[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3514":3,"related-3514":94},{"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":70,"slug":73,"view_count":22,"doi":74,"paper":75,"created_at":93},3514,"Artificial Intelligence for Climate Adaptation Decision Support in Data-Poor Developing Regions","https:\u002F\u002Fdoi.org\u002F10.22541\u002Fessoar.15009304\u002Fv1","Climate adaptation is a sequence of decisions taken under uncertainty, and the regions where climate risk is rising fastest are those with the least information to guide them. Only about 10 per cent of deaths are registered in the WHO African Region; nearly 90 per cent of required surface weather observations are missing across least developed countries and small island states; and only 40 per cent of African countries have multi-hazard early warning systems. This report examines whether artificial intelligence — machine learning, remote sensing and predictive analytics — can close these information gaps and improve adaptation decisions in data-poor developing regions. The report organises the problem as a decision chain with three information gaps — observation, prediction and decision — followed by an action gap that AI cannot close. It finds that AI has advanced fastest on prediction: AI weather models became operational at ECMWF in 2025, AI flood forecasts now cover 100 countries and about 700 million people, satellite nowcasts reach a continent with little radar, and AI monsoon-onset forecasts reached 38 million Indian farmers in 2025. On observation, satellite machine learning explains around 70 per cent of the variation in village wealth but only up to about half of the variation in changes over time. On decision, evidence from Togo, Bangladesh and Kenya shows that AI-assisted targeting, forecast-based triggers and satellite index insurance can deliver assistance faster and better, within clear limits. The report's central argument is the ground-truth paradox: AI stretches scarce observations further, but every AI product must be trained and verified against ground truth, so reliance on AI raises the value of each remaining station, survey and label. The 2025 interruption of FEWS NET and termination of the DHS Program show how fragile that foundation is. Because the value of information is the product of skill, lead time, reach, trust and the means to act, the highest returns usually lie not in more skilful models but in dissemination, institutions and prearranged finance. The report sets out a risk register, a six-principle policy framework, actions by actor and a roadmap to 2030.","气候适应是在不确定性下做出的一系列决策，而气候风险上升最快的地区恰恰是指导信息最匮乏的地区。世卫组织非洲区域仅登记了约10%的死亡病例；最不发达国家和小岛屿国家缺失了近90%所需的地面天气观测数据；仅有40%的非洲国家拥有多灾种早期预警系统。本报告考察人工智能——机器学习、遥感和预测分析——能否弥合这些信息缺口，改善数据匮乏的发展中地区的适应决策。报告将这一问题组织为一条决策链，包含三个信息缺口——观测、预测和决策——以及一个人工智能无法弥合的行动缺口。报告发现，人工智能在预测方面进展最快：人工智能天气模型于2025年在欧洲中期天气预报中心（ECMWF）投入业务运行，人工智能洪水预报现已覆盖100个国家和约7亿人口，卫星临近预报覆盖了一个几乎没有雷达的大陆，人工智能季风爆发预报于2025年惠及3800万印度农民。在观测方面，卫星机器学习可解释村庄财富约70%的变异，但对时间变化的解释力仅约一半。在决策方面，来自多哥、孟加拉国和肯尼亚的证据表明，人工智能辅助的目标定位、基于预报的触发机制和卫星指数保险能够在明确限度内更快、更好地提供援助。报告的核心论点是地面真值悖论：人工智能能够将稀缺的观测数据发挥更大效用，但每个人工智能产品都必须依据地面真值进行训练和验证，因此对人工智能的依赖提升了每一个剩余站点、调查和标注数据的价值。2025年FEWS NET的中断和DHS项目的终止表明这一基础何等脆弱。由于信息的价值是技能、提前期、覆盖面、信任和行动手段的乘积，最高回报通常不在于更精密的模型，而在于传播、制度和预先安排的融资。