[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3914":3,"related-3914":87},{"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":28,"primary_topic":34,"secondary_topics":40,"classification":41,"search_phrases":67,"slug":70,"view_count":62,"doi":71,"paper":72,"created_at":86},3914,"Artificial Intelligence for Sustainable Development: Opportunities, Innovations, and Systemic Risks","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23052318","Abstract The paper considers the SDGs as a broad organizing framework, with particular attention to SDG 2 (Zero Hunger), SDG 6 (Clean Water and Sanitation), SDG 7 (Affordable and Clean Energy), SDG 12 (Responsible Consumption and Production), SDG 13 (Climate Action), and SDG 15 (Life on Land). The outcomes of AI applications may vary with local infrastructure, data quality, energy sources, water availability, and institutional capacity. Any numerical claims drawn from prior studies should therefore be interpreted in the context and scope of the original source. This paper is a qualitative review based on secondary information and published literature concerning AI and sustainable development. It uses a descriptive and thematic approach to organize the discussion into application areas, enabling technologies, potential benefits, and implementation risks. The principal themes are precision agriculture, renewable energy integration, circular logistics, data infrastructure, algorithmic fairness, and environmental governance. No new field survey, experiment, or original statistical dataset was collected for this paper. Accordingly, the discussion is interpretive and should not be treated as a causal or quantitative evaluation of a particular AI system.","本文将可持续发展目标视为一个广泛的组织框架，特别关注可持续发展目标2（零饥饿）、可持续发展目标6（清洁饮水和卫生设施）、可持续发展目标7（经济适用的清洁能源）、可持续发展目标12（负责任消费和生产）、可持续发展目标13（气候行动）以及可持续发展目标15（陆地生物）。人工智能应用的成效可能因当地基础设施、数据质量、能源来源、水资源可得性和机构能力而异。因此，凡引自既有研究的数值性论断，均应结合原始来源的语境和范围加以解读。本文是一项基于二手信息和已发表文献的定性综述，涉及人工智能与可持续发展。文章采用描述性和主题式方法，将讨论组织为应用领域、使能技术、潜在效益和实施风险。主要主题包括精准农业、可再生能源整合、循环物流、数据基础设施、算法公平性和环境治理。本文未开展新的实地调查、实验，也未收集原始统计数据集。因此，相关讨论属于阐释性分析，不应被视为对某一特定人工智能系统的因果性或定量评估。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-09-30T00:00:00Z","论文",25,false,69,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,14,15,13,9,1,"Zenodo 发布的 AI 与可持续发展定性综述，覆盖精准农业、算法公平等议题，但无新数据与实证，属框架性梳理，参考价值中等。",[25,26],{"name":10,"url":6},{"name":10,"url":27},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23052317",[29,30,31,32,33],"智慧农业","农业人工智能","可持续发展","精准农业","算法公平",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":42,"locked":14,"version":43,"method":44,"evidence":45},"classified","2026-10-22.2","rules",{"candidates":46,"agricultural_evidence":48},[47,53,60],{"code":35,"score":21,"supported":48,"title":49,"abstract":51,"tags":52},true,[50],"artificial intelligence",[],[30],{"code":54,"score":55,"supported":48,"title":56,"abstract":57,"tags":59},"I1",8,[],[32,58],"precision agriculture",[29,32],{"code":61,"score":62,"supported":48,"title":63,"abstract":64,"tags":66},"H3",3,[],[65],"水资源",[],[68,69],"AI 可持续农业 SDGs","农业人工智能 可持续发展 智慧农业 算法公平","AI可持续农业SDGs-3914","10.5281\u002Fzenodo.23052318",{"doi":71,"openalex_id":73,"authors":74,"venue":10,"cited_by_count":77,"oa_url":6,"card":78,"direction":84,"ingested_from":85},"W7214923133",[75],{"name":76,"orcid":9},"Snehal Ramesh Patil",0,{"tldr":79,"method":80,"finding":81,"direction":82,"opportunity":83},"综述AI在可持续发展目标中的应用机遇、创新与系统性风险。","基于二手文献的定性主题综述，无新数据或实验。","AI效益受本地基础设施、数据质量与制度能力制约，需情境化解读。","农业绿色发展与碳","可针对特定区域开展AI农业应用的实证量化评估，弥补综述缺乏因果验证的空白。","农业人工智能与决策模型","openalex","2026-10-01T23:30:49.083263Z",{"total":88,"page":22,"page_size":88,"items":89},6,[90,178,283,364,442,508],{"id":91,"title":92,"url":93,"summary":94,"summary_zh":95,"content":9,"source_name":96,"source_url":93,"published_at":97,"category":12,"cover_url":9,"hotness":98,"is_selected":14,"score":99,"score_detail":100,"sources":104,"tags":106,"primary_topic":108,"secondary_topics":109,"classification":116,"search_phrases":153,"slug":156,"view_count":22,"doi":157,"paper":158,"created_at":177},2642,"Artificial Intelligence for Environmental Resilience and Sustainable