效，具行业参考价值。",[71],{"name":64,"url":62},[26,28,73,30],"数字农技",[75,76],"低碳农业 农业碳汇 数字农技 智慧农业","低碳农业 农业碳汇","低碳农业农业碳汇数字农技智慧农业-1819",{"doi":9,"openalex_id":9,"authors":79,"venue":9,"cited_by_count":35,"oa_url":9,"card":80,"direction":52,"ingested_from":85},[],{"tldr":81,"method":82,"finding":83,"direction":52,"opportunity":84},"实证分析数字农技服务平台如何通过多源感知与垂直大模型助力农业降碳，构建闭环管理模式。","依托天-空-地-人-机多源感知体系与农业垂直大模型，结合多场景实证数据量化分析。","平台构建“监测—测算—决策—减排”闭环，有效减污降碳，提供落地参考。","可延伸研究数字农技服务在不同作物、区域下的碳减排差异，以及平台模式与农户采纳行为的耦合机制。","agent","2026-09-07T00:04:41.769592Z",{"id":88,"title":89,"url":90,"summary":91,"summary_zh":92,"content":9,"source_name":93,"source_url":90,"published_at":94,"category":12,"cover_url":9,"hotness":95,"is_selected":14,"score":96,"score_detail":97,"sources":100,"tags":104,"search_phrases":109,"slug":112,"view_count":35,"doi":113,"paper":114,"created_at":125},3357,"AI-Driven Precision Agriculture and Crop Resilience: Integrating Artificial Intelligence, IoT and Remote Sensing for Climate-Resilient Indian Agriculture: A Vision for Viksit Bharat 2047","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22914538","Abstract Agriculture is central to India's economic development, food security, rural employment, and the achievement of the Viksit Bharat@2047 vision. However, Indian agriculture faces increasingly complex challenges, including climate variability, water scarcity, soil degradation, pest and disease outbreaks, fragmented landholdings, market uncertainty, and unequal access to agricultural knowledge. These challenges require a transition from conventional, input-intensive agriculture towards data-driven, resource-efficient, climate-resilient and farmer-centric production systems. Agriculture in India is increasingly affected by climate variability, water scarcity, soil degradation, pest and disease outbreaks, and unpredictable weather conditions. These challenges threaten crop productivity and food security, particularly among small and marginal farmers. Artificial Intelligence (AI), Internet of Things (IoT), remote sensing, and machine learning offer new opportunities to transform conventional agricultural practices into data-driven precision agriculture systems. This paper presents a conceptual framework for AI-driven precision agriculture aimed at improving crop resilience under changing climatic conditions. Artificial Intelligence (AI), combined with precision agriculture, Internet of Things (IoT), remote sensing, satellite imagery, drones, machine learning, robotics and digital public infrastructure, offers significant opportunities to transform Indian agriculture. AI can support crop and yield prediction, disease and pest identification, weather-based advisories, irrigation optimisation, soil management, crop insurance, market intelligence and early-warning systems. The paper also discusses challenges related to digital inclusion, data governance, affordability, AI reliability, farmer skills, privacy and institutional coordination. It argues that India's objective should not simply be the digitisation of agriculture, but the creation of an intelligent, inclusive and resilient agricultural ecosystem in which technology augments farmer knowledge and decision-making. By 2047, India can aspire to establish globally competitive agriculture that produces more with fewer resources, withstands climate shocks, generates higher and more stable farm incomes, and ensures sustainable food and nutritional security.","摘要 农业对印度的经济发展、粮食安全、农村就业以及“发达印度@2047”愿景的实现至关重要。然而，印度农业面临日益复杂的挑战，包括气候变异性、水资源短缺、土壤退化、病虫害暴发、土地持有碎片化、市场不确定性以及农业知识获取不平等。这些挑战要求从传统的投入密集型农业向数据驱动、资源高效、气候韧性且以农民为中心的生产体系转型。印度农业日益受到气候变异性、水资源短缺、土壤退化、病虫害暴发及不可预测天气条件的影响。这些挑战威胁着作物生产力和粮食安全，尤其是对小农和边缘农民而言。人工智能（AI）、物联网（IoT）、遥感和机器学习为将传统农业实践转变为数据驱动的精准农业系统提供了新机遇。本文提出了一个AI驱动的精准农业概念框架，旨在改善气候变化条件下作物的韧性。人工智能（AI）与精准农业、物联网（IoT）、遥感、卫星影像、无人机、机器学习、机器人技术及数字公共基础设施相结合，为改造印度农业提供了重大机遇。AI可支持作物与产量预测、病虫害识别、基于天气的农事建议、灌溉优化、土壤管理、作物保险、市场情报及预警系统。本文还讨论了与数字包容、数据治理、可负担性、AI可靠性、农民技能、隐私及机构协调相关的挑战。文章认为，印度的目标不应仅仅是农业数字化，而应是创建一个智能、包容且有韧性的农业生态系统，使技术增强农民的知识与决策能力。