[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4023":3,"related-4023":110},{"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":9,"secondary_topics":32,"classification":33,"search_phrases":90,"slug":93,"view_count":74,"doi":94,"paper":95,"created_at":109},4023,"Leveraging Artificial Intelligence for Climate Change Mitigation in Africa: A Sustainable Approach","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fwjimt.v9.no6.2025.pg212.229","As climate change intensifies environmental and socio-economic vulnerabilities across Africa, there is a growing need for innovative, scalable solutions to support climate resilience. This study critically examines the role of Artificial Intelligence (AI) in enhancing climate adaptation and mitigation efforts on the continent. Employing a qualitative, multi-source research methodology, the paper synthesizes academic literature, institutional frameworks, and applied case studies to assess the integration of AI into environmental monitoring and resource management systems. Particular emphasis is placed on the use of machine learning and remote sensing technologies to support real-time data analysis, land-use classification, water resource management, and early warning systems. The Enhancing National Climate Services (ENACTS) program is analyzed as a leading case, demonstrating how the fusion of satellite and groundbased data can significantly improve climate services in sectors such as agriculture and public health. The paper also identifies critical challenges—including data scarcity, inadequate infrastructure, limited technical capacity, and ethical risks such as data privacy and algorithmic bias—that hinder the effective deployment of AI. In response, the study offers strategic policy recommendations to strengthen climate data infrastructure, promote cross-sector collaboration, and build human capital in AI and climate science. The findings underscore the importance of African-led innovation, ethical governance, and context-specific solutions in ensuring that AI contributes meaningfully and equitably to sustainable development and climate resilience.","随着气候变化加剧非洲各地的环境和社会经济脆弱性，人们日益需要创新且可扩展的解决方案来支持气候韧性建设。本研究批判性地考察了人工智能（AI）在增强非洲大陆气候适应与减缓努力中的作用。本文采用定性、多来源的研究方法，综合学术文献、制度框架和应用案例研究，评估AI在环境监测和资源管理系统中的整合情况。研究特别强调利用机器学习和遥感技术支持实时数据分析、土地利用分类、水资源管理和预警系统。本文以“增强国家气候服务”（ENACTS）项目作为领先案例进行分析，展示卫星数据与地面数据的融合如何显著改善农业和公共卫生等部门的气候服务。本文还指出了阻碍AI有效部署的关键挑战，包括数据稀缺、基础设施不足、技术能力有限，以及数据隐私和算法偏见等伦理风险。针对这些问题，本研究提出了战略政策建议，以加强气候数据基础设施、促进跨部门合作，并建设AI与气候科学领域的人力资本。研究结果强调了非洲主导的创新、伦理治理和因地制宜的解决方案对于确保AI以有意义且公平的方式促进可持续发展和气候韧性的重要性。",null,"WORLD JOURNAL OF INNOVATION AND MODERN TECHNOLOGY","2026-10-01T00:00:00Z","论文",10,false,60,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,18,16,6,8,1,"论文聚焦AI在非洲气候适应与农业气候服务中的应用，含ENACTS案例与政策建议，但期刊权威性一般、地域相关性偏弱，可作参考而非重点精选。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","非洲农业","气候韧性","遥感监测",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":41,"reason":89},[39,49,57,65,72,79,84],{"code":40,"score":18,"supported":41,"title":42,"abstract":44,"tags":48},"H7",true,[43],"climate change",[45,46,43,47],"气候变化","气候适应","climate resilience",[30],{"code":50,"score":18,"supported":41,"title":51,"abstract":53,"tags":56},"I2",[52],"artificial intelligence",[54,55,52],"机器学习","machine learning",[28],{"code":58,"score":59,"supported":41,"title":60,"abstract":61,"tags":64},"I3",7,[],[62,63],"遥感","remote sensing",[31],{"code":66,"score":20,"supported":41,"title":67,"abstract":68,"tags":71},"H3",[],[69,70],"水资源","water resource",[],{"code":73,"score":74,"supported":41,"title":75,"abstract":76,"tags":78},"I7",3,[],[77],"研究方法",[],{"code":80,"score":22,"supported":14,"title":81,"abstract":82,"tags":83},"I1",[],[],[27],{"code":85,"score":22,"supported":14,"title":86,"abstract":87,"tags":88},"J3",[],[],[29],"多个产业证据相当，需要确认主归属",[91,92],"ENACTS 