报告提出了风险登记册、六项原则的政策框架、各行为主体的行动以及到2030年的路线图。",null,"OpenAlex","2026-09-22T00:00:00Z","论文",10,false,86,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},22,24,19,13,8,1,"系统梳理AI在数据匮乏地区气候适应决策中的观测、预测与决策三类信息缺口，提出“地面真值悖论”，数据与结论扎实，对农业信息化与智慧农业有较强参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","气候适应","遥感监测","早期预警",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":69},[39,50,58,64],{"code":40,"score":41,"supported":42,"title":43,"abstract":46,"tags":49},"I2",26,true,[44,45],"artificial intelligence","decision support",[47,48,44],"机器学习","machine learning",[28],{"code":51,"score":52,"supported":42,"title":53,"abstract":54,"tags":57},"I3",7,[],[55,56],"遥感","remote sensing",[30],{"code":59,"score":60,"supported":42,"title":61,"abstract":62,"tags":63},"H7",4,[],[29],[29],{"code":65,"score":22,"supported":14,"title":66,"abstract":67,"tags":68},"I1",[],[],[27],"农业相关性需要复核",[71,72],"AI 气候适应 决策支持","数据匮乏地区 农业预警","AI气候适应决策支持-3514","10.22541\u002Fessoar.15009304\u002Fv1",{"doi":74,"openalex_id":76,"authors":77,"venue":9,"cited_by_count":83,"oa_url":84,"card":85,"direction":91,"ingested_from":92},"W7214097088",[78,80],{"name":79,"orcid":9},"H Heuristics",{"name":81,"orcid":82},"Hunter Hughes","https:\u002F\u002Forcid.org\u002F0009-0002-6161-9387",0,"https:\u002F\u002Fessopenarchive.org\u002Fdoi\u002Fpdf\u002F10.22541\u002Fessoar.15009304\u002Fv1",{"tldr":86,"method":87,"finding":88,"direction":89,"opportunity":90},"评估AI能否弥补数据匮乏地区气候适应决策的信息缺口，并提出地面真值悖论。","梳理观测、预测、决策三环节，结合AI天气模型、卫星ML与多国案例证据。","AI预测进展最快，但依赖地面真值；最高回报常在传播、制度与预置资金而非模型。","农业人工智能与决策模型","可研究AI辅助农业气候适应中地面真值稀缺下的验证与信任机制，及预置资金触发设计。","数字乡村与农业信息化","openalex","2026-09-25T23:30:46.008325Z",{"total":95,"page":22,"page_size":95,"items":96},6,[97,174,247,328,389,465],{"id":98,"title":99,"url":100,"summary":101,"summary_zh":102,"content":9,"source_name":103,"source_url":100,"published_at":104,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":105,"score_detail":106,"sources":111,"tags":113,"primary_topic":116,"secondary_topics":121,"classification":122,"search_phrases":157,"slug":160,"view_count":83,"doi":161,"paper":162,"created_at":173},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","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",56,{"impact":107,"substance":108,"depth":20,"authority":95,"freshness":109,"relevant":22,"comment":110},12,16,9,"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。",[112],{"name":103,"url":100},[27,28,30,114,115],"风险管理","病虫害监测",{"code":117,"slug":118,"name":119,"path":120,"parent_code":9},"I","digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":123,"locked":14,"version":35,"method":36,"evidence":124},"partial",{"candidates":125,"agricultural_evidence":42,"reason":156},[126,130,136,144,150],{"code":40,"score":13,"supported":42,"title":127,"abstract":128,"tags":129},[],[47,48,44,45],[28],{"code":51,"score":13,"supported":42,"title":131,"abstract":132,"tags":135},[],[55,56,133,134],"multispectral","hyperspectral",[30],{"code":137,"score":109,"supported":42,"title":138,"abstract":139,"tags":143},"I5",[],[140,141,142],"物联网","internet of things","IoT",[],{"code":65,"score":52,"supported":42,"title":145,"abstract":146,"tags":149},[],[147,148],"精准农业","precision