[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4732":3,"related-4732":86},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":8,"secondary_topics":30,"classification":31,"search_phrases":70,"slug":73,"view_count":20,"doi":74,"paper":75,"created_at":85},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",null,"Discover Agriculture 2026-10-09","2026-10-09T00:00:00Z","论文",10,false,81,{"impact":16,"substance":16,"depth":17,"authority":18,"freshness":19,"relevant":20,"comment":21},22,18,13,6,1,"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。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","精准施肥","遥感监测","旱地农业",[],{"status":32,"locked":13,"version":33,"method":34,"evidence":35},"needs_review","2026-10-22.2","rules",{"candidates":36,"agricultural_evidence":40,"reason":69},[37,45,51,57,64],{"code":38,"score":39,"supported":40,"title":41,"abstract":42,"tags":44},"F3",4,true,[],[43],"施肥",[27],{"code":46,"score":39,"supported":40,"title":47,"abstract":48,"tags":50},"I2",[],[49],"机器学习",[26],{"code":52,"score":39,"supported":40,"title":53,"abstract":54,"tags":56},"I3",[],[55],"遥感",[28],{"code":58,"score":59,"supported":40,"title":60,"abstract":61,"tags":63},"I5",3,[],[62],"物联网",[],{"code":65,"score":20,"supported":13,"title":66,"abstract":67,"tags":68},"I1",[],[],[25],"多个产业证据相当，需要确认主归属",[71,72],"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":74,"openalex_id":8,"authors":76,"venue":8,"cited_by_count":77,"oa_url":8,"card":78,"direction":82,"ingested_from":84},[],0,{"tldr":79,"method":80,"finding":81,"direction":82,"opportunity":83},"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。","智慧农业 \u002F 农业物联网","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",{"total":19,"page":20,"page_size":19,"items":87},[88,170,257,353,418,495],{"id":89,"title":90,"url":91,"summary":92,"summary_zh":93,"content":8,"source_name":94,"source_url":91,"published_at":95,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":96,"score_detail":97,"sources":102,"tags":104,"primary_topic":107,"secondary_topics":112,"classification":113,"search_phrases":152,"slug":155,"view_count":77,"doi":156,"paper":157,"created_at":169},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":98,"substance":99,"depth":18,"authority":19,"freshness":100,"relevant":20,"comment":101},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。",[103],{"name":94,"url":91},[25,26,28,105,106],"风险管理","病虫害监测",{"code":108,"slug":109,"name":110,"path":111,"parent_code":8},"I","digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":114,"locked":13,"version":33,"method":34,"evidence":115},"partial",{"candidates":116,"agricultural_evidence":40,"reason":151},[117,124,131,137,144],{"code":46,"score":12,"supported":40,"title":118,"abstract":119,"tags":123},[],[49,120,121,122],"machine learning","artificial intelligence","decision support",[26],{"code":52,"score":12,"supported":40,"title":125,"abstract":126,"tags":130},[],[55,127,128,129],"remote sensing","multispectral","hyperspectral",[28],{"code":58,"score":100,"supported":40,"title":132,"abstract":133,"tags":136},[],[62,134,135],"internet of things","IoT",[],{"code":65,"score":138,"supported":40,"title":139,"abstract":140,"tags":143},7,[],[141,142],"精准农业","precision agriculture",[25],{"code":145,"score":19,"supported":40,"title":146,"abstract":147,"tags":150},"H7",[],[148,149],"气候变化","climate change",[],"一级领域明确，二级方向需要细分",[153,154],"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":156,"openalex_id":158,"authors":159,"venue":94,"cited_by_count":77,"oa_url":91,"card":162,"direction":82,"ingested_from":168},"W7223085400",[160],{"name":161,"orcid":8},"Dr. Geetha Govindan K