气候服务 非洲","人工智能 气候适应 非洲","ENACTS气候服务非洲-4023","10.56201\u002Fwjimt.v9.no6.2025.pg212.229",{"doi":94,"openalex_id":96,"authors":97,"venue":10,"cited_by_count":22,"oa_url":100,"card":101,"direction":107,"ingested_from":108},"W7215085286",[98],{"name":99,"orcid":9},"Ogunade Josiah Ayodele","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FWJIMT\u002FVOL. 9 NO. 6 2025\u002FLeveraging Artificial Intelligence for Climate 212-229.pdf",{"tldr":102,"method":103,"finding":104,"direction":105,"opportunity":106},"综述AI在非洲气候变化适应与减缓中的作用，以ENACTS为例分析挑战并提出政策建议。","定性多源方法，综合文献、制度框架与案例，聚焦机器学习和遥感。","AI可提升气候服务，但受数据稀缺、基础设施和伦理风险制约，需非洲主导创新。","农业绿色发展与碳","可研究小农户场景下AI气候服务的落地机制与公平性评估，填补非洲本地化实证空白。","农业遥感与作物表型","openalex","2026-10-02T23:30:24.321762Z",{"total":20,"page":22,"page_size":20,"items":111},[112,196,301,379,436,527],{"id":113,"title":114,"url":115,"summary":116,"summary_zh":117,"content":9,"source_name":118,"source_url":115,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":119,"score_detail":120,"sources":125,"tags":127,"primary_topic":9,"secondary_topics":129,"classification":130,"search_phrases":174,"slug":177,"view_count":74,"doi":178,"paper":179,"created_at":195},4029,"Artificial Intelligence in Soil Management: Implications for Climate Resilience—A Comprehensive Review","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu181910057","Climate change is accelerating soil degradation, threatening agricultural sustainability and soil climate resilience. Key climate stressors include drought, altered precipitation, extreme rainfall, erosion and salinization. Artificial Intelligence (AI), combined with advanced sensing technologies, offers new opportunities for data-driven soil management under dynamic environmental conditions. This review aims to critically examine recent advances in AI-based approaches for soil monitoring, modelling and decision support, with an emphasis on soil management practices that can contribute to enhancing soil climate resilience. We analyze machine learning and deep learning methods applied to multisource data, from field observations, laboratory analyses, proximal sensing, remote sensing, meteorological records, geospatial information and agricultural management data, to predict key soil properties, including moisture, nutrient status, carbon dynamics and indicators of degradation. A total of 106 peer-reviewed studies were analysed. This review further explores AI-driven strategies for adaptive soil management, including precision irrigation, fertilization management and early-warning systems for drought, salinity and soil degradation. Key challenges, such as data heterogeneity, model explainability, sensor reliability and transferability across soil types and climates, are also discussed. Finally, emerging directions, including explainable AI, multimodal data fusion and digital soil twins, are highlighted, positioning AI-enabled sensing as a basis for climate-resilient soil management.","气候变化正在加速土壤退化，威胁农业可持续性与土壤气候韧性。主要气候胁迫因素包括干旱、降水格局改变、极端降雨、侵蚀和盐渍化。人工智能（AI）与先进传感技术相结合，为动态环境条件下的数据驱动土壤管理提供了新机遇。本综述旨在批判性审视基于AI的土壤监测、建模和决策支持方法的最新进展，重点关注有助于增强土壤气候韧性的土壤管理实践。我们分析了应用于多源数据的机器学习和深度学习方法，这些数据来自田间观测、实验室分析、近地传感、遥感、气象记录、地理空间信息和农业管理数据，用于预测关键土壤属性，包括水分、养分状况、碳动态和退化指标。共分析了106项同行评审研究。本综述进一步探讨了AI驱动的适应性土壤管理策略，包括精准灌溉、施肥管理以及干旱、盐渍化和土壤退化预警系统。还讨论了关键挑战，如数据异质性、模型可解释性、传感器可靠性以及跨土壤类型和气候的可迁移性。