到2047年，印度有望建立具有全球竞争力的农业，以更少资源生产更多产品，抵御气候冲击，创造更高且更稳定的农业收入，并确保可持续的粮食与营养安全。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-30T00:00:00Z",25,69,{"impact":18,"substance":17,"depth":98,"authority":19,"freshness":35,"relevant":21,"comment":99},16,"概念性框架论文，系统梳理AI、IoT与遥感在印度气候韧性农业中的应用与挑战，有参考价值但无实证数据，且发布日期在未来、时效性缺失，暂不宜进入每日精选。",[101,102],{"name":93,"url":90},{"name":93,"url":103},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22914539",[105,26,106,107,29,108],"数字乡村","农业人工智能","农业物联网","遥感监测",[110,111],"印度 精准农业 AI","农业人工智能 农业物联网 数字乡村 智慧农业","印度精准农业AI-3357","10.5281\u002Fzenodo.22914538",{"doi":113,"openalex_id":115,"authors":116,"venue":93,"cited_by_count":35,"oa_url":90,"card":119,"direction":123,"ingested_from":54},"W7214083098",[117],{"name":118,"orcid":9},"Twinkal Prakash Sawant",{"tldr":120,"method":121,"finding":122,"direction":123,"opportunity":124},"提出AI+物联网+遥感驱动的精准农业概念框架，提升印度气候韧性作物生产。","概念框架分析，整合AI、IoT、遥感、卫星、无人机、机器学习与数字公共基础设施。","印度农业应构建智能、包容、有韧性的生态系统，而非仅数字化，以应对气候与资源挑战。","智慧农业 \u002F 农业物联网","可实证检验小农户场景下AI+IoT+遥感集成对作物韧性与收入的实际效果及数字包容机制。","2026-09-24T23:30:13.211525Z",{"id":127,"title":128,"url":129,"summary":130,"summary_zh":9,"content":9,"source_name":131,"source_url":9,"published_at":132,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":133,"sources":135,"tags":137,"search_phrases":141,"slug":144,"view_count":35,"doi":9,"paper":145,"created_at":153},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。","MDPI Sustainability 18(18):9645","2026-09-20T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":134},"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。",[136],{"name":131,"url":129},[26,138,139,29,140],"可解释AI","数字孪生","再生农业",[142,143],"GAFRM 数字孪生 再生农业","GRASI 气候韧性","GAFRM数字孪生再生农业-3050",{"doi":9,"openalex_id":9,"authors":146,"venue":9,"cited_by_count":35,"oa_url":9,"card":147,"direction":151,"ingested_from":85},[],{"tldr":148,"method":149,"finding":150,"direction":151,"opportunity":152},"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。","2026-09-21T00:04:39.490071Z",{"id":155,"title":156,"url":157,"summary":158,"summary_zh":159,"content":9,"source_name":160,"source_url":157,"published_at":161,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":162,"score_detail":163,"sources":168,"tags":170,"search_phrases":174,"slug":177,"view_count":35,"doi":178,"paper":179,"created_at":195},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":98,"substance":164,"depth":165,"authority":19,"freshness":166,"relevant":21,"comment":167},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。",[169],{"name":160,"url":157},[26,29,171,108,172,173],"生物多样性","营养安全","草原保护",[175,176],"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":178,"openalex_id":180,"authors":181,"venue":160,"cited_by_count":35,"oa_url":189,"card":190,"direction":123,"ingested_from":54},"W7213261782",[182,184,187],{"name":183,"orcid":9},"Kwame Anokye",{"name":185,"orcid":186},"Huang Rong","https:\u002F\u002Forcid.org\u002F0000-0003-1039-0914",{"name":188,"orcid":9},"Zhen-fen Zhang","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2666154326006733\u002Fpdf",{"tldr":191,"method":192,"finding":193,"direction":52,"opportunity":194},"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。","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。","2026-09-17T23:30:10.652175Z",{"id":197,"title":198,"url":199,"summary":200,"summary_zh":201,"content":9,"source_name":202,"source_url":199,"published_at":203,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":204,"score_detail":205,"sources":209,"tags":211,"search_phrases":215,"slug":218,"view_count":35,"doi":219,"paper":220,"created_at":232},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":18,"substance":206,"depth":207,"authority":19,"freshness":166,"relevant":21,"comment":208},23,19,"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。",[210],{"name":202,"url":199},[26,29,212,213,214],"农光互补","设施园艺","光伏农业",[216,217],"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":219,"openalex_id":221,"authors":222,"venue":202,"cited_by_count":35,"oa_url":226,"card":227,"direction":123,"ingested_from":54},"W7213346284",[223],{"name":224,"orcid":225},"Jadala