agriculture",[27],{"code":59,"score":95,"supported":42,"title":151,"abstract":152,"tags":155},[],[153,154],"气候变化","climate change",[],"一级领域明确，二级方向需要细分",[158,159],"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":161,"openalex_id":163,"authors":164,"venue":103,"cited_by_count":83,"oa_url":100,"card":167,"direction":172,"ingested_from":92},"W7223085400",[165],{"name":166,"orcid":9},"Dr. Geetha Govindan K Gopalakrishnan",{"tldr":168,"method":169,"finding":170,"direction":89,"opportunity":171},"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。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:24.428080Z",{"id":175,"title":176,"url":177,"summary":178,"summary_zh":179,"content":9,"source_name":180,"source_url":177,"published_at":181,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":182,"score_detail":183,"sources":187,"tags":189,"primary_topic":191,"secondary_topics":195,"classification":196,"search_phrases":231,"slug":234,"view_count":83,"doi":235,"paper":236,"created_at":246},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":108,"substance":184,"depth":185,"authority":107,"freshness":109,"relevant":22,"comment":186},21,17,"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。",[188],{"name":180,"url":177},[27,28,140,190,30],"作物胁迫",{"code":137,"slug":192,"name":193,"path":194,"parent_code":117},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",[],{"status":197,"locked":14,"version":35,"method":36,"evidence":198},"classified",{"candidates":199,"agricultural_evidence":42},[200,205,209,216,223,227],{"code":137,"score":201,"supported":42,"title":202,"abstract":203,"tags":204},18,[142],[140,141,142],[140],{"code":51,"score":52,"supported":42,"title":206,"abstract":207,"tags":208},[],[55,56],[30],{"code":210,"score":95,"supported":42,"title":211,"abstract":212,"tags":215},"H2",[],[213,214],"土壤","soil",[],{"code":217,"score":95,"supported":42,"title":218,"abstract":219,"tags":222},"H3",[],[220,221],"灌溉","irrigation",[],{"code":65,"score":22,"supported":14,"title":224,"abstract":225,"tags":226},[],[],[27],{"code":40,"score":22,"supported":14,"title":228,"abstract":229,"tags":230},[],[],[28],[232,233],"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":235,"openalex_id":237,"authors":238,"venue":180,"cited_by_count":83,"oa_url":177,"card":241,"direction":172,"ingested_from":92},"W7222730658",[239],{"name":240,"orcid":9},"Sunitha Thankappan",{"tldr":242,"method":243,"finding":244,"direction":172,"opportunity":245},"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",{"id":248,"title":249,"url":250,"summary":251,"summary_zh":252,"content":9,"source_name":103,"source_url":250,"published_at":253,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":254,"score_detail":255,"sources":258,"tags":260,"primary_topic":262,"secondary_topics":266,"classification":272,"search_phrases":311,"slug":314,"view_count":22,"doi":315,"paper":316,"created_at":327},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。","2026-10-15T00:00:00Z",70,{"impact":201,"substance":108,"depth":256,"authority":107,"freshness":109,"relevant":22,"comment":257},15,"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。",[259],{"name":103,"url":250},[27,28,261,153,30],"可持续发展",{"code":40,"slug":263,"name":264,"path":265,"parent_code":117},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[267],{"code":59,"slug":268,"name":269,"path":270,"parent_code":271},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":197,"locked":14,"version":35,"method":36,"evidence":273},{"candidates":274,"agricultural_evidence":42},[275,281,285,289,293,300,307],{"code":40,"score":201,"supported":42,"title":276,"abstract":277,"tags":280},[44],[278,47,48,279,44],"深度学习","deep