Innovation","https:\u002F\u002Fdoi.org\u002F10.68012\u002Fair.v1i2.239","Environmental challenges such as climate change, resource depletion, biodiversity loss, and increasing pollution require innovative and adaptive solutions to strengthen environmental resilience. Artificial intelligence (AI) has emerged as a transformative technology capable of improving environmental monitoring, prediction, and decision-making processes. This study examines the role of artificial intelligence in enhancing environmental resilience and promoting sustainable innovation across various sectors, including natural resource management, climate adaptation, waste management, environmental conservation, and smart environmental systems. This study adopts a Systematic Literature Review (SLR) methodology integrated with bibliometric analysis and thematic content analysis to identify research trends, conceptual developments, and emerging themes related to AI applications in environmental sustainability. The selected studies were analyzed using VOSviewer, Tableau, and Microsoft Excel to visualize knowledge structures, thematic relationships, and research patterns. The findings reveal that AI significantly contributes to environmental resilience by enabling advanced data analysis, early detection of environmental risks, predictive assessment, resource optimization, and evidence-based decision-making. Furthermore, AI-driven innovations support sustainable practices through renewable energy optimization, precision agriculture, waste management improvement, water resource monitoring, and smart city development. However, challenges remain regarding data quality, technological accessibility, computational requirements, ethical considerations, and responsible governance. The study concludes that AI has substantial potential to accelerate environmental resilience and sustainable innovation when supported by appropriate policies, stakeholder collaboration, and long-term sustainability strategies. By integrating intelligent technologies with responsible environmental management approaches, AI can serve as a strategic enabler in addressing complex ecological challenges and supporting a more adaptive, resilient, and sustainable future.","气候变化、资源枯竭、生物多样性丧失和污染加剧等环境挑战，需要创新性和适应性的解决方案来增强环境韧性。人工智能（AI）已成为一种变革性技术，能够改善环境监测、预测和决策过程。本研究探讨了人工智能在增强环境韧性和促进各领域可持续创新中的作用，涵盖自然资源管理、气候适应、废物管理、环境保护和智能环境系统等。本研究采用系统性文献综述（SLR）方法，并结合文献计量分析和主题内容分析，以识别与人工智能在环境可持续性中应用相关的研究趋势、概念发展和新兴主题。所选研究使用VOSviewer、Tableau和Microsoft Excel进行分析，以可视化知识结构、主题关系和研究模式。研究结果表明，人工智能通过实现先进数据分析、环境风险早期检测、预测性评估、资源优化和循证决策，显著促进了环境韧性。此外，人工智能驱动的创新通过可再生能源优化、精准农业、废物管理改进、水资源监测和智慧城市发展，支持可持续实践。然而，在数据质量、技术可及性、计算需求、伦理考量和负责任治理方面仍存在挑战。研究结论认为，在适当的政策、利益相关者协作和长期可持续战略支持下，人工智能具有加速环境韧性和可持续创新的巨大潜力。通过将智能技术与负责任的环境管理方法相结合，人工智能可以作为应对复杂生态挑战、支持更具适应性、韧性和可持续未来的战略赋能者。","AI Innovation and Resilience for the Environment (AIR)","2026-09-15T00:00:00Z",10,72,{"impact":17,"substance":17,"depth":101,"authority":102,"freshness":55,"relevant":22,"comment":103},16,12,"系统综述类论文，梳理AI在环境韧性与可持续创新中的应用，涵盖精准农业、水资源监测等方向，方法规范但结论偏综述性，对农业信息化有参考价值。",[105],{"name":96,"url":93},[29,30,31,32,107],"环境韧性",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[110],{"code":111,"slug":112,"name":113,"path":114,"parent_code":115},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":42,"locked":14,"version":43,"method":44,"evidence":117},{"candidates":118,"agricultural_evidence":48},[119,123,130,134,139,146],{"code":35,"score":102,"supported":48,"title":120,"abstract":121,"tags":122},[50],[50],[30],{"code":111,"score":21,"supported":48,"title":124,"abstract":125,"tags":129},[],[126,127,128],"气候变化","气候适应","climate change",[],{"code":54,"score":55,"supported":48,"title":131,"abstract":132,"tags":133},[],[32,58],[29,32],{"code":61,"score":88,"supported":48,"title":135,"abstract":136,"tags":138},[],[65,137],"water resource",[],{"code":140,"score":88,"supported":48,"title":141,"abstract":142,"tags":145},"H6",[],[143,144],"生物多样性","biodiversity",[],{"code":147,"score":88,"supported":48,"title":148,"abstract":149,"tags":152},"I7",[],[150,151],"文献计量","bibliometric",[],[154,155],"农业人工智能 可持续发展 智慧农业 环境韧性","农业人工智能 