Shankaraswamy","https:\u002F\u002Forcid.org\u002F0000-0002-5623-6384","https:\u002F\u002Fijoear.com\u002Fassets\u002Farticles_menuscripts\u002Ffile\u002FIJOEAR-SEP-2026-8.pdf",{"tldr":228,"method":229,"finding":230,"direction":52,"opportunity":231},"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":234,"title":235,"url":236,"summary":237,"summary_zh":238,"content":9,"source_name":93,"source_url":236,"published_at":94,"category":12,"cover_url":9,"hotness":239,"is_selected":14,"score":240,"score_detail":241,"sources":244,"tags":252,"search_phrases":254,"slug":257,"view_count":21,"doi":258,"paper":259,"created_at":272},2313,"Artificial Intelligence For Climate-Resilient Plants: Emerging Ai Approaches For Predicting Drought, Salinity And Temperature Stress.","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22724439","Abstract Climate change is creating serious problems for agriculture by increasing drought, soil salinity, and extreme temperatures. These stresses affect plant growth, development, crop yield, and global food security. Traditional methods used to study plant responses to stress are often time-consuming, labour-intensive, and difficult to use for large numbers of plants. Artificial intelligence (AI) is becoming an important tool for studying and predicting plant responses to changing environmental conditions. AI can analyse large amounts of data collected through plant phenotyping, remote sensing, environmental sensors, and molecular studies. This review focuses on recent AI approaches used to predict plant responses to drought, salinity, and temperature stress from 2016 to 2026. Machine learning, deep learning, computer vision, thermal imaging, and hyperspectral imaging can help in early detection and prediction of plant stress. Recent developments are moving beyond simple stress identification towards predicting crop performance and stress tolerance. The combination of AI with high-throughput phenotyping and multi-omics can help identify stress-tolerant crop varieties and support climate-resilient breeding. However, challenges related to data quality, limited field validation, unclear model predictions, and poor performance across different environments still remain. Future research should develop reliable and explainable AI models for sustainable agriculture and improved crop production under climate change.","摘要 气候变化正通过加剧干旱、土壤盐渍化和极端温度，给农业带来严重问题。这些胁迫影响植物生长、发育、作物产量和全球粮食安全。用于研究植物胁迫响应的传统方法往往耗时、费力，且难以应用于大量植物。人工智能（AI）正成为研究和预测植物对环境条件变化响应的重要工具。AI可以分析通过植物表型分析、遥感、环境传感器和分子研究收集的大量数据。本文综述聚焦于2016年至2026年间用于预测植物对干旱、盐分和温度胁迫响应的近期AI方法。机器学习、深度学习、计算机视觉、热成像和高光谱成像有助于植物胁迫的早期检测和预测。近期发展正超越简单的胁迫识别，转向预测作物表现和胁迫耐受性。AI与高通量表型分析和多组学的结合有助于识别耐胁迫作物品种，并支持气候韧性育种。然而，数据质量、田间验证有限、模型预测不明确以及在不同环境中表现不佳等挑战仍然存在。未来研究应开发可靠且可解释的AI模型，以促进气候变化下的可持续农业和作物生产提升。",55,71,{"impact":17,"substance":68,"depth":165,"authority":19,"freshness":242,"relevant":21,"comment":243},3,"系统综述AI预测干旱、盐碱与高温胁迫的研究进展，方法覆盖机器学习、深度学习与高光谱成像，对气候韧性育种有参考价值，但属综述类论文且距发布已逾两周，时效性偏弱。",[245,246,248,250],{"name":93,"url":236},{"name":93,"url":247},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22724438",{"name":93,"url":249},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22767935",{"name":93,"url":251},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22767934",[26,106,29,108,253],"作物育种",[255,256],"农业人工智能 作物育种 智慧农业 气候韧性","农业人工智能 作物育种","农业人工智能作物育种智慧农业气候韧性-2313","10.5281\u002Fzenodo.22724439",{"doi":258,"openalex_id":260,"authors":261,"venue":93,"cited_by_count":35,"oa_url":236,"card":266,"direction":270,"ingested_from":54},"W7212377743",[262,264],{"name":263,"orcid":9},"Aruna Nangare",{"name":265,"orcid":9},"Vaishali Wankhede",{"tldr":267,"method":268,"finding":269,"direction":270,"opportunity":271},"综述2016-2026年AI预测植物干旱、盐碱和温度胁迫响应的进展。","机器学习、深度学习、计算机视觉、热成像与高光谱成像结合表型组和多组学数据。","AI已从简单胁迫识别转向预测作物表现与耐逆性，但数据质量、田间验证和跨环境泛化仍是瓶颈。","农业遥感与作物表型","可解释AI与高通量表型、多组学融合，用于跨环境耐逆品种预测与气候韧性育种。","2026-09-13T23:30:17.636747Z"]