learning",[28],{"code":59,"score":256,"supported":42,"title":282,"abstract":283,"tags":284},[154],[153,154],[153],{"code":65,"score":52,"supported":42,"title":286,"abstract":287,"tags":288},[],[147,148],[27],{"code":51,"score":52,"supported":42,"title":290,"abstract":291,"tags":292},[],[55,56],[30],{"code":294,"score":95,"supported":42,"title":295,"abstract":296,"tags":299},"H4",[],[297,298],"碳排放","carbon emission",[],{"code":301,"score":95,"supported":42,"title":302,"abstract":303,"tags":306},"H6",[],[304,305],"生物多样性","biodiversity",[],{"code":137,"score":95,"supported":42,"title":308,"abstract":309,"tags":310},[],[140,141],[],[312,313],"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":315,"openalex_id":317,"authors":318,"venue":103,"cited_by_count":83,"oa_url":250,"card":321,"direction":172,"ingested_from":92},"W7222092855",[319],{"name":320,"orcid":9},"Deepmala Suryavanshi",{"tldr":322,"method":323,"finding":324,"direction":325,"opportunity":326},"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。","农业绿色发展与碳","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",{"id":329,"title":330,"url":331,"summary":332,"summary_zh":333,"content":9,"source_name":334,"source_url":331,"published_at":335,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":336,"score_detail":337,"sources":339,"tags":341,"primary_topic":344,"secondary_topics":345,"classification":347,"search_phrases":370,"slug":373,"view_count":374,"doi":375,"paper":376,"created_at":388},4809,"Enhanced crop yield estimation in smart agriculture via AI and IoT using the GODL geo-optimized algorithm","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12083-026-02299-3","Abstract Precision agriculture is one of the major breakthroughs which has been adopted to counteract some of the major challenges facing contemporary agriculture, such as maximizing crop yield, maximizing resource efficiency, and sustainability. The application of Artificial Intelligence (AI) and the Internet of Things (IoT) technologies can go a long way in enhancing crop yield prediction through data obtained from multiple sources, including satellite images, ground sensors, and weather stations. This research paper proposes a next-generation crop yield prediction system called Geo-Optimized Deep Learning (GODL) algorithm. The GODL algorithm integrates geospatial information with deep learning algorithms for crop yield analysis and prediction. This combination of deep learning and geospatial data allows accurate and scalable crop yield predictions using geospatial and sensor information. In order to ensure model optimization in terms of accuracy and efficiency, the GODL Algorithm makes use of advanced hyperparameter optimization methods that include Adam and Stochastic Gradient Descent (SGD). The research paper highlights the