可持续发展","农业人工智能可持续发展智慧农业环境韧性-2642","10.68012\u002Fair.v1i2.239",{"doi":157,"openalex_id":159,"authors":160,"venue":96,"cited_by_count":77,"oa_url":93,"card":171,"direction":176,"ingested_from":85},"W7213274173",[161,163,166,169],{"name":162,"orcid":9},"Sri Poedji Lestari",{"name":164,"orcid":165},"Rosa Lesmana","https:\u002F\u002Forcid.org\u002F0009-0002-5308-6023",{"name":167,"orcid":168},"Sabil Maulana Fauzi","https:\u002F\u002Forcid.org\u002F0009-0003-1833-1027",{"name":170,"orcid":9},"Alexander Johnson Johnson",{"tldr":172,"method":173,"finding":174,"direction":82,"opportunity":175},"系统综述AI在环境韧性与可持续创新中的应用、趋势与挑战。","系统文献综述结合文献计量与主题分析，用VOSviewer等可视化。","AI通过数据分析、风险预警和资源优化显著增强环境韧性，但存在数据与治理挑战。","可聚焦AI在精准农业与碳减排协同中的实证研究，弥补数据质量与治理机制空白。","智慧农业 \u002F 农业物联网","2026-09-16T23:30:11.835233Z",{"id":179,"title":180,"url":181,"summary":182,"summary_zh":183,"content":9,"source_name":184,"source_url":181,"published_at":185,"category":12,"cover_url":9,"hotness":98,"is_selected":14,"score":186,"score_detail":187,"sources":190,"tags":192,"primary_topic":195,"secondary_topics":196,"classification":206,"search_phrases":259,"slug":262,"view_count":77,"doi":263,"paper":264,"created_at":282},5553,"Artificial Intelligence–Driven Precision Agriculture Systems: Engineering Frameworks for Climate-Resilient Food Production","https:\u002F\u002Fdoi.org\u002F10.53982\u002Fajerd.2026.01.14-j","Climate change, soil issues, water scarcity, and the increasing need for food worldwide mean we need smart farming systems that can manage resources flexibly. Old farming methods used the same inputs and simple rules for decisions. AI can help us manage resources better by adapting and optimizing them as we deal with complex environmental factors. This paper looks closely at AI-powered precision agriculture systems designed to make food production more resilient to changing weather. The authors reviewed research from 2021 to 2026 in agricultural engineering, computer engineering, and environmental systems. The authors examined how machine learning, deep learning, reinforcement learning, computer vision, robotics, and IoT sensors are used for predicting crop yields, spotting diseases, improving irrigation, managing nutrients, and automating field tasks. Our analysis shows that hybrid deep learning models (CNN-LSTM) are better than single models for predicting crop yields over space and time. Reinforcement learning algorithms are also better at adjusting irrigation cycles when weather is unpredictable. Using edge computing for system design reduces delays and improves real-time decision-making compared to traditional cloud-based systems. However, there are still big challenges with understanding how AI models work, having enough diverse data, ensuring cybersecurity, setting common standards, and making these systems affordable, especially in developing countries. To tackle these problems, we suggest a framework that combines AI analysis, distributed sensor networks, adaptive controls, and climate risk assessments. The authors provided critical insights into essential research gaps and outline a technological roadmap towards achieving sustainable, resilient and intelligent agricultural systems.","气候变化、土壤问题、水资源短缺以及全球日益增长的粮食需求，意味着我们需要能够灵活管理资源的智能农业系统。传统农业方法对相同的投入采用固定模式，并依赖简单规则进行决策。人工智能可以帮助我们更好地管理资源，通过适应和优化资源利用来应对复杂的环境因素。本文深入探讨了人工智能驱动的精准农业系统，旨在使粮食生产对不断变化的天气更具韧性。作者综述了2021年至2026年间农业工程、计算机工程和环境系统领域的研究。作者考察了机器学习、深度学习、强化学习、计算机视觉、机器人技术及物联网传感器如何被用于作物产量预测、病害识别、灌溉优化、养分管理和田间作业自动化。我们的分析表明，混合深度学习模型（CNN-LSTM）在时空作物产量预测方面优于单一模型。在天气不可预测的情况下，强化学习算法在调整灌溉周期方面也表现更优。与传统基于云的系统相比，采用边缘计算进行系统设计可降低延迟并改善实时决策能力。然而，在理解人工智能模型的工作原理、获取足够多样化的数据、确保网络安全、制定通用标准以及使这些系统具备可负担性方面，仍存在重大挑战，尤其是在发展中国家。为应对这些问题，我们提出了一个融合人工智能分析、分布式传感器网络、自适应控制和气候风险评估的框架。