[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1364":3,"related-1364":111},{"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":94,"slug":97,"view_count":98,"doi":99,"paper":100,"created_at":110},1364,"【综述】多组学与人工智能赋能气候适应型和营养强化作物：进展、应用与未来展望","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1931230\u002Ffull","综述整合 AI 驱动的多组学、基因编辑与精准农业进展。机器学习增强的基因组选择（如 CNN、Transformer）提高复杂抗逆与营养性状预测准确度；AI+无人机遥感实现高通量表型；AI+CRISPR-Cas9 加速生物强化目标筛选（小麦铁锌生物有效性、大豆植酸降低、木薯\u002F玉米维生素提升）；为 2050 年粮食安全提出路线图。",null,"Frontiers in Sustainable Food Systems · 2026-09","2026-08-31T16:00:00Z","论文",10,false,83,{"impact":16,"substance":16,"depth":17,"authority":18,"freshness":19,"relevant":20,"comment":21},22,19,13,7,1,"综述整合AI与多组学在作物育种中的应用，提出2050年路线图，具有产业前瞻性。",[23],{"name":9,"url":6},[25,26,27,28,29],"农业人工智能","粮食安全","精准农业","基因编辑","多组学",[],{"status":32,"locked":13,"version":33,"method":34,"evidence":35},"needs_review","2026-10-22.2","rules",{"candidates":36,"agricultural_evidence":40,"reason":93},[37,45,51,57,62,68,75,81,87],{"code":38,"score":39,"supported":40,"title":41,"abstract":42,"tags":44},"F2",8,true,[],[28,43],"基因组选择",[28,29],{"code":46,"score":39,"supported":40,"title":47,"abstract":49,"tags":50},"H7",[48],"气候适应",[],[],{"code":52,"score":53,"supported":40,"title":54,"abstract":55,"tags":56},"A8",4,[],[26],[26],{"code":58,"score":53,"supported":40,"title":59,"abstract":60,"tags":61},"I1",[],[27],[27],{"code":63,"score":53,"supported":40,"title":64,"abstract":65,"tags":67},"I2",[],[66],"机器学习",[25],{"code":69,"score":70,"supported":40,"title":71,"abstract":72,"tags":74},"A2",3,[],[73],"小麦",[],{"code":76,"score":70,"supported":40,"title":77,"abstract":78,"tags":80},"A3",[],[79],"玉米",[],{"code":82,"score":70,"supported":40,"title":83,"abstract":84,"tags":86},"A4",[],[85],"大豆",[],{"code":88,"score":70,"supported":40,"title":89,"abstract":90,"tags":92},"I3",[],[91],"遥感",[],"多个产业证据相当，需要确认主归属",[95,96],"农业人工智能 基因编辑 粮食安全 精准农业","农业人工智能 基因编辑","农业人工智能基因编辑粮食安全精准农业-1364",2,"10.3389\u002Ffsufs.2026.1931230\u002Ffull",{"doi":99,"openalex_id":8,"authors":101,"venue":8,"cited_by_count":102,"oa_url":8,"card":103,"direction":107,"ingested_from":109},[],0,{"tldr":104,"method":105,"finding":106,"direction":107,"opportunity":108},"综述AI与多组学、基因编辑、精准农业结合，提升作物抗逆与营养性状改良效率。","综述AI驱动的多组学、基因编辑与精准农业技术，包括机器学习基因组选择、无人机遥感","AI增强基因组选择、表型分析和基因编辑，可加速气候适应与营养强化作物育种。","农业人工智能与决策模型","可探索AI与多组学整合在特定作物（如小麦、大豆）营养强化中的实际应用与模型优化。","agent","2026-09-02T00:05:06.849146Z",{"total":112,"page":20,"page_size":112,"items":113},6,[114,226,347,412,470,562],{"id":115,"title":116,"url":117,"summary":118,"summary_zh":119,"content":8,"source_name":120,"source_url":117,"published_at":121,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":122,"score_detail":123,"sources":127,"tags":129,"primary_topic":132,"secondary_topics":137,"classification":148,"search_phrases":207,"slug":209,"view_count":98,"doi":210,"paper":211,"created_at":225},2625,"Multi-omics and artificial intelligence for climate-resilient and nutrient-enriched food crops: advances, applications and future perspectives","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1931230","Climate change has intensified both abiotic and biotic stresses, threatening global crop productivity, nutritional quality, and agricultural sustainability. These challenges have accelerated the adoption of advanced multi-omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, together