Gopalakrishnan",{"tldr":163,"method":164,"finding":165,"direction":166,"opportunity":167},"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。","农业人工智能与决策模型","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。","openalex","2026-10-22T11:20:24.428080Z",{"id":171,"title":172,"url":173,"summary":174,"summary_zh":175,"content":8,"source_name":176,"source_url":173,"published_at":95,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":177,"score_detail":178,"sources":180,"tags":182,"primary_topic":185,"secondary_topics":189,"classification":190,"search_phrases":233,"slug":236,"view_count":77,"doi":237,"paper":238,"created_at":256},5559,"Smart AI-Based Agriculture Decision Support System (AgriSens)","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.85162","Traditional farming relies heavily on empirical guesswork and legacy ancestral knowledge, frequently leaving crops vulnerable to uncalibrated chemical fertilizer application, unpredictable micro-climates, and delayed foliar disease detection. This paper introduces AgriSens, a comprehensive, full-stack smart farming platform developed to empower farmers with datadriven precision agriculture. The system integrates a multi-parameter crop recommendation engine using a mathematical Gaussian-decay environmental fitness model alongside a multimodal leaf disease diagnostic system powered by Google Gemini 3.7 Flash computer vision to identify foliar pathogens from smartphone imagery. Furthermore, the architecture deploys a soiltexture-aware fertilizer optimization engine that calculates precise N-P-K (Nitrogen, Phosphorus, Potassium) nutrient deficits, a real-time agro-meteorological forecasting stream driven by the Open-Meteo API for chemical spray safety alerts, and a closedloop automated IoT irrigation telemetry simulator. To eliminate structural literacy barriers within rural farming communities, the platform features native voice synthesis and a 7-language localized translation module. Simulation benchmarks demonstrate that the platform achieves rapid visual diagnostic latencies under 2.5 seconds and optimizes water conservation by 25% to 35% compared to baseline scheduled flooding methods","传统农业严重依赖经验性猜测和祖辈传承的知识，作物常常面临化肥施用不当、微气候难以预测以及叶部病害发现滞后等问题。本文介绍AgriSens——一个综合性全栈智慧农业平台，旨在以数据驱动的精准农业赋能农民。该系统集成了基于高斯衰减环境适应度数学模型的多元参数作物推荐引擎，以及由Google Gemini 3.7 Flash计算机视觉驱动的多模态叶片病害诊断系统，可从智能手机图像中识别叶部病原体。此外，该架构还部署了土壤质地感知的肥料优化引擎，用于计算精确的氮磷钾（N-P-K）养分亏缺；由Open-Meteo API驱动的实时农业气象预报流，用于提供施药安全预警；以及闭环自动化物联网灌溉遥测模拟器。为消除农村农业社区中的结构性识字障碍，该平台具备原生语音合成功能和7种语言的本地化翻译模块。仿真基准测试表明，该平台实现了低于2.5秒的快速视觉诊断延迟，并且与基准定时漫灌方法相比，将节水效率优化了25%至35%。","International Journal for Research in Applied Science and Engineering Technology",65,{"impact":98,"substance":17,"depth":99,"authority":100,"freshness":12,"relevant":20,"comment":179},"论文提出集作物推荐、病害诊断、施肥优化与灌溉模拟于一体的全栈平台，方法有创新但尚属仿真验证阶段，具备一定参考价值。",[181],{"name":176,"url":173},[25,26,27,183,184],"智能灌溉","病害诊断",{"code":46,"slug":186,"name":187,"path":188,"parent_code":108},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[],{"status":191,"locked":13,"version":33,"method":34,"evidence":192},"classified",{"candidates":193,"agricultural_evidence":40},[194,201,205,211,218,222,228],{"code":46,"score":195,"supported":40,"title":196,"abstract":197,"tags":200},15,[122],[198,199],"计算机视觉","computer vision",[26],{"code":65,"score":12,"supported":40,"title":202,"abstract":203,"tags":204},[],[25,141,142],[25],{"code":38,"score":138,"supported":40,"title":206,"abstract":207,"tags":210},[],[208,209],"肥料","fertilizer",[27],{"code":212,"score":138,"supported":40,"title":213,"abstract":214,"tags":217},"H3",[],[215,216],"灌溉","irrigation",[183],{"code":58,"score":19,"supported":40,"title":219,"abstract":220,"tags":221},[],[62,135],[],{"code":223,"score":59,"supported":40,"title":224,"abstract":225,"tags":227},"H2",[],[226],"土壤",[],{"code":145,"score":59,"supported":40,"title":229,"abstract":230,"tags":232},[],[231],"农业气象",[],[234,235],"AgriSens 智慧农业","Gemini 