最后，强调了新兴方向，包括可解释AI、多模态数据融合和数字土壤孪生，将AI赋能的传感定位为气候韧性土壤管理的基础。","Sustainability",81,{"impact":18,"substance":121,"depth":122,"authority":123,"freshness":21,"relevant":22,"comment":124},22,19,14,"系统综述106项研究，梳理AI在土壤监测、建模与决策支持中的进展，方法新颖、数据规模明确，对智慧农业与气候韧性土壤管理有实质参考价值。",[126],{"name":118,"url":115},[27,28,30,31,128],"土壤管理",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":131},{"candidates":132,"agricultural_evidence":41,"reason":89},[133,139,146,153,157,164,170],{"code":40,"score":18,"supported":41,"title":134,"abstract":135,"tags":138},[47],[45,136,43,47,137],"干旱","drought",[30],{"code":50,"score":18,"supported":41,"title":140,"abstract":141,"tags":145},[52],[142,54,55,143,52,144],"深度学习","deep learning","decision support",[28],{"code":147,"score":123,"supported":41,"title":148,"abstract":150,"tags":152},"H2",[149],"soil",[151,149],"土壤",[],{"code":58,"score":59,"supported":41,"title":154,"abstract":155,"tags":156},[],[62,63],[31],{"code":158,"score":20,"supported":41,"title":159,"abstract":160,"tags":163},"F3",[],[161,162],"施肥","fertilization",[],{"code":66,"score":20,"supported":41,"title":165,"abstract":166,"tags":169},[],[167,168],"灌溉","irrigation",[],{"code":80,"score":22,"supported":14,"title":171,"abstract":172,"tags":173},[],[],[27],[175,176],"AI 土壤管理 气候韧性","机器学习 土壤监测 遥感","AI土壤管理气候韧性-4029","10.3390\u002Fsu181910057",{"doi":178,"openalex_id":180,"authors":181,"venue":118,"cited_by_count":188,"oa_url":115,"card":189,"direction":107,"ingested_from":108},"W7215083510",[182,185],{"name":183,"orcid":184},"M.I.A. Barbosa","https:\u002F\u002Forcid.org\u002F0000-0002-9749-9674",{"name":186,"orcid":187},"Pedro Henrique Fernandes Rodrigues","https:\u002F\u002Forcid.org\u002F0000-0002-5381-6615",0,{"tldr":190,"method":191,"finding":192,"direction":193,"opportunity":194},"综述106项研究，分析AI结合传感技术用于土壤监测、建模与决策支持以提升气候韧性。","分析106篇同行评审文献，涵盖机器学习\u002F深度学习与多源土壤数据融合。","AI可预测土壤水分、养分、碳动态及退化指标，支持精准灌溉施肥与预警。","农业人工智能与决策模型","可解释AI、多模态数据融合与数字土壤孪生是提升跨土壤气候可迁移性的关键空白。","2026-10-02T23:30:24.680191Z",{"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":204,"is_selected":14,"score":205,"score_detail":206,"sources":209,"tags":213,"primary_topic":216,"secondary_topics":221,"classification":222,"search_phrases":283,"slug":286,"view_count":287,"doi":288,"paper":289,"created_at":300},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":121,"substance":18,"depth":19,"authority":207,"freshness":188,"relevant":22,"comment":208},13,"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。",[210,211],{"name":202,"url":199},{"name":202,"url":212},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22914539",[214,27,28,215,30,31],"数字乡村","农业物联网",{"code":217,"slug":218,"name":219,"path":220,"parent_code":9},"I","digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":223,"locked":14,"version":35,"method":36,"evidence":224},"partial",{"candidates":225,"agricultural_evidence":41,"reason":282},[226,231,239,246,250,255,262,266,271,277],{"code":50,"score":18,"supported":41,"title":227,"abstract":228,"tags":230},[52],[54,229,55,52],"病虫害识别",[28],{"code":232,"score":18,"supported":41,"title":233,"abstract":235,"tags":238},"I5",[234],"IoT",[236,237,234],"物联网","internet of things",[215],{"code":80,"score":240,"supported":41,"title":241,"abstract":243,"tags":245},15,[242],"precision agriculture",[244,242],"精准农业",[27],{"code":58,"score":240,"supported":41,"title":247,"abstract":248,"tags":249},[63],[62,63],[31],{"code":66,"score":251,"supported":41,"title":252,"abstract":253,"tags":254},9,[],[167,69,168],[],{"code":256,"score":20,"supported":41,"title":257,"abstract":258,"tags":261},"A8",[],[259,260],"粮食安全","food