with artificial intelligence (AI) to accelerate the development of climate-resilient and nutrient-enriched crops. This review provides an integrated view of the recent advances in AI-enabled multi-omics, genome editing and precision agriculture for climate-resilient and nutrient-rich crops along with emerging trends, current challenges, and future research priorities. Machine learning-enhanced genomic selection approaches such as convolutional neural network (CNN)-based models and transformer models enhance the accuracy of predictions for complex stress-tolerant and nutritional traits. AI-assisted unmanned aerial vehicle (UAV) remote sensing allows measurement of traits at an unprecedented scale and with high-throughput and quantitative phenotyping. AI-driven multi-omics integration approaches, such as pan-omics pipelines, decipher abiotic stress tolerance regulatory networks. The AI-assisted CRISPR-Cas9 genome editing systems have accelerated crop biofortification outcomes by improving target prioritization and guide RNA design. Genome-editing studies have reported enhanced iron and zinc bioavailability in experimental wheat lines, reduced phytic acid content in soybean, and enhanced vitamin and amino acid content in cassava and maize, highlighting the potential of AI-assisted genome-editing approaches for crop biofortification. AI-enabled precision agriculture platforms use AI together with satellite data and Internet of Things (IoT) sensors and predictive modeling to enhance crop management while decreasing environmental resource usage. This review examines data standardization and model interpretability together with access and regulations while it presents a future roadmap for achieving 2050 food security targets. By integrating multi-omics, artificial intelligence, genome engineering, and precision agriculture into a unified framework, this review identifies future research directions and key knowledge gaps for the development of climate-resilient and nutrient-rich crops.","气候变化加剧了非生物与生物胁迫，对全球作物生产力、营养品质和农业可持续性构成威胁。这些挑战加速了先进多组学技术（包括基因组学、转录组学、蛋白质组学和代谢组学）以及人工智能（AI）的应用，以加快气候韧性及营养强化作物的开发。本综述综合阐述了AI赋能的多组学、基因组编辑和精准农业在气候韧性及营养丰富作物方面的最新进展，以及新兴趋势、当前挑战和未来研究重点。机器学习增强的基因组选择方法，如基于卷积神经网络（CNN）的模型和Transformer模型，提高了对复杂耐逆性和营养性状预测的准确性。AI辅助的无人机（UAV）遥感能够以前所未有的规模进行高通量定量表型测量。AI驱动的多组学整合方法，如泛组学流程，可解析非生物胁迫耐受调控网络。AI辅助的CRISPR-Cas9基因组编辑系统通过改进靶标优先级排序和引导RNA设计，加速了作物生物强化成果。基因组编辑研究报告了实验小麦品系中铁和锌生物利用度的提高、大豆中植酸含量的降低，以及木薯和玉米中维生素和氨基酸含量的增强，凸显了AI辅助基因组编辑方法在作物生物强化方面的潜力。AI赋能的精准农业平台利用AI结合卫星数据、物联网（IoT）传感器和预测建模，在减少环境资源使用的同时增强作物管理。本综述审视了数据标准化和模型可解释性以及可及性和监管问题，同时提出了实现2050年粮食安全目标的未来路线图。通过将多组学、人工智能、基因组工程和精准农业整合为统一框架，本综述确定了开发气候韧性及营养丰富作物的未来研究方向和关键知识空白。","Frontiers in Sustainable Food Systems","2026-09-16T00:00:00Z",86,{"impact":16,"substance":16,"depth":17,"authority":124,"freshness":125,"relevant":20,"comment":126},14,9,"核心期刊综述，系统梳理AI驱动多组学、基因编辑与精准农业在气候韧性及营养强化作物上的进展，方法新颖、结论可靠，对智慧育种与农业AI方向有较高参考价值。",[128],{"name":120,"url":117},[25,27,28,130,131,29],"气候韧性","智慧育种",{"code":63,"slug":133,"name":134,"path":135,"parent_code":136},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[138,143],{"code":38,"slug":139,"name":140,"path":141,"parent_code":142},"biotechnology","生物育种与基因技术","\u002Ftopic\u002Finputs-equipment\u002Fbiotechnology","F",{"code":144,"slug":145,"name":146,"path":147,"parent_code":136},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":149,"locked":13,"version":33,"method":34,"evidence":150},"classified",{"candidates":151,"agricultural_evidence":40},[152,159,164,171,177,182,187,192,197,202],{"code":63,"score":153,"supported":40,"title":154,"abstract":156,"tags":158},18,[155],"artificial