叶片病害诊断","AgriSens智慧农业-5559","10.22214\u002Fijraset.2026.85162",{"doi":237,"openalex_id":239,"authors":240,"venue":176,"cited_by_count":77,"oa_url":173,"card":251,"direction":82,"ingested_from":168},"W7223158459",[241,243,245,247,249],{"name":242,"orcid":8},"Harshini G. K.",{"name":244,"orcid":8},"Monisha B. P.",{"name":246,"orcid":8},"Manoj G.",{"name":248,"orcid":8},"Chandana K. M.",{"name":250,"orcid":8},"Prof. Sushma S.",{"tldr":252,"method":253,"finding":254,"direction":166,"opportunity":255},"构建AgriSens全栈智慧农业决策支持平台，集成作物推荐、病害诊断、施肥优化与灌溉模拟。","高斯衰减环境适应度模型、Gemini视觉诊断、Open-Meteo API与Io","视觉诊断延迟低于2.5秒，节水25%–35%，并支持7语言语音以降低识字门槛。","可验证多模态病害诊断在真实田间的准确率，并研究语音多语言交互对农户采纳行为的影响。","2026-10-22T11:20:18.985947Z",{"id":258,"title":259,"url":260,"summary":261,"summary_zh":262,"content":8,"source_name":263,"source_url":260,"published_at":264,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":265,"score_detail":266,"sources":269,"tags":271,"primary_topic":275,"secondary_topics":280,"classification":281,"search_phrases":334,"slug":337,"view_count":338,"doi":339,"paper":340,"created_at":352},5235,"Role of Smart Fertilization in Modern Agriculture","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fijpss\u002F2026\u002Fv38i106378","Global food demand is increasing rapidly owing to population growth, placing considerable pressure on agricultural systems to enhance productivity under constraints on natural resources. Although conventional fertiliser use has contributed significantly to improved yields, its excessive and imbalanced application has resulted in soil degradation, nutrient leaching, groundwater contamination, and increased emissions of nitrous oxide (N₂O), thereby reducing nutrient use efficiency (NUE). In this context, smart fertilisation has emerged as a science-driven nutrient management strategy that emphasises the application of the right nutrient source at the right rate, time and place (4R principle), supported by advanced tools such as soil and plant sensors, remote sensing, geographic information systems, and decision-support platforms. Key components include controlled-release fertilisers, nano-fertilisers, bio-formulations, fertigation systems, variable rate technology (VRT), and unmanned aerial vehicle (UAV)-based crop monitoring. Evidence from multi-location field studies across diverse agro-climatic regions of India and other countries indicates that smart fertilisation can enhance nutrient use efficiency by 50–80%, reduce fertiliser input by 19–30%, and improve crop yields by 22–30% in major crops such as rice, wheat, maize and pulses. With the global fertiliser market exceeding USD 200 billion, the adoption of precision nutrient management technologies is gaining momentum. Overall, smart fertilisation offers a sustainable intensification pathway by improving productivity while simultaneously minimising environmental externalities. However, challenges related to technology accessibility, cost, and farmer adoption remain critical constraints that require policy and institutional support.","由于人口增长，全球粮食需求迅速上升，给农业系统带来了巨大压力，要求在自然资源约束下提高生产力。尽管常规施肥对提高产量作出了显著贡献，但过量和不平衡施肥已导致土壤退化、养分淋失、地下水污染以及氧化亚氮（N₂O）排放增加，从而降低了养分利用效率（NUE）。在此背景下，智能施肥作为一种以科学为驱动的养分管理策略应运而生，强调在正确的时间、正确的地点以正确的用量施用正确的养分来源（4R原则），并借助土壤与植物传感器、遥感、地理信息系统和决策支持平台等先进工具加以支撑。其关键组成部分包括控释肥料、纳米肥料、生物制剂、水肥一体化系统、变量施肥技术（VRT）以及基于无人机（UAV）的作物监测。来自印度及其他国家不同农业气候区多点田间研究的数据表明，智能施肥可使养分利用效率提高50%–80%，减少肥料投入19%–30%，并使水稻、小麦、玉米和豆类等主要作物增产22%–30%。随着全球肥料市场规模超过2000亿美元，精准养分管理技术的采用正在加速推进。总体而言，智能施肥通过提高生产力同时最大限度减少环境负外部性，提供了一条可持续集约化路径。