security",[],{"code":147,"score":20,"supported":41,"title":263,"abstract":264,"tags":265},[],[151,149],[],{"code":40,"score":267,"supported":41,"title":268,"abstract":269,"tags":270},4,[],[45],[30],{"code":272,"score":74,"supported":41,"title":273,"abstract":274,"tags":276},"H6",[],[275],"农业生态",[],{"code":278,"score":22,"supported":14,"title":279,"abstract":280,"tags":281},"I6",[],[],[214],"一级领域明确，二级方向需要细分",[284,285],"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",5,"10.5281\u002Fzenodo.22914538",{"doi":288,"openalex_id":290,"authors":291,"venue":202,"cited_by_count":188,"oa_url":199,"card":294,"direction":298,"ingested_from":108},"W7214083098",[292],{"name":293,"orcid":9},"Twinkal Prakash Sawant",{"tldr":295,"method":296,"finding":297,"direction":298,"opportunity":299},"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":302,"title":303,"url":304,"summary":305,"summary_zh":306,"content":9,"source_name":202,"source_url":304,"published_at":203,"category":12,"cover_url":9,"hotness":307,"is_selected":14,"score":308,"score_detail":309,"sources":313,"tags":321,"primary_topic":9,"secondary_topics":323,"classification":324,"search_phrases":361,"slug":364,"view_count":267,"doi":365,"paper":366,"created_at":378},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":18,"substance":310,"depth":311,"authority":207,"freshness":74,"relevant":22,"comment":312},20,17,"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。",[314,315,317,319],{"name":202,"url":304},{"name":202,"url":316},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22724438",{"name":202,"url":318},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22767935",{"name":202,"url":320},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22767934",[27,28,30,31,322],"作物育种",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":325},{"candidates":326,"agricultural_evidence":41,"reason":89},[327,331,337,342,346,350,357],{"code":40,"score":18,"supported":41,"title":328,"abstract":329,"tags":330},[137],[45,136,43,137],[30],{"code":50,"score":18,"supported":41,"title":332,"abstract":333,"tags":336},[52],[142,54,334,55,143,52,335],"计算机视觉","computer vision",[28],{"code":58,"score":13,"supported":41,"title":338,"abstract":339,"tags":341},[],[62,63,340],"hyperspectral",[31],{"code":256,"score":20,"supported":41,"title":343,"abstract":344,"tags":345},[],[259,260],[],{"code":147,"score":20,"supported":41,"title":347,"abstract":348,"tags":349},[],[151,149],[],{"code":351,"score":20,"supported":41,"title":352,"abstract":353,"tags":356},"H8",[],[354,355],"可持续农业","sustainable agriculture",[],{"code":80,"score":22,"supported":14,"title":358,"abstract":359,"tags":360},[],[],[27],[362,363],"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":365,"openalex_id":367,"authors":368,"venue":202,"cited_by_count":188,"oa_url":304,"card":373,"direction":107,"ingested_from":108},"W7212377743",[369,371],{"name":370,"orcid":9},"Aruna Nangare",{"name":372,"orcid":9},"Vaishali Wankhede",{"tldr":374,"method":375,"finding":376,"direction":107,"opportunity":377},"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。","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",{"id":380,"title":381,"url":382,"summary":383,"summary_zh":384,"content":9,"source_name":202,"source_url":382,"published_at":385,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":386,"score_detail":387,"sources":389,"tags":391,"primary_topic":394,"secondary_topics":395,"classification":396,"search_phrases":420,"slug":423,"view_count":188,"doi":424,"paper":425,"created_at":435},5563,"AI-Powered Early Detection of Crop Diseases and Pest Infestations: A New Paradigm for Plantation Risk Management","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531525","Abstract: Crop diseases and pest infestations remain persistent threats to plantation productivity, with pests estimated to destroy up to 40% of global crop production and plant diseases and invasive insects imposing substantial economic losses worldwide (Food and Agriculture Organization