intelligence",[66,157,155],"machine learning",[25],{"code":38,"score":125,"supported":40,"title":160,"abstract":161,"tags":163},[],[43,162],"genomic selection",[28,29,131],{"code":144,"score":125,"supported":40,"title":165,"abstract":166,"tags":170},[],[167,168,169],"物联网","internet of things","IoT",[],{"code":46,"score":19,"supported":40,"title":172,"abstract":173,"tags":176},[],[174,175],"气候变化","climate change",[130],{"code":58,"score":19,"supported":40,"title":178,"abstract":179,"tags":181},[],[27,180],"precision agriculture",[27],{"code":69,"score":112,"supported":40,"title":183,"abstract":184,"tags":186},[],[73,185],"wheat",[],{"code":76,"score":112,"supported":40,"title":188,"abstract":189,"tags":191},[],[79,190],"maize",[],{"code":82,"score":112,"supported":40,"title":193,"abstract":194,"tags":196},[],[85,195],"soybean",[],{"code":52,"score":112,"supported":40,"title":198,"abstract":199,"tags":201},[],[26,200],"food security",[],{"code":88,"score":112,"supported":40,"title":203,"abstract":204,"tags":206},[],[91,205],"remote sensing",[],[208,96],"农业人工智能 基因编辑 智慧育种 气候韧性","农业人工智能基因编辑智慧育种气候韧性-2625","10.3389\u002Ffsufs.2026.1931230",{"doi":210,"openalex_id":212,"authors":213,"venue":120,"cited_by_count":102,"oa_url":117,"card":218,"direction":223,"ingested_from":224},"W7213241547",[214,216],{"name":215,"orcid":8},"Surasreeta Paul",{"name":217,"orcid":8},"Sandeep Singh Rana",{"tldr":219,"method":220,"finding":221,"direction":107,"opportunity":222},"综述AI赋能多组学、基因编辑与精准农业，培育气候韧性与营养强化作物。","整合基因组、转录组、蛋白与代谢组学，结合CNN、Transformer、UAV遥","AI辅助多组学与基因编辑可提升耐逆与营养性状预测及生物强化效率。","多组学数据标准化与模型可解释性不足，可研究跨物种可迁移的AI决策框架。","智慧农业 \u002F 农业物联网","openalex","2026-09-16T23:30:07.496425Z",{"id":227,"title":228,"url":229,"summary":230,"summary_zh":231,"content":8,"source_name":232,"source_url":229,"published_at":233,"category":11,"cover_url":8,"hotness":12,"is_selected":40,"score":14,"score_detail":234,"sources":237,"tags":239,"primary_topic":243,"secondary_topics":249,"classification":250,"search_phrases":312,"slug":315,"view_count":102,"doi":316,"paper":317,"created_at":346},4832,"Applications and Future Prospects of Plant Multi-Omics Technologies in Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fijms27209034","Against the backdrop of global population growth, intensifying climate change, and limited arable land, ensuring food security and promoting sustainable agriculture have become critical issues. The limitations of conventional agricultural systems are becoming increasingly apparent, creating an urgent need for innovation driven by science and technology. In this context, plant multi-omics technologies serve as the foundational data and technical cornerstone of systems biology and precision agriculture. Integrating multi-level data from genomics, transcriptomics, proteomics, metabolomics, epigenomics, and microbiomics offers substantial advantages for systematically understanding and improving plants. Unlike previous reviews that mainly catalog omics technologies or focus on individual crops, this review adopts a unified discovery-to-translation framework and critically evaluates the strength of evidence across applications. This review focuses on cutting-edge applications