然而，技术可及性、成本和农民采用等方面的挑战仍是关键制约因素，需要政策和制度支持。","International Journal of Plant & Soil Science","2026-10-17T00:00:00Z",83,{"impact":16,"substance":16,"depth":17,"authority":18,"freshness":267,"relevant":20,"comment":268},8,"系统综述智能施肥技术体系与多国田间数据，量化增产节肥效果，对智慧农业与绿色投入品方向有参考价值，但属综述性论文且非国内案例，时效与权威度中等。",[270],{"name":263,"url":260},[25,272,27,273,28,274],"无人机","变量施肥","养分利用效率",{"code":38,"slug":276,"name":277,"path":278,"parent_code":279},"fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",[],{"status":191,"locked":13,"version":33,"method":34,"evidence":282},{"candidates":283,"agricultural_evidence":40},[284,293,297,304,311,318,323,330],{"code":38,"score":285,"supported":40,"title":286,"abstract":288,"tags":292},20,[287],"fertilization",[43,208,289,290,291],"水肥一体化","养分管理","fertigation",[274,273,27],{"code":52,"score":138,"supported":40,"title":294,"abstract":295,"tags":296},[],[55,127],[28],{"code":298,"score":19,"supported":40,"title":299,"abstract":300,"tags":303},"A1",[],[301,302],"水稻","rice",[],{"code":305,"score":19,"supported":40,"title":306,"abstract":307,"tags":310},"A2",[],[308,309],"小麦","wheat",[],{"code":312,"score":19,"supported":40,"title":313,"abstract":314,"tags":317},"A3",[],[315,316],"玉米","maize",[],{"code":223,"score":19,"supported":40,"title":319,"abstract":320,"tags":322},[],[226,321],"soil",[],{"code":324,"score":19,"supported":40,"title":325,"abstract":326,"tags":329},"I4",[],[327,328],"地理信息","geographic information",[],{"code":65,"score":20,"supported":13,"title":331,"abstract":332,"tags":333},[],[],[25],[335,336],"智能施肥 4R原则","变量施肥技术 无人机","智能施肥4R原则-5235",2,"10.9734\u002Fijpss\u002F2026\u002Fv38i106378",{"doi":339,"openalex_id":341,"authors":342,"venue":263,"cited_by_count":77,"oa_url":260,"card":347,"direction":82,"ingested_from":168},"W7222838957",[343,345],{"name":344,"orcid":8},"S. T. Panusha",{"name":346,"orcid":8},"T. M. Soumya",{"tldr":348,"method":349,"finding":350,"direction":82,"opportunity":351},"综述智能施肥（4R原则+传感器\u002F遥感\u002FVRT）提升养分利用效率与产量的机制与证据。","整合多地点田间试验证据，梳理传感器、遥感、GIS、控释\u002F纳米肥与无人机监测等技术","智能施肥可提高养分利用效率50–80%、减施化肥19–30%、增产22–30%，但受成本与农户采纳制","可研究低成本传感器+无人机变量施肥在小农户场景的适配与采纳机制，及减氮减排的量化评估。","2026-10-18T23:30:07.850055Z",{"id":354,"title":355,"url":356,"summary":357,"summary_zh":358,"content":8,"source_name":359,"source_url":356,"published_at":360,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":361,"score_detail":362,"sources":366,"tags":368,"primary_topic":370,"secondary_topics":374,"classification":375,"search_phrases":402,"slug":405,"view_count":77,"doi":406,"paper":407,"created_at":417},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":99,"substance":363,"depth":364,"authority":98,"freshness":100,"relevant":20,"comment":365},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。",[367],{"name":359,"url":356},[25,26,62,369,28],"作物胁迫",{"code":58,"slug":371,"name":372,"path":373,"parent_code":108},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",[],{"status":191,"locked":13,"version":33,"method":34,"evidence":376},{"candidates":377,"agricultural_evidence":40},[378,382,386,390,394,398],{"code":58,"score":17,"supported":40,"title":379,"abstract":380,"tags":381},[135],[62,134,135],[62],{"code":52,"score":138,"supported":40,"title":383,"abstract":384,"tags":385},[],[55,127],[28],{"code":223,"score":19,"supported":40,"title":387,"abstract":388,"tags":389},[],[226,321],[],{"code":212,"score":19,"supported":40,"title":391,"abstract":392,"tags":393},[],[215,216],[],{"code":65,"score":20,"supported":13,"title":395,"abstract":396,"tags":397},[],[],[25],{"code":46,"score":20,"supported":13,"title":399,"abstract":400,"tags":401},[],[],[26],[403,404],"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":406,"openalex_id":408,"authors":409,"venue":359,"cited_by_count":77,"oa_url":356,"card":412,"direction":82,"ingested_from":168},"W7222730658",[410],{"name":411,"orcid":8},"Sunitha