作者对关键研究空白提供了重要见解，并勾勒了实现可持续、韧性和智能农业系统的技术路线图。","ABUAD Journal of Engineering Research and Development (AJERD)","2026-10-21T00:00:00Z",80,{"impact":17,"substance":188,"depth":17,"authority":20,"freshness":21,"relevant":22,"comment":189},22,"系统综述AI驱动精准农业的工程框架与技术路线，方法梳理扎实、结论有参考价值，但属综述类论文，产业落地影响有限。",[191],{"name":184,"url":181},[29,30,193,32,194],"物联网","气候韧性",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[197,202],{"code":198,"slug":199,"name":200,"path":201,"parent_code":39},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":61,"slug":203,"name":204,"path":205,"parent_code":115},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":42,"locked":14,"version":43,"method":44,"evidence":207},{"candidates":208,"agricultural_evidence":48},[209,219,223,230,235,240,247,253],{"code":35,"score":17,"supported":48,"title":210,"abstract":211,"tags":218},[50],[212,213,214,215,216,217],"深度学习","机器学习","计算机视觉","machine learning","deep learning","computer vision",[30],{"code":54,"score":101,"supported":48,"title":220,"abstract":221,"tags":222},[58],[32,58],[29,32],{"code":198,"score":98,"supported":48,"title":224,"abstract":225,"tags":229},[],[193,226,227,228],"边缘计算","IoT","edge computing",[193],{"code":61,"score":21,"supported":48,"title":231,"abstract":232,"tags":234},[],[233,65,203],"灌溉",[],{"code":111,"score":236,"supported":48,"title":237,"abstract":238,"tags":239},7,[],[126,128],[194],{"code":241,"score":88,"supported":48,"title":242,"abstract":243,"tags":246},"H2",[],[244,245],"土壤","soil",[],{"code":248,"score":62,"supported":48,"title":249,"abstract":250,"tags":252},"A7",[],[251],"间作",[],{"code":254,"score":62,"supported":48,"title":255,"abstract":256,"tags":258},"F3",[],[257],"养分管理",[],[260,261],"AI 精准农业 气候韧性","CNN-LSTM 作物产量预测","AI精准农业气候韧性-5553","10.53982\u002Fajerd.2026.01.14-j",{"doi":263,"openalex_id":265,"authors":266,"venue":184,"cited_by_count":77,"oa_url":181,"card":277,"direction":176,"ingested_from":85},"W7223121456",[267,270,272,274],{"name":268,"orcid":269},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":271,"orcid":9},"Oluwadare OYEBODE",{"name":273,"orcid":9},"Iretiayo Adelaiye",{"name":275,"orcid":276},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":278,"method":279,"finding":280,"direction":84,"opportunity":281},"综述2021-2026年AI驱动精准农业系统，提出气候韧性粮食生产工程框架。","综述机器学习、深度学习、强化学习、计算机视觉、机器人与物联网在精准农业中的应用。","CNN-LSTM混合模型和强化学习在产量预测与灌溉调度上优于单一方法，边缘计算提升实时性。","可研究边缘计算与强化学习结合的轻量级灌溉决策系统，并探索发展中国家低成本部署与标准化路径。","2026-10-22T11:20:18.151376Z",{"id":284,"title":285,"url":286,"summary":287,"summary_zh":288,"content":9,"source_name":289,"source_url":286,"published_at":290,"category":12,"cover_url":9,"hotness":98,"is_selected":48,"score":291,"score_detail":292,"sources":297,"tags":299,"primary_topic":302,"secondary_topics":303,"classification":304,"search_phrases":332,"slug":335,"view_count":322,"doi":336,"paper":337,"created_at":363},5382,"Modeling the global potential of smart farming innovations","https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.2530081123","Smart farming innovations can improve agricultural productivity, resilience, and environmental sustainability, yet their real-world impact hinges on their diffusion dynamics, which in turn depend on their economic viability. We present here a global assessment of the profitability of smart farming innovation across croplands at a spatial resolution of ~5 km from the farmers’ and the societal perspectives. We cover application mapping for precision agriculture, automated application of inputs, autonomous weeding machines, GPS steering assistance systems, and autonomous irrigation systems. For this, we compiled a globally comprehensive dataset of smart farming innovation adoption locations (N = 4,422) based on a combination of a survey (N = 2,677) and a meta-analysis (N = 1,745) and combined them with remotely sensed and statistical data to train an ensemble of five geospatial machine learning models (generalized linear models, random forests, support vector machines, maximum entropy, and boosted regression trees), which show the global distribution of the different innovations’ profitability and its overlap with major environmental issues the innovations can help to mitigate. Simulations of future socioeconomic pathways combined with assumptions about future agri-environmental policies reveal that, especially in middle-income countries, the potential of smart farming will grow over time.","智慧农业创新能够提升农业生产力、韧性和环境可持续性，然而其现实影响取决于扩散动态，而扩散动态又取决于其经济可行性。本文从农民和社会两个视角，以约5公里的空间分辨率对全球农田范围内智慧农业创新的盈利能力进行了评估。研究涵盖精准农业的应用测绘、投入品的自动化施用、自主除草机械、GPS转向辅助系统以及自主灌溉系统。为此，我们基于一项调查（N = 2,677）和一项元分析（N = 1,745）相结合，编制了全球范围内较为全面的智慧农业创新采用地点数据集（N = 4,422），并将其与遥感和统计数据相结合，用于训练由五种地理空间机器学习模型（广义线性模型、随机森林、支持向量机、最大熵和提升回归树）组成的集成模型，这些模型揭示了不同创新盈利能力的全球分布及其与这些创新有助于缓解的主要环境问题的重叠情况。