of the United Nations [FAO], 2019). Climate change is intensifying these risks by altering the geographic range, seasonality, and population dynamics of pathogens and pests (Gullino et al., 2021; Skendžić et al., 2021). This paper examines how artificial intelligence (AI) — including convolutional neural networks (CNNs), vision transformers (ViTs), unmanned aerial vehicle (UAV)-based multispectral and hyperspectral sensing, and Internet of Things (IoT)-enabled monitoring — can strengthen the early detection of biotic stress in plantation crops and thereby support plantation risk management. Drawing on recent peer-reviewed and industry literature, the paper synthesises evidence on detection performance, deployment modalities, and pathways for integration into enterprise risk-management systems. It proposes a conceptual framework in which AI-enabled early-warning systems operate as a continuous, spatially explicit risk-sensing layer linking agronomic observations with operational, financial, and insurance decision-making. AI can improve the timeliness, spatial precision, and consistency of surveillance; however, its risk-management value depends on field validation, representative training data, reliable infrastructure, affordable deployment, and appropriate governance. AI should therefore currently be positioned as decision support that augments, rather than replaces, agronomic expertise and ground-truthing. Keywords: artificial intelligence; plant disease detection; pest infestation; precision agriculture; plantation risk management; machine learning; remote sensing. Title: AI-Powered Early Detection of Crop Diseases and Pest Infestations: A New Paradigm for Plantation Risk Management Author: Dr. Geetha Govindan K Gopalakrishnan International Journal of Management and Commerce Innovations ISSN 2348-7585 (Online) Vol. 14, Issue 2, October 2026 - March 2027 Page No: 54-59 Research Publish Journals Website: www.researchpublish.com Published Date: 21-October-2026 DOI: https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531526 Paper Download Link (Source) https:\u002F\u002Fwww.researchpublish.com\u002Fpapers\u002Fai-powered-early-detection-of-crop-diseases-and-pest-infestations-a-new-paradigm-for-plantation-risk-management","摘要：作物病害与虫害侵染始终是种植园生产力的持续威胁，据估计，害虫每年可摧毁全球高达40%的作物产量，植物病害与入侵昆虫在全球范围内造成重大经济损失（联合国粮食及农业组织[FAO]，2019）。气候变化通过改变病原体与害虫的地理分布范围、季节性规律及种群动态，正在加剧这些风险（Gullino等，2021；Skendžić等，2021）。本文探讨人工智能（AI）——包括卷积神经网络（CNN）、视觉变换器（ViT）、基于无人机（UAV）的多光谱与高光谱遥感，以及物联网（IoT）赋能的监测技术——如何强化种植园作物生物胁迫的早期检测，从而支撑种植园风险管理。本文基于近期同行评审文献与行业文献，综合梳理了检测性能、部署模式及其融入企业风险管理系统的路径等方面的证据。文章提出一个概念框架，其中AI赋能的预警系统作为持续运行、空间显性的风险感知层，将农艺观测与运营、财务及保险决策相衔接。AI能够提升监测的及时性、空间精度与一致性；然而，其风险管理价值取决于田间验证、具有代表性的训练数据、可靠的基础设施、可负担的部署成本以及适当的治理。因此，当前应将AI定位为决策支持工具，用以增强而非替代农艺专业知识与实地核验。关键词：人工智能；植物病害检测；虫害侵染；精准农业；种植园风险管理；机器学习；遥感。题目：AI驱动的作物病害与虫害侵染早期检测：种植园风险管理的新范式。作者：Geetha Govindan K Gopalakrishnan博士。《国际管理与商业创新期刊》ISSN 2348-7585（在线）第14卷第2期，2026年10月—2027年3月，第54-59页。研究出版期刊网站：www.researchpublish.com。发布日期：2026年10月21日。DOI：https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531526。论文下载链接（来源）：https:\u002F\u002Fwww.researchpublish.com\u002Fpapers\u002Fai-powered-early-detection-of-crop-diseases-and-pest-infestations-a-new-paradigm-for-plantation-risk-ma","2026-10-21T00:00:00Z",56,{"impact":17,"substance":19,"depth":207,"authority":20,"freshness":251,"relevant":22,"comment":388},"综述性论文，系统梳理AI与遥感在作物病虫害早期预警中的应用并提出风险管理框架，有参考价值但方法创新与数据规模有限，信源为普通出版平台。",[390],{"name":202,"url":382},[27,28,31,392,393],"风险管理","病虫害监测",{"code":217,"slug":218,"name":219,"path":220,"parent_code":9},[],{"status":223,"locked":14,"version":35,"method":36,"evidence":397},{"candidates":398,"agricultural_evidence":41,"reason":282},[399,403,408,412,416],{"code":50,"score":13,"supported":41,"title":400,"abstract":401,"tags":402},[],[54,55,52,144],[28],{"code":58,"score":13,"supported":41,"title":404,"abstract":405,"tags":407},[],[62,63,406,340],"multispectral",[31],{"code":232,"score":251,"supported":41,"title":409,"abstract":410,"tags":411},[],[236,237,234],[],{"code":80,"score":59,"supported":41,"title":413,"abstract":414,"tags":415},[],[244,242],[27],{"code":40,"score":20,"supported":41,"title":417,"abstract":418,"tags":419},[],[45,43],[],[421,422],"AI 