Thankappan",{"tldr":413,"method":414,"finding":415,"direction":82,"opportunity":416},"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":419,"title":420,"url":421,"summary":422,"summary_zh":423,"content":8,"source_name":94,"source_url":421,"published_at":424,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":425,"score_detail":426,"sources":428,"tags":430,"primary_topic":432,"secondary_topics":433,"classification":439,"search_phrases":478,"slug":481,"view_count":20,"doi":482,"paper":483,"created_at":494},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":17,"substance":99,"depth":195,"authority":98,"freshness":100,"relevant":20,"comment":427},"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。",[429],{"name":94,"url":421},[25,26,431,148,28],"可持续发展",{"code":46,"slug":186,"name":187,"path":188,"parent_code":108},[434],{"code":145,"slug":435,"name":436,"path":437,"parent_code":438},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":191,"locked":13,"version":33,"method":34,"evidence":440},{"candidates":441,"agricultural_evidence":40},[442,448,452,456,460,467,474],{"code":46,"score":17,"supported":40,"title":443,"abstract":444,"tags":447},[121],[445,49,120,446,121],"深度学习","deep learning",[26],{"code":145,"score":195,"supported":40,"title":449,"abstract":450,"tags":451},[149],[148,149],[148],{"code":65,"score":138,"supported":40,"title":453,"abstract":454,"tags":455},[],[141,142],[25],{"code":52,"score":138,"supported":40,"title":457,"abstract":458,"tags":459},[],[55,127],[28],{"code":461,"score":19,"supported":40,"title":462,"abstract":463,"tags":466},"H4",[],[464,465],"碳排放","carbon emission",[],{"code":468,"score":19,"supported":40,"title":469,"abstract":470,"tags":473},"H6",[],[471,472],"生物多样性","biodiversity",[],{"code":58,"score":19,"supported":40,"title":475,"abstract":476,"tags":477},[],[62,134],[],[479,480],"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":482,"openalex_id":484,"authors":485,"venue":94,"cited_by_count":77,"oa_url":421,"card":488,"direction":82,"ingested_from":168},"W7222092855",[486],{"name":487,"orcid":8},"Deepmala Suryavanshi",{"tldr":489,"method":490,"finding":491,"direction":492,"opportunity":493},"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":496,"title":497,"url":498,"summary":499,"summary_zh":500,"content":8,"source_name":501,"source_url":498,"published_at":502,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":503,"score_detail":504,"sources":506,"tags":508,"primary_topic":511,"secondary_topics":512,"classification":514,"search_phrases":536,"slug":539,"view_count":59,"doi":540,"paper":541,"created_at":553},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":98,"substance":17,"depth":99,"authority":18,"freshness":100,"relevant":20,"comment":505},"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。",[507],{"name":501,"url":498},[25,26,509,28,510],"农业物联网","作物估产",{"code":58,"slug":371,"name":372,"path":373,"parent_code":108},[513],{"code":46,"slug":186,"name":187,"path":188,"parent_code":108},{"status":191,"locked":13,"version":33,"method":34,"evidence":515},{"candidates":516,"agricultural_evidence":40},[517,523,528,532],{"code":58,"score":17,"supported":40,"title":518,"abstract":519,"tags":522},[135],[62,520,134,135,521],"边缘计算","edge computing",[509],{"code":65,"score":195,"supported":40,"title":524,"abstract":526,"tags":527},[525],"smart agriculture",[141,142],[25],{"code":46,"score":12,"supported":40,"title":529,"abstract":530,"tags":531},[],[445,49,120,446,121],[26],{"code":52,"score":338,"supported":13,"title":533,"abstract":534,"tags":535},[],[],[510,28],[537,538],"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","10.1007\u002Fs12083-026-02299-3",{"doi":540,"openalex_id":542,"authors":543,"venue":501,"cited_by_count":77,"oa_url":498,"card":548,"direction":82,"ingested_from":168},"W7221560555",[544,546],{"name":545,"orcid":8},"Keerthana T",{"name":547,"orcid":8},"Janarthanan R",{"tldr":549,"method":550,"finding":551,"direction":82,"opportunity":552},"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"]