对未来社会经济路径的模拟结合对未来农业环境政策的假设表明，智慧农业的潜力将随时间推移而增长，尤其是在中等收入国家。","Proceedings of the National Academy of Sciences","2026-10-19T00:00:00Z",93,{"impact":293,"substance":294,"depth":295,"authority":19,"freshness":21,"relevant":22,"comment":296},26,24,19,"PNAS 全球尺度智慧农业盈利性评估，数据规模与方法新颖，结论对智慧农业推广具政策参考价值，值得进入每日精选。",[298],{"name":289,"url":286},[29,30,32,300,301],"遥感","全球评估",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":305},{"candidates":306,"agricultural_evidence":48},[307,312,316,320,326],{"code":54,"score":308,"supported":48,"title":309,"abstract":310,"tags":311},11,[],[29,32,58],[29,32],{"code":35,"score":236,"supported":48,"title":313,"abstract":314,"tags":315},[],[213,215],[30],{"code":61,"score":88,"supported":48,"title":317,"abstract":318,"tags":319},[],[233,203],[],{"code":321,"score":322,"supported":48,"title":323,"abstract":324,"tags":325},"I3",4,[],[300],[300],{"code":327,"score":62,"supported":48,"title":328,"abstract":329,"tags":331},"F4",[],[330],"除草",[],[333,334],"农业人工智能 全球评估 智慧农业 精准农业","农业人工智能 全球评估","农业人工智能全球评估智慧农业精准农业-5382","10.1073\u002Fpnas.2530081123",{"doi":336,"openalex_id":338,"authors":339,"venue":289,"cited_by_count":77,"oa_url":286,"card":358,"direction":176,"ingested_from":85},"W7222974188",[340,343,346,349,352,355],{"name":341,"orcid":342},"Shahrear Roman","https:\u002F\u002Forcid.org\u002F0009-0006-5659-4292",{"name":344,"orcid":345},"Hadi Hadi","https:\u002F\u002Forcid.org\u002F0000-0002-2410-9751",{"name":347,"orcid":348},"Wyclife Agumba Oluoch","https:\u002F\u002Forcid.org\u002F0000-0003-0803-9279",{"name":350,"orcid":351},"David Zilberman","https:\u002F\u002Forcid.org\u002F0000-0001-5743-5134",{"name":353,"orcid":354},"Robert Finger","https:\u002F\u002Forcid.org\u002F0000-0002-0634-5742",{"name":356,"orcid":357},"David Wuepper","https:\u002F\u002Forcid.org\u002F0000-0002-1344-6023",{"tldr":359,"method":360,"finding":361,"direction":176,"opportunity":362},"全球5公里尺度评估五类智慧农业创新在农田的盈利性与扩散潜力。","整合4422个采用点调查与元分析数据，用五种地理空间机器学习集成建模。","智慧农业盈利性空间差异显著，中等收入国家未来潜力将随社会经济路径增长。","可结合区域政策与农户异质性，细化盈利性预测并评估环境协同效益。","2026-10-20T23:30:08.754594Z",{"id":365,"title":366,"url":367,"summary":368,"summary_zh":369,"content":9,"source_name":370,"source_url":367,"published_at":371,"category":12,"cover_url":9,"hotness":98,"is_selected":14,"score":372,"score_detail":373,"sources":377,"tags":379,"primary_topic":381,"secondary_topics":382,"classification":383,"search_phrases":399,"slug":402,"view_count":77,"doi":403,"paper":404,"created_at":441},5306,"A growing challenge: stakeholder perceptions of AI and ML applications in U.S. agriculture","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10459-x","Abstract Context The global population is expected to exceed ten billion by 2050 and food demand is projected to increase by up to 100% in the same timeframe. Agricultural management must adapt to meet the increasing global demands. Artificial intelligence (AI) and machine learning (ML) technologies can help the agricultural community respond to these needs. Aims An online survey and follow-up interviews were conducted to examine patterns of use, perceptions, and barriers among early adopting stakeholders regarding AI and ML. Methods The online survey with agricultural stakeholders (n=129) included questions regarding the participants’ views on incorporating AI and ML techniques into farming practices and individual follow-up interviews with agricultural stakeholders (n=10) were conducted to better understand their concerns toward technology use and adoption of AI-enabled tools and technologies. Key results Using a mixed-methods approach, four challenges to establishing AI-\u002FML-enabled technology in agriculture were identified. Namely, challenges related to (1) systems of systems (SoS), (2) sub-seasonal to seasonal weather (S2S) forecasts, (3) precision soil management, and (4) policy and economics. Each challenge was discussed in the context of recent literature. Conclusion Concerns were voiced clustered around four broad research themes. This brief communication brings the perspective of agricultural stakeholders to the discussion of the