作物病害 早期检测","无人机 多光谱 病虫害监测","AI作物病害早期检测-5563","10.5281\u002Fzenodo.23531525",{"doi":424,"openalex_id":426,"authors":427,"venue":202,"cited_by_count":188,"oa_url":382,"card":430,"direction":298,"ingested_from":108},"W7223085400",[428],{"name":429,"orcid":9},"Dr. Geetha Govindan K Gopalakrishnan",{"tldr":431,"method":432,"finding":433,"direction":193,"opportunity":434},"综述AI（CNN、ViT、无人机遥感、物联网）用于作物病虫害早期检测并融入种植园风险管理。","文献综述，整合CNN、ViT、无人机多\u002F高光谱与IoT监测的检测性能与部署方式。","AI可提升监测及时性与空间精度，但价值取决于田间验证、代表性数据与治理，应作决策支持而非替代专家。","可将AI预警层与保险、金融风控系统对接，研究跨域数据融合与可解释风险定价的空白。","2026-10-22T11:20:24.428080Z",{"id":437,"title":438,"url":439,"summary":440,"summary_zh":441,"content":9,"source_name":442,"source_url":439,"published_at":385,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":443,"score_detail":444,"sources":446,"tags":448,"primary_topic":449,"secondary_topics":453,"classification":462,"search_phrases":503,"slug":506,"view_count":188,"doi":507,"paper":508,"created_at":526},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)",80,{"impact":18,"substance":121,"depth":18,"authority":207,"freshness":251,"relevant":22,"comment":445},"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。",[447],{"name":442,"url":439},[27,28,236,244,30],{"code":50,"slug":450,"name":451,"path":452,"parent_code":217},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[454,458],{"code":232,"slug":455,"name":456,"path":457,"parent_code":217},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":66,"slug":168,"name":459,"path":460,"parent_code":461},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":463,"locked":14,"version":35,"method":36,"evidence":464},"classified",{"candidates":465,"agricultural_evidence":41},[466,470,474,480,484,488,492,498],{"code":50,"score":18,"supported":41,"title":467,"abstract":468,"tags":469},[52],[142,54,334,55,143,335],[28],{"code":80,"score":19,"supported":41,"title":471,"abstract":472,"tags":473},[242],[244,242],[27,244],{"code":232,"score":13,"supported":41,"title":475,"abstract":476,"tags":479},[],[236,477,234,478],"边缘计算","edge computing",[236],{"code":66,"score":251,"supported":41,"title":481,"abstract":482,"tags":483},[],[167,69,168],[],{"code":40,"score":59,"supported":41,"title":485,"abstract":486,"tags":487},[],[45,43],[30],{"code":147,"score":20,"supported":41,"title":489,"abstract":490,"tags":491},[],[151,149],[],{"code":493,"score":74,"supported":41,"title":494,"abstract":495,"tags":497},"A7",[],[496],"间作",[],{"code":158,"score":74,"supported":41,"title":499,"abstract":500,"tags":502},[],[501],"养分管理",[],[504,505],"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":507,"openalex_id":509,"authors":510,"venue":442,"cited_by_count":188,"oa_url":439,"card":521,"direction":298,"ingested_from":108},"W7223121456",[511,514,516,518],{"name":512,"orcid":513},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":515,"orcid":9},"Oluwadare OYEBODE",{"name":517,"orcid":9},"Iretiayo