of multi-omics in three key areas: improving the traits of cereal crops (rice, wheat and maize), remediating soil health, and innovating products such as biofertilizers and biostimulants for agriculture. We systematically explore the roles of multi-omics in accelerating breeding processes, deciphering complex agronomic traits, restoring soil ecosystems, and constructing intelligent decision-support systems. The review also examines current challenges, including the complexity of data integration and the difficulties of predicting genotype-by-environment interactions. Furthermore, it outlines future directions involving deeper convergence with artificial intelligence, high-throughput phenomics, and digital twins. In summary, plant multi-omics technology is a vital tool for unravelling the mysteries of plant life and is also a key enabler of intelligent, precise and sustainable agricultural transformation.","在全球人口增长、气候变化加剧和耕地有限的背景下，保障粮食安全和促进可持续农业已成为关键议题。传统农业系统的局限性日益显现，迫切需要由科学和技术驱动的创新。在此背景下，植物多组学技术作为系统生物学和精准农业的基础数据与技术基石，整合基因组学、转录组学、蛋白质组学、代谢组学、表观基因组学和微生物组学等多层次数据，为系统理解和改良植物提供了显著优势。与以往主要罗列组学技术或聚焦单一作物的综述不同，本综述采用统一的“从发现到转化”框架，并批判性评估各应用中的证据强度。本综述聚焦多组学在三个关键领域的前沿应用：改良谷类作物（水稻、小麦和玉米）的性状、修复土壤健康，以及创新生物肥料和生物刺激素等农业产品。我们系统探讨了多组学在加速育种进程、解析复杂农艺性状、恢复土壤生态系统和构建智能决策支持系统中的作用。本综述还审视了当前挑战，包括数据整合的复杂性和预测基因型与环境互作的困难。此外，文章展望了未来方向，涉及与人工智能、高通量表型组学和数字孪生的更深层次融合。总之，植物多组学技术是揭示植物生命奥秘的重要工具，也是智能、精准和可持续农业转型的关键使能技术。","International Journal of Molecular Sciences","2026-10-12T00:00:00Z",{"impact":16,"substance":235,"depth":153,"authority":124,"freshness":39,"relevant":20,"comment":236},21,"核心期刊综述，系统梳理植物多组学在精准农业中的应用与AI、数字孪生等未来方向，专业深度与信源权威度俱佳，值得进入每日精选。",[238],{"name":232,"url":229},[240,25,241,27,242,29],"智慧农业","种业振兴","土壤健康",{"code":244,"slug":245,"name":246,"path":247,"parent_code":248},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":149,"locked":13,"version":33,"method":34,"evidence":251},{"candidates":252,"agricultural_evidence":40},[253,258,263,270,274,278,282,286,293,297,303,308],{"code":58,"score":17,"supported":40,"title":254,"abstract":255,"tags":257},[180],[27,256,180],"数字孪生",[240,27],{"code":244,"score":19,"supported":40,"title":259,"abstract":260,"tags":262},[],[261,245],"土壤",[242],{"code":264,"score":112,"supported":40,"title":265,"abstract":266,"tags":269},"A1",[],[267,268],"水稻","rice",[],{"code":69,"score":112,"supported":40,"title":271,"abstract":272,"tags":273},[],[73,185],[],{"code":76,"score":112,"supported":40,"title":275,"abstract":276,"tags":277},[],[79,190],[],{"code":52,"score":112,"supported":40,"title":279,"abstract":280,"tags":281},[],[26,200],[],{"code":46,"score":112,"supported":40,"title":283,"abstract":284,"tags":285},[],[174,175],[],{"code":287,"score":112,"supported":40,"title":288,"abstract":289,"tags":292},"H8",[],[290,291],"可持续农业","sustainable agriculture",[],{"code":63,"score":53,"supported":40,"title":294,"abstract":295,"tags":296},[],[155],[25],{"code":298,"score":70,"supported":40,"title":299,"abstract":300,"tags":302},"F3",[],[301],"肥料",[],{"code":304,"score":20,"supported":13,"title":305,"abstract":306,"tags":307},"F1",[],[],[241],{"code":38,"score":20,"supported":13,"title":309,"abstract":310,"tags":311},[],[],[29],[313,314],"植物多组学 精准农业","多组学 智能育种","植物多组学精准农业-4832","10.3390\u002Fijms27209034",{"doi":316,"openalex_id":318,"authors":319,"venue":232,"cited_by_count":102,"oa_url":229,"card":340,"direction":345,"ingested_from":224},"W7221567543",[320,323,325,328,330,332,335,338],{"name":321,"orcid":322},"Tao