potential of AI\u002FML-enabled technology and the direction of their development in the agricultural sector. Implications Data collected in 2021, establish an important baseline for the early phase of AI\u002FML use in U.S. agriculture, enabling comparison of future perceptions and developments in the sector. Impact This brief communication describes a survey and follow-up interviews conducted with agricultural stakeholders on factors influencing their willingness and reluctance to incorporate AI and ML techniques into farming practices. The findings, summarized and discussed as four key challenges for AI and ML in the agricultural sector, have the potential to streamline research activities.","摘要\n\n背景 预计到2050年全球人口将超过100亿，同期粮食需求预计将增加多达100%。农业管理必须作出调整以满足不断增长的全球需求。人工智能（AI）和机器学习（ML）技术可帮助农业界应对这些需求。\n\n目的 通过开展在线调查和后续访谈，考察早期采用相关技术的利益相关者在AI和ML方面的使用模式、认知及障碍。\n\n方法 针对农业利益相关者（n=129）的在线调查包含有关参与者对将AI和ML技术纳入农业实践看法的问题，并对农业利益相关者（n=10）进行个体后续访谈，以更好地了解他们对技术使用及采用AI赋能工具和技术的顾虑。\n\n主要结果 采用混合方法，识别出在农业中建立AI\u002FML赋能技术面临的四项挑战，即与以下方面相关的挑战：（1）系统之系统（SoS），（2）次季节至季节天气（S2S）预报，（3）精准土壤管理，以及（4）政策与经济。每项挑战均结合近期文献进行了讨论。\n\n结论 所表达的顾虑集中于四个广泛的研究主题。本简报将农业利益相关者的视角引入关于AI\u002FML赋能技术潜力及其在农业部门发展方向讨论中。\n\n意义 2021年收集的数据为美国农业中AI\u002FML应用的早期阶段建立了重要基线，有助于比较该部门未来的认知和发展。\n\n影响 本简报描述了针对农业利益相关者开展的一项调查和后续访谈，内容涉及影响其愿意和不愿将AI和ML技术纳入农业实践的因素。研究结果总结并讨论为农业部门AI和ML的四项关键挑战，有望优化研究活动。","Precision Agriculture","2026-10-18T00:00:00Z",77,{"impact":17,"substance":374,"depth":375,"authority":18,"freshness":55,"relevant":22,"comment":376},20,17,"基于129份问卷与10次访谈的混合方法研究，系统梳理美国农业AI\u002FML应用的四大挑战，为早期采用者认知提供基线数据，对智慧农业研究有参考价值。",[378],{"name":370,"url":367},[29,30,213,32,380],"农户认知",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":384},{"candidates":385,"agricultural_evidence":48},[386,390,394],{"code":35,"score":308,"supported":48,"title":387,"abstract":388,"tags":389},[],[213,215,50],[30,213],{"code":241,"score":88,"supported":48,"title":391,"abstract":392,"tags":393},[],[244,245],[],{"code":54,"score":395,"supported":14,"title":396,"abstract":397,"tags":398},2,[],[],[29,32],[400,401],"美国农业 AI ML 调查","农业人工智能 应用障碍","美国农业AIML调查-5306","10.1007\u002Fs11119-026-10459-x",{"doi":403,"openalex_id":405,"authors":406,"venue":370,"cited_by_count":77,"oa_url":367,"card":436,"direction":84,"ingested_from":85},"W7222866367",[407,410,413,416,419,422,425,428,430,433],{"name":408,"orcid":409},"Jennifer Koch","https:\u002F\u002Forcid.org\u002F0000-0002-7067-2705",{"name":411,"orcid":412},"Andrew Fox","https:\u002F\u002Forcid.org\u002F0000-0001-6719-8902",{"name":414,"orcid":415},"Richa Bhattarai","https:\u002F\u002Forcid.org\u002F0000-0003-3827-7761",{"name":417,"orcid":418},"Hank C. Jenkins‐Smith","https:\u002F\u002Forcid.org\u002F0000-0002-5204-2726",{"name":420,"orcid":421},"Jonathan McFadden","https:\u002F\u002Forcid.org\u002F0000-0003-3762-1428",{"name":423,"orcid":424},"Audrey Reinert","https:\u002F\u002Forcid.org\u002F0000-0001-8951-702X",{"name":426,"orcid":427},"Juan Sesmero","https:\u002F\u002Forcid.org\u002F0000-0002-2669-4536",{"name":429,"orcid":9},"M. Z. Walkup",{"name":431,"orcid":432},"Wolfgang Jentner","https:\u002F\u002Forcid.org\u002F0000-0003-1045-6020",{"name":434,"orcid":435},"David S. Ebert","https:\u002F\u002Forcid.org\u002F0000-0001-6177-1296",{"tldr":437,"method":438,"finding":439,"direction":84,"opportunity":440},"通过问卷与访谈调查美国农业利益相关者对AI\u002FML应用的认知与障碍。","在线问卷129人加10人深度访谈的混合方法。","识别出系统之系统、次季节天气预报、精准土壤管理和政策经济四大挑战。","可针对四类障碍开展分区域、分作物类型的采纳意愿追踪与干预研究。","2026-10-19T23:30:02.345893Z",{"id":443,"title":444,"url":445,"summary":446,"summary_zh":447,"content":9,"source_name":448,"source_url":445,"published_at":449,"category":12,"cover_url":9,"hotness":98,"is_selected":14,"score":450,"score_detail":451,"sources":454,"tags":456,"primary_topic":459,"secondary_topics":460,"classification":461,"search_phrases":472,"slug":475,"view_count":77,"doi":476,"paper":477,"created_at":507},5153,"Real-Time UAV scheduling under hazardous weather conditions using an integrated fuzzy logic and machine learning framework","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43926-026-00489-2","The use of Unmanned Aerial Vehicles (UAVs) in precision agriculture, specifically palm farm management, is