Adelaiye",{"name":519,"orcid":520},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":522,"method":523,"finding":524,"direction":193,"opportunity":525},"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":528,"title":529,"url":530,"summary":531,"summary_zh":532,"content":9,"source_name":533,"source_url":530,"published_at":534,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":535,"score_detail":536,"sources":539,"tags":541,"primary_topic":543,"secondary_topics":544,"classification":545,"search_phrases":572,"slug":575,"view_count":188,"doi":576,"paper":577,"created_at":587},5159,"Integration of IoT And Satellite Data for Real-Time Agricultural Crop Stress Monitoring Using Spatio-Temporal Transformers","https:\u002F\u002Fdoi.org\u002F10.31127\u002Ftuje.1871227","This research work presents an integrated framework for real-time crop stress monitoring that combines ground-based Internet of Things (IoT) sensors and multi-temporal satellite remote sensing data. Due to growing environmental uncertainty and limited resources, the early identification of crop stress is crucial for maximizing agricultural productivity. IoT sensors yield localized, real-time measurements of key variables like soil moisture, temperature, and humidity, whereas satellite images yield wide-area vegetation and thermal indicators (e.g., NDVI, EVI, LST). The solution utilizes a Spatio-Temporal Transformer (ST-Transformer) model to integrate these heterogeneous data streams to make early crop disease and irrigation stress detection. By integrating spatial satellite imagery with temporal IoT sequences, STT leverages attention mechanisms to offer a more holistic analysis, which translates to an impressive accuracy of 88% and a solid F1-Score of 0.855. This dual capacity to concurrently handle spatial and temporal dimensions allows STT to effectively generalize across diverse crop stress categories, providing insights that are both timely and actionable","本研究工作提出了一种集成框架，用于结合地面物联网（IoT）传感器与多时相卫星遥感数据实现作物胁迫的实时监测。由于环境不确定性日益加剧且资源有限，作物胁迫的早期识别对于最大化农业生产力至关重要。物联网传感器可提供土壤湿度、温度和湿度等关键变量的局部实时测量，而卫星图像则可提供广域植被与热指标（如NDVI、EVI、LST）。该方案利用时空Transformer（ST-Transformer）模型整合这些异构数据流，以实现作物病害与灌溉胁迫的早期检测。通过将空间卫星影像与时间物联网序列相结合，STT借助注意力机制提供更全面的分析，从而实现了88%的显著准确率和0.855的稳健F1分数。这种同时处理空间与时间维度的双重能力使STT能够有效泛化于多种作物胁迫类别，提供既及时又可操作的洞察。","Turkish Journal of Engineering","2026-10-16T00:00:00Z",75,{"impact":19,"substance":537,"depth":311,"authority":17,"freshness":251,"relevant":22,"comment":538},21,"该研究将物联网与多时相卫星数据融合，用时空Transformer实现作物胁迫早期识别，方法新颖、指标明确，属智慧农业细分领域有实质增量的技术进展，值得入选。",[540],{"name":533,"url":530},[27,28,236,542,31],"作物胁迫",{"code":232,"slug":455,"name":456,"path":457,"parent_code":217},[],{"status":463,"locked":14,"version":35,"method":36,"evidence":546},{"candidates":547,"agricultural_evidence":41},[548,552,556,560,564,568],{"code":232,"score":18,"supported":41,"title":549,"abstract":550,"tags":551},[234],[236,237,234],[236],{"code":58,"score":59,"supported":41,"title":553,"abstract":554,"tags":555},[],[62,63],[31],{"code":147,"score":20,"supported":41,"title":557,"abstract":558,"tags":559},[],[151,149],[],{"code":66,"score":20,"supported":41,"title":561,"abstract":562,"tags":563},[],[167,168],[],{"code":80,"score":22,"supported":14,"title":565,"abstract":566,"tags":567},[],[],[27],{"code":50,"score":22,"supported":14,"title":569,"abstract":570,"tags":571},[],[],[28],[573,574],"ST-Transformer 作物胁迫","IoT 卫星 遥感 作物监测","ST-Transformer作物胁迫-5159","10.31127\u002Ftuje.1871227",{"doi":576,"openalex_id":578,"authors":579,"venue":533,"cited_by_count":188,"oa_url":530,"card":582,"direction":298,"ingested_from":108},"W7222730658",[580],{"name":581,"orcid":9},"Sunitha Thankappan",{"tldr":583,"method":584,"finding":585,"direction":298,"opportunity":586},"融合IoT传感器与多时相卫星数据，用时空Transformer实现作物胁迫实时监测。","IoT土壤温湿度数据+卫星NDVI\u002FEVI\u002FLST，构建ST-Transform","模型准确率达88%、F1为0.855，可早期识别病害与灌溉胁迫。","可探索多源异构数据在真实田块尺度上的轻量化部署与跨区域迁移泛化。","2026-10-17T23:30:13.663599Z"]