efficiency of the GODL Algorithm by conducting extensive experiments that compare the algorithm’s performance with other crop yield prediction models like Recurrent Neural Network (RNN) and other machine learning techniques. The evaluation takes into consideration important metrics such as accuracy, Jaccard Index, Fowlkes-Mallows Index (FMI), and G-mean, as well as IoT data delay and its effects on the prediction accuracy. In future research, the use of blockchain technology to facilitate data sharing and edge computing to process the data will be considered.","摘要 精准农业是为应对当代农业面临的若干重大挑战而采用的主要突破之一，这些挑战包括最大化作物产量、最大化资源效率以及可持续性。人工智能（AI）与物联网（IoT）技术的应用，能够通过从卫星图像、地面传感器和气象站等多源获取的数据，大幅提升作物产量预测能力。本文提出了一种下一代作物产量预测系统，称为地理优化深度学习（GODL）算法。GODL算法将地理空间信息与深度学习算法相结合，用于作物产量分析与预测。深度学习与地理空间数据的这种结合，使得利用地理空间和传感器信息进行准确且可扩展的作物产量预测成为可能。为确保模型在准确性和效率方面实现优化，GODL算法采用了先进的超参数优化方法，包括Adam和随机梯度下降（SGD）。本文通过开展大量实验，将该算法的性能与其他作物产量预测模型（如循环神经网络（RNN）及其他机器学习技术）进行比较，从而突出GODL算法的效率。评估考虑了准确率、Jaccard指数、Fowlkes-Mallows指数（FMI）和G均值等重要指标，以及物联网数据延迟及其对预测精度的影响。在未来的研究中，将考虑使用区块链技术促进数据共享，并利用边缘计算处理数据。","Peer-to-Peer Networking and Applications","2026-10-12T00:00:00Z",68,{"impact":107,"substance":201,"depth":108,"authority":20,"freshness":109,"relevant":22,"comment":338},"提出GODL地理优化深度学习算法融合AI与IoT进行作物估产，方法有创新且对比实验充分，但属学术论文，产业落地影响有限。",[340],{"name":334,"url":331},[27,28,342,30,343],"农业物联网","作物估产",{"code":137,"slug":192,"name":193,"path":194,"parent_code":117},[346],{"code":40,"slug":263,"name":264,"path":265,"parent_code":117},{"status":197,"locked":14,"version":35,"method":36,"evidence":348},{"candidates":349,"agricultural_evidence":42},[350,356,361,365],{"code":137,"score":201,"supported":42,"title":351,"abstract":352,"tags":355},[142],[140,353,141,142,354],"边缘计算","edge computing",[342],{"code":65,"score":256,"supported":42,"title":357,"abstract":359,"tags":360},[358],"smart agriculture",[147,148],[27],{"code":40,"score":13,"supported":42,"title":362,"abstract":363,"tags":364},[],[278,47,48,279,44],[28],{"code":51,"score":366,"supported":14,"title":367,"abstract":368,"tags":369},2,[],[],[343,30],[371,372],"GODL 作物估产 算法","AI IoT 智慧农业 产量预测","GODL作物估产算法-4809",3,"10.1007\u002Fs12083-026-02299-3",{"doi":375,"openalex_id":377,"authors":378,"venue":334,"cited_by_count":83,"oa_url":331,"card":383,"direction":172,"ingested_from":92},"W7221560555",[379,381],{"name":380,"orcid":9},"Keerthana T",{"name":382,"orcid":9},"Janarthanan R",{"tldr":384,"method":385,"finding":386,"direction":172,"opportunity":387},"提出GODL算法，融合地理空间信息与深度学习，结合AI和IoT提升作物产量预测精度。","GODL算法，融合地理空间数据与深度学习，采用Adam和SGD超参数优化。","GODL在准确率、Jaccard指数、FMI和G-mean上优于RNN等模型，且IoT数据延迟影响预","可探索区块链保障数据共享安全、边缘计算降低IoT延迟，以及多源异构数据融合的轻量化模型。","2026-10-13T23:30:10.304344Z",{"id":390,"title":391,"url":392,"summary":393,"summary_zh":394,"content":9,"source_name":395,"source_url":392,"published_at":335,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":396,"score_detail":397,"sources":399,"tags":401,"primary_topic":404,"secondary_topics":410,"classification":416,"search_phrases":438,"slug":441,"view_count":22,"doi":442,"paper":443,"created_at":464},4808,"Early Detection of Apple Scab in Orchards and Mildew in Vineyards Using A Novel Deep Learning Model and Multispectral Imagery","https:\u002F\u002Fdoi.org\u002F10.15832\u002Fankutbd.1898342","Agricultural