perpetually challenged by dynamic and extreme weather. From sudden wind gusts and thermal stress to destructive regional events like sandstorms and flash floods, these hazards severely constrict operational windows. This paper proposes a robust, hybrid scheduling framework that integrates fuzzy logic inference with an ensemble of machine learning classifiers to enhance real-time UAV flight reliability. The primary novelty of this research lies not in a new mathematical paradigm, but in its specialized application: the holistic integration of region-specific agricultural hazards into a real-time, decision-fusion architecture. The framework classifies takeoff viability into three categories (SAFE, CAUTION, and UNSAFE) automatically issuing alerts to trigger preventative measures and preserve hardware. By leveraging both short-term farm datasets and a decade of historical weather records, the fuzzy logic component reacts to rapid environmental shifts while establishing safety baselines. Simultaneously, tree-based ensemble models (Gradient Boosting, XGBoost, LightGBM, and CatBoost) analyze non-linear atmospheric interactions, achieving up to 99.67% accuracy in predicting safe windows. Comprehensive sensitivity analyses, including evaluations without SMOTE and seasonal temporal holdout validations, confirm the model’s robustness against class imbalance and unseen extremes. Empirical benchmarking demonstrates the framework's superiority over existing hybrid approaches, highlighting the necessity of combining adaptive fuzzy reasoning with deep historical learning for smart farming.","无人机（UAV）在精准农业尤其是棕榈农场管理中的应用，始终受到动态极端天气的困扰。从突发阵风和热应激到沙尘暴和山洪等破坏性区域事件，这些灾害严重压缩了作业窗口。本文提出了一种稳健的混合调度框架，将模糊逻辑推理与机器学习分类器集成相结合，以提升无人机实时飞行的可靠性。本研究的主要创新点不在于新的数学范式，而在于其专门化应用：将区域特定的农业灾害整体集成到实时决策融合架构中。该框架将起飞可行性分为三类（安全、 caution、不安全），自动发出警报以触发预防措施并保护硬件。通过利用短期农场数据集和十年历史天气记录，模糊逻辑组件能够对快速环境变化作出反应，同时建立安全基线。与此同时，基于树的集成模型（梯度提升、XGBoost、LightGBM和CatBoost）分析非线性大气相互作用，在预测安全窗口方面达到高达99.67%的准确率。全面的敏感性分析，包括无SMOTE的评估和季节性时间留出验证，证实了模型对类别不平衡和未见极端情况的稳健性。实证基准测试表明，该框架优于现有的混合方法，凸显了将自适应模糊推理与深度历史学习相结合对于智慧农业的必要性。","Discover Internet of Things","2026-10-15T00:00:00Z",76,{"impact":101,"substance":452,"depth":17,"authority":20,"freshness":55,"relevant":22,"comment":453},21,"将模糊逻辑与集成学习融合用于极端天气下的农业无人机实时调度，方法扎实、验证充分，对智慧农业作业安全有实际参考价值。",[455],{"name":448,"url":445},[29,30,457,32,458],"农业无人机","气象预警",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":462},{"candidates":463,"agricultural_evidence":48},[464,468],{"code":35,"score":19,"supported":48,"title":465,"abstract":466,"tags":467},[215],[213,215],[30],{"code":54,"score":308,"supported":48,"title":469,"abstract":470,"tags":471},[],[29,32,58],[29,32],[473,474],"无人机 精准农业 极端天气","UAV 棕榈园 调度","无人机精准农业极端天气-5153","10.1007\u002Fs43926-026-00489-2",{"doi":476,"openalex_id":478,"authors":479,"venue":448,"cited_by_count":77,"oa_url":501,"card":502,"direction":176,"ingested_from":85},"W7166891579",[480,483,486,488,490,493,495,498],{"name":481,"orcid":482},"Ahmad B. Alkhodre","https:\u002F\u002Forcid.org\u002F0000-0001-6168-3552",{"name":484,"orcid":485},"Adnan Ahmed Abi Sen","https:\u002F\u002Forcid.org\u002F0000-0003-2960-5807",{"name":487,"orcid":9},"Abdelouahab Abid",{"name":489,"orcid":9},"Nour Mahmoud Bahbouh",{"name":491,"orcid":492},"Adel Ben Mnaouer","https:\u002F\u002Forcid.org\u002F0000-0003-3617-7636",{"name":494,"orcid":9},"Mohamed Benaida",{"name":496,"orcid":497},"Yazed Alsaawy","https:\u002F\u002Forcid.org\u002F0000-0001-5031-3388",{"name":499,"orcid":500},"Hani Almoamari","https:\u002F\u002Forcid.org\u002F0000-0002-3550-8977","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs43926-026-00489-2.pdf",{"tldr":503,"method":504,"finding":505,"direction":176,"opportunity":506},"提出融合模糊逻辑与机器学习分类器的无人机实时调度框架，在恶劣天气下保障精准农业飞行安全。","模糊逻辑推理结合梯度提升、XGBoost、LightGBM、CatBoost集成","框架将起飞可行性分为安全、警告、不安全三级，预测安全窗口准确率最高达99.67%，优于现有混合方法。","可将区域特定灾害（沙尘暴、山洪）纳入实时决策融合，并扩展到多机协同调度与边缘端轻量化部署研究。","2026-10-17T23:30:08.889571Z",{"id":509,"title":510,"url":511,"summary":512,"summary_zh":513,"content":9,"source_name":10,"source_url":511,"published_at":449,"category":12,"cover_url":9,"hotness":98,"is_selected":14,"score":514,"score_detail":515,"sources":517,"tags":519,"primary_topic":521,"secondary_topics":522,"classification":524,"search_phrases":560,"slug":563,"view_count":22,"doi":564,"paper":565,"created_at":575},5086,"Artificial