Tong","https:\u002F\u002Forcid.org\u002F0000-0002-9003-9667",{"name":324,"orcid":8},"Wang Qingxia",{"name":326,"orcid":327},"Fanrong Zeng","https:\u002F\u002Forcid.org\u002F0000-0001-7742-5195",{"name":329,"orcid":8},"Shuzhen Ye",{"name":331,"orcid":8},"Zhijuan Ji",{"name":333,"orcid":334},"Zhong‐Hua Chen","https:\u002F\u002Forcid.org\u002F0000-0002-7531-320X",{"name":336,"orcid":337},"Jiaoyu Wang","https:\u002F\u002Forcid.org\u002F0000-0002-0249-808X",{"name":339,"orcid":8},"Younan Ouyang",{"tldr":341,"method":342,"finding":343,"direction":107,"opportunity":344},"综述植物多组学在精准农业中的应用，涵盖作物改良、土壤修复和生物产品创新。","整合基因组、转录组、蛋白组、代谢组等多组学数据，采用发现到转化框架。","多组学可加速育种、解析复杂农艺性状、修复土壤生态并构建智能决策系统。","多组学与AI、高通量表型、数字孪生融合，解决基因型-环境互作预测难题。","数字乡村与农业信息化","2026-10-13T23:30:29.504594Z",{"id":348,"title":349,"url":350,"summary":351,"summary_zh":352,"content":8,"source_name":353,"source_url":350,"published_at":354,"category":11,"cover_url":8,"hotness":355,"is_selected":13,"score":356,"score_detail":357,"sources":360,"tags":364,"primary_topic":366,"secondary_topics":367,"classification":368,"search_phrases":396,"slug":399,"view_count":53,"doi":400,"paper":401,"created_at":411},2866,"Upcoming Technologies for Agriculture: Innovations Shaping the Future of Farming","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22816586","Agriculture has always been a cornerstone of human civilization, providing food, raw materials, and employment. However, with the global population projected to reach nearly 10 billion by 2050, the demand for food production is expected to increase substantially (Godfray et al., 2010). Traditional farming methods are increasingly challenged by climate change, resource limitations, and environmental concerns. To address these challenges, upcoming technologies in agriculture promise to revolutionize farming practices by enhancing productivity, sustainability, and resilience. This article reviews key emerging technologies, including precision agriculture, artificial intelligence (AI), gene editing, drone and robotic systems, Internet of Things (IoT) sensors, and sustainable farming innovations. The integration of these technologies is expected to transform agriculture into a more efficient, data-driven, and environmentally friendly sector.","农业一直是人类文明的基石，为人类提供食物、原材料和就业机会。然而，随着全球人口预计到2050年将接近100亿，粮食生产需求预计将大幅增加（Godfray等，2010）。传统耕作方式日益受到气候变化、资源限制和环境问题的挑战。为应对这些挑战，农业领域的新兴技术有望通过提高生产力、可持续性和韧性来彻底变革耕作方式。本文综述了关键新兴技术，包括精准农业、人工智能（AI）、基因编辑、无人机与机器人系统、物联网（IoT）传感器以及可持续农业创新。这些技术的融合有望将农业转变为一个更高效、数据驱动且环境友好的产业。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-17T00:00:00Z",25,68,{"impact":153,"substance":124,"depth":358,"authority":18,"freshness":39,"relevant":20,"comment":359},15,"综述性论文系统梳理精准农业、AI、基因编辑等前沿技术，时效性尚可，但缺乏新数据与独家结论，适合作为主题聚合素材而非每日精选头条。",[361,362],{"name":353,"url":350},{"name":353,"url":363},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22816587",[240,25,365,27,28],"农业物联网",{"code":144,"slug":145,"name":146,"path":147,"parent_code":136},[],{"status":149,"locked":13,"version":33,"method":34,"evidence":369},{"candidates":370,"agricultural_evidence":40},[371,375,379,384,388,392],{"code":144,"score":12,"supported":40,"title":372,"abstract":373,"tags":374},[],[167,168,169],[365],{"code":58,"score":39,"supported":40,"title":376,"abstract":377,"tags":378},[],[27,180],[240,27],{"code":38,"score":19,"supported":40,"title":380,"abstract":381,"tags":383},[],[28,382],"gene