diseases are a significant problem for growers as they cause substantial damage to fruits and leaves, leading to yield losses. Therefore, developing systems for early detection of plant diseases is a crucial issue. In this study, a new deep learning-based hybrid model is presented for the detection and segmentation of plant diseases, specifically apple scab disease in apple orchards and mildew disease in vineyards. Multispectral imagery data collected in agricultural areas of Tekirdag province in Turkey via drones between May 2023 and December 2024 were used in developing the early disease detection system. The datasets contain 3625 images of apple orchards and 5625 images of vineyards. DJI Phantom 4 Multispectral drone, equipped with five spectral bands (Blue, Green, Red, Red Edge, and Near Infrared), was utilized to capture both spectral richness and temporal progression. This study introduces a novel deep learning architecture developed specifically for time series-based disease detection and segmentation using multispectral drone imagery. Changes in plants over time provide important information about the occurrence of plant diseases. For this reason, time series were used in this study to extract features from the changes in plants over time. The proposed model architecture was specifically designed to leverage time series information, enabling robust tracking of crop health changes over time. The model trained with the dataset collected for apple diseases achieved an average Intersection Over Unit (IoU) value of 82.05%, while the model trained with the dataset collected for vineyard diseases reached an IoU value of 79.67%. The findings highlight the effectiveness of integrating drone-acquired multispectral time-series data with advanced deep learning models for scalable, accurate, and timely plant disease detection in precision agriculture.","农业病害是种植者面临的重大问题，会对果实和叶片造成严重损害，导致产量损失。因此，开发植物病害早期检测系统是一个至关重要的问题。本研究提出了一种基于深度学习的新型混合模型，用于植物病害的检测与分割，具体针对苹果园中的苹果黑星病和葡萄园中的霜霉病。该早期病害检测系统的开发使用了2023年5月至2024年12月期间通过无人机在土耳其泰基尔达省农业区域采集的多光谱影像数据。数据集包含3625幅苹果园图像和5625幅葡萄园图像。所用DJI Phantom 4多光谱无人机配备五个光谱波段（蓝、绿、红、红边和近红外），用于捕捉光谱丰富度和时间变化过程。本研究提出了一种专门开发的深度学习架构，用于基于时间序列的多光谱无人机影像病害检测与分割。植物随时间的变化提供了关于植物病害发生的重要信息。因此，本研究使用时间序列从植物随时间的变化中提取特征。所提出的模型架构专门设计以利用时间序列信息，从而能够稳健地跟踪作物健康状况随时间的变化。使用苹果病害数据集训练的模型达到了82.05%的平均交并比（IoU）值，而使用葡萄园病害数据集训练的模型达到了79.67%的IoU值。研究结果凸显了将无人机获取的多光谱时间序列数据与先进深度学习模型相结合，在精准农业中实现可扩展、准确且及时的植物病害检测方面的有效性。","Tarım Bilimleri Dergisi",77,{"impact":108,"substance":184,"depth":201,"authority":20,"freshness":109,"relevant":22,"comment":398},"该研究提出基于多光谱时序影像的新型深度学习模型，实现果园与葡萄园病害早期检测，数据规模大、方法新颖，对精准农业病害预警具有参考价值。",[400],{"name":395,"url":392},[27,402,28,30,403],"无人机","病害预警",{"code":405,"slug":406,"name":407,"path":408,"parent_code":409},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[411,412],{"code":40,"slug":263,"name":264,"path":265,"parent_code":117},{"code":51,"slug":413,"name":414,"path":415,"parent_code":117},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":197,"locked":14,"version":35,"method":36,"evidence":417},{"candidates":418,"agricultural_evidence":42},[419,426,430,434],{"code":405,"score":185,"supported":42,"title":420,"abstract":422,"tags":425},[421],"apple",[423,424,421],"苹果","葡萄",[],{"code":40,"score":256,"supported":42,"title":427,"abstract":428,"tags":429},[279],[278,279],[28],{"code":51,"score":107,"supported":42,"title":431,"abstract":432,"tags":433},[133],[133],[30],{"code":65,"score":52,"supported":42,"title":435,"abstract":436,"tags":437},[],[147,148],[27],[439,440],"苹果黑星病 