Intelligence for Environmental Sustainability and Climate Change Mitigation: Recent Advances, Challenges, and Future Perspectives","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23399627","Abstract Climate change and environmental degradation have emerged as two of the most pressing global challenges of the twenty-first century, threatening ecosystems, biodiversity, human health, and sustainable development. Artificial Intelligence (AI) has transformed environmental research and management by enabling advanced data analytics, real-time monitoring, predictive modeling, and intelligent decision-support systems. This review critically examines the applications of AI in addressing environmental and climate-related challenges, including climate forecasting, air and water quality monitoring, biodiversity conservation, precision agriculture, renewable energy optimization, carbon emission assessment, waste management, and disaster risk reduction. Furthermore, The review also identifies current research gaps and highlights future opportunities for developing transparent, energy-efficient, and explainable AI systems that support evidence-based environmental decisionmaking. It concludes that AI has become a transformative tool for accelerating progress toward climate change mitigation, environmental sustainability, and the achievement of the United Nations Sustainable Development Goals (SDGs). Strengthening interdisciplinary collaboration, responsible AI governance, and policy support will be essential to maximize the environmental benefits of AI while minimizing its ecological footprint. Keywords: Artificial Intelligence; Climate Change; Environmental Sustainability; Machine Learning; Deep Learning; Remote Sensing; Internet of Things; Renewable Energy; Biodiversity Conservation; Sustainable Development Goals.","摘要 气候变化和环境退化已成为二十一世纪最紧迫的全球性挑战之一，威胁着生态系统、生物多样性、人类健康和可持续发展。人工智能（AI）通过实现先进的数据分析、实时监测、预测建模和智能决策支持系统，改变了环境研究与管理的方式。本文综述批判性地考察了AI在应对环境和气候相关挑战中的应用，包括气候预测、空气和水质监测、生物多样性保护、精准农业、可再生能源优化、碳排放评估、废物管理和灾害风险降低。此外，本综述还指出了当前的研究空白，并强调了未来开发透明、节能且可解释的AI系统的机遇，以支持基于证据的环境决策。文章得出结论，AI已成为加速气候变化减缓、环境可持续发展以及实现联合国可持续发展目标（SDGs）的变革性工具。加强跨学科合作、负责任的AI治理和政策支持，对于最大化AI的环境效益并同时最小化其生态足迹至关重要。关键词：人工智能；气候变化；环境可持续性；机器学习；深度学习；遥感；物联网；可再生能源；生物多样性保护；可持续发展目标。",70,{"impact":17,"substance":101,"depth":19,"authority":102,"freshness":21,"relevant":22,"comment":516},"综述性论文，覆盖AI在气候预测、精准农业、遥感监测等方向的应用，与农业信息化相关但偏宏观综述，增量有限。",[518],{"name":10,"url":511},[29,30,31,126,520],"遥感监测",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[523],{"code":111,"slug":112,"name":113,"path":114,"parent_code":115},{"status":42,"locked":14,"version":43,"method":44,"evidence":525},{"candidates":526,"agricultural_evidence":48},[527,531,535,539,544,551,555],{"code":35,"score":17,"supported":48,"title":528,"abstract":529,"tags":530},[50],[212,213,215,216,50],[30],{"code":111,"score":19,"supported":48,"title":532,"abstract":533,"tags":534},[128],[126,128],[126],{"code":54,"score":236,"supported":48,"title":536,"abstract":537,"tags":538},[],[32,58],[29],{"code":321,"score":236,"supported":48,"title":540,"abstract":541,"tags":543},[],[300,542],"remote sensing",[520],{"code":545,"score":88,"supported":48,"title":546,"abstract":547,"tags":550},"H4",[],[548,549],"碳排放","carbon emission",[],{"code":140,"score":88,"supported":48,"title":552,"abstract":553,"tags":554},[],[143,144],[],{"code":198,"score":88,"supported":48,"title":556,"abstract":557,"tags":559},[],[193,558],"internet of things",[],[561,562],"AI 气候变化","人工智能 环境可持续","AI气候变化-5086","10.5281\u002Fzenodo.23399627",{"doi":564,"openalex_id":566,"authors":567,"venue":10,"cited_by_count":77,"oa_url":511,"card":570,"direction":176,"ingested_from":85},"W7222092855",[568],{"name":569,"orcid":9},"Deepmala Suryavanshi",{"tldr":571,"method":572,"finding":573,"direction":82,"opportunity":574},"综述AI在气候变化缓解与环境可持续领域的应用进展、挑战与未来方向。","文献综述，涵盖机器学习、深度学习、遥感、物联网等技术应用。","AI已成为推动气候减缓与可持续发展目标实现的变革性工具。","可聚焦可解释、低能耗AI在农业碳减排与精准环境决策中的落地研究。","2026-10-16T23:30:15.059867Z"]