editing",[28],{"code":46,"score":112,"supported":40,"title":385,"abstract":386,"tags":387},[],[174,175],[],{"code":63,"score":53,"supported":40,"title":389,"abstract":390,"tags":391},[],[155],[25],{"code":287,"score":70,"supported":40,"title":393,"abstract":394,"tags":395},[],[290],[],[397,398],"精准农业 人工智能 无人机","农业物联网 传感器 机器人","精准农业人工智能无人机-2866","10.5281\u002Fzenodo.22816586",{"doi":400,"openalex_id":402,"authors":403,"venue":353,"cited_by_count":102,"oa_url":350,"card":406,"direction":223,"ingested_from":224},"W7213515489",[404],{"name":405,"orcid":8},"Zorawar Singh",{"tldr":407,"method":408,"finding":409,"direction":223,"opportunity":410},"综述精准农业、AI、基因编辑、无人机、物联网等新兴技术如何重塑未来农业。","文献综述，整合精准农业、AI、基因编辑、无人机、IoT等关键技术。","技术融合将推动农业向高效、数据驱动和环境友好方向转型。","可聚焦多技术集成落地中的成本、数据标准与农户采纳障碍等实证研究空白。","2026-09-18T23:30:14.975692Z",{"id":413,"title":414,"url":415,"summary":416,"summary_zh":417,"content":8,"source_name":353,"source_url":415,"published_at":418,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":419,"score_detail":420,"sources":423,"tags":425,"primary_topic":427,"secondary_topics":428,"classification":429,"search_phrases":453,"slug":456,"view_count":102,"doi":457,"paper":458,"created_at":469},5588,"Certified Temporal Fusion of Artificial Intelligence Climate Analytics for Crop Failure Early Warning Under Sparse Agricultural Sensors: Error Bounds and Famine Relief Targeting","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545489","Sparse agricultural sensor networks across sub-Saharan Africa leave crop-failure early-warning systems with too little ground observation to support reliable famine relief targeting. The framework treats sensor sparsity as a missing-data problem with a known observation operator and propagates climate-analytics uncertainty through a temporal Kalman-Buys filter whose gain is constrained by a certified risk envelope. Using NASA POWER monthly reanalysis for Accra, Ghana (2000-2023, 312 observations), the analysis establishes a mean temperature of 26.521 C and a warming trend of 0.1622 C per decade, which calibrates the climate-analytics prior. The derived bounds show that relief-targeting error scales with the square root of sensor density and with the Lipschitz constant of the crop-response function, yielding an explicit sensor-placement rule for a target misclassification rate. The contribution is a certified, assumption-explicit bridge between climate analytics and famine relief targeting that states the conditions under which early warning is trustworthy.","撒哈拉以南非洲稀疏的农业传感器网络使得作物歉收预警系统缺乏足够的地面观测数据，难以支撑可靠的饥荒救援目标定位。该框架将传感器稀疏性视为具有已知观测算子的缺失数据问题，并通过时间Kalman-Buys滤波器传播气候分析不确定性，该滤波器的增益受经认证的风险包络约束。利用NASA POWER对加纳阿克拉的月度再分析数据（2000—2023年，312个观测值），分析确定了平均气温为26.521°C、每十年0.1622°C的增暖趋势，据此校准了气候分析先验。推导出的界限表明，救援目标定位误差随传感器密度的平方根以及作物响应函数的Lipschitz常数缩放，从而为目标误分类率给出了明确的传感器布设规则。该研究的贡献在于构建了一个经认证的、假设明确的气候分析与饥荒救援目标定位之间的桥梁，阐明了早期预警可信赖的条件。","2026-10-21T00:00:00Z",74,{"impact":421,"substance":16,"depth":153,"authority":421,"freshness":12,"relevant":20,"comment":422},12,"该研究提出一种带误差界认证的时序融合方法，用于稀疏传感器条件下的作物歉收预警与饥荒救援瞄准，方法新颖、结论明确，但案例局限于加纳阿克拉，产业影响有限，适合作为技术前沿条目收录。",[424],{"name":353,"url":415},[240,25,26,91,426],"气候分析",{"code":63,"slug":133,"name":134,"path":135,"parent_code":136},[],{"status":149,"locked":13,"version":33,"method":34,"evidence":430},{"candidates":431,"agricultural_evidence":40},[432,436,441,445,449],{"code":63,"score":125,"supported":40,"title":433,"abstract":434,"tags":435},[155],[],[25],{"code":144,"score":70,"supported":40,"title":437,"abstract":438,"tags":440},[],[439],"农业传感",[],{"code":52,"score":20,"supported":13,"title":442,"abstract":443,"tags":444},[],[],[26],{"code":58,"score":20,"supported":13,"title":446,"abstract":447,"tags":448},[],[],[240],{"code":88,"score":20,"supported":13,"title":450,"abstract":451,"tags":452},[],[],[91],[454,455],"NASA