无人机 多光谱","葡萄园 霜霉病 深度学习","苹果黑星病无人机多光谱-4808","10.15832\u002Fankutbd.1898342",{"doi":442,"openalex_id":444,"authors":445,"venue":395,"cited_by_count":83,"oa_url":457,"card":458,"direction":172,"ingested_from":92},"W7221558726",[446,449,452,454],{"name":447,"orcid":448},"Batuhan Aşıroğlu","https:\u002F\u002Forcid.org\u002F0000-0003-0767-6348",{"name":450,"orcid":451},"Aydın Kağan Turkoglu","https:\u002F\u002Forcid.org\u002F0009-0001-8383-8807",{"name":453,"orcid":9},"Fatih Sinan Bir",{"name":455,"orcid":456},"Sibel Senan","https:\u002F\u002Forcid.org\u002F0000-0001-6773-0428","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5761152",{"tldr":459,"method":460,"finding":461,"direction":462,"opportunity":463},"提出一种基于无人机多光谱时序影像的深度学习混合模型，实现苹果黑星病和葡萄霉病的早期检测与分割。","DJI Phantom 4多光谱无人机采集时序影像，构建时序深度学习分割模型。","苹果病IoU达82.05%，葡萄病达79.67%，验证多光谱时序数据有效性。","农业遥感与作物表型","可探索多作物、多病害的时序多光谱通用模型，并融合气象与地面数据提升早期预警能力。","2026-10-13T23:30:10.242467Z",{"id":466,"title":467,"url":468,"summary":469,"summary_zh":9,"content":9,"source_name":470,"source_url":9,"published_at":471,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":472,"score_detail":473,"sources":475,"tags":477,"primary_topic":9,"secondary_topics":480,"classification":481,"search_phrases":507,"slug":510,"view_count":22,"doi":511,"paper":512,"created_at":520},4732,"智能农业提升旱地混合作物家畜系统的氮利用与产量预测","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs44264-025-00012-8","整合1990至2025年158篇文献,综述人工智能、物联网与遥感在旱地混作系统的应用。精准施肥使氮利用效率提高超15%;机器学习结合卫星数据可解释超70%产量变异。数字可及性、基础设施与社会经济制约仍是广泛采纳的主要障碍","Discover Agriculture 2026-10-09","2026-10-09T00:00:00Z",81,{"impact":17,"substance":17,"depth":201,"authority":20,"freshness":95,"relevant":22,"comment":474},"基于158篇文献的系统综述，量化了智能农业在旱地混作系统的增产增效潜力，对智慧农业研究与实践有参考价值。",[476],{"name":470,"url":468},[27,28,478,30,479],"精准施肥","旱地农业",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":482},{"candidates":483,"agricultural_evidence":42,"reason":506},[484,490,494,498,502],{"code":485,"score":60,"supported":42,"title":486,"abstract":487,"tags":489},"F3",[],[488],"施肥",[478],{"code":40,"score":60,"supported":42,"title":491,"abstract":492,"tags":493},[],[47],[28],{"code":51,"score":60,"supported":42,"title":495,"abstract":496,"tags":497},[],[55],[30],{"code":137,"score":374,"supported":42,"title":499,"abstract":500,"tags":501},[],[140],[],{"code":65,"score":22,"supported":14,"title":503,"abstract":504,"tags":505},[],[],[27],"多个产业证据相当，需要确认主归属",[508,509],"旱地混作系统 氮利用效率","机器学习 作物产量预测","旱地混作系统氮利用效率-4732","10.1007\u002Fs44264-025-00012-8",{"doi":511,"openalex_id":9,"authors":513,"venue":9,"cited_by_count":83,"oa_url":9,"card":514,"direction":172,"ingested_from":519},[],{"tldr":515,"method":516,"finding":517,"direction":172,"opportunity":518},"综述AI、物联网与遥感在旱地混作系统的应用，评估其对氮利用与产量预测的提升。","整合1990-2025年158篇文献，分析精准施肥、机器学习与卫星遥感数据。","精准施肥使氮利用效率提高超15%，机器学习结合卫星数据可解释超70%产量变异。","数字可及性、基础设施与社会经济制约是采纳瓶颈，可研究低成本适配旱地小农的智能方案。","agent","2026-10-12T00:11:14.741867Z"]