POWER 加纳 气候分析","作物歉收 早期预警 传感器","NASAPOWER加纳气候分析-5588","10.5281\u002Fzenodo.23545489",{"doi":457,"openalex_id":459,"authors":460,"venue":353,"cited_by_count":102,"oa_url":415,"card":463,"direction":107,"ingested_from":224},"W7223111766",[461],{"name":462,"orcid":8},"Fatima-Zahra El Khattabi",{"tldr":464,"method":465,"finding":466,"direction":467,"opportunity":468},"提出带误差界的时间融合框架，在稀疏传感器下用气候分析预警作物歉收。","卡尔曼-布西滤波+NASA POWER再分析数据，风险包络约束增益。","救济瞄准误差随传感器密度平方根和作物响应Lipschitz常数缩放，给出布点规则。","农业遥感与作物表型","可将该认证误差界框架扩展到多作物多区域，结合卫星遥感弥补地面传感器稀疏。","2026-10-22T11:21:02.682497Z",{"id":471,"title":472,"url":473,"summary":474,"summary_zh":475,"content":8,"source_name":476,"source_url":473,"published_at":418,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":477,"score_detail":478,"sources":480,"tags":482,"primary_topic":483,"secondary_topics":484,"classification":491,"search_phrases":538,"slug":541,"view_count":102,"doi":542,"paper":543,"created_at":561},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":153,"substance":16,"depth":153,"authority":18,"freshness":125,"relevant":20,"comment":479},"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。",[481],{"name":476,"url":473},[240,25,167,27,130],{"code":63,"slug":133,"name":134,"path":135,"parent_code":136},[485,486],{"code":144,"slug":145,"name":146,"path":147,"parent_code":136},{"code":487,"slug":488,"name":489,"path":490,"parent_code":248},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":149,"locked":13,"version":33,"method":34,"evidence":492},{"candidates":493,"agricultural_evidence":40},[494,502,507,513,519,523,527,533],{"code":63,"score":153,"supported":40,"title":495,"abstract":496,"tags":501},[155],[497,66,498,157,499,500],"深度学习","计算机视觉","deep learning","computer vision",[25],{"code":58,"score":503,"supported":40,"title":504,"abstract":505,"tags":506},16,[180],[27,180],[240,27],{"code":144,"score":12,"supported":40,"title":508,"abstract":509,"tags":512},[],[167,510,169,511],"边缘计算","edge computing",[167],{"code":487,"score":125,"supported":40,"title":514,"abstract":515,"tags":518},[],[516,517,488],"灌溉","水资源",[],{"code":46,"score":19,"supported":40,"title":520,"abstract":521,"tags":522},[],[174,175],[130],{"code":244,"score":112,"supported":40,"title":524,"abstract":525,"tags":526},[],[261,245],[],{"code":528,"score":70,"supported":40,"title":529,"abstract":530,"tags":532},"A7",[],[531],"间作",[],{"code":298,"score":70,"supported":40,"title":534,"abstract":535,"tags":537},[],[536],"养分管理",[],[539,540],"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":542,"openalex_id":544,"authors":545,"venue":476,"cited_by_count":102,"oa_url":473,"card":556,"direction":223,"ingested_from":224},"W7223121456",[546,549,551,553],{"name":547,"orcid":548},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":550,"orcid":8},"Oluwadare OYEBODE",{"name":552,"orcid":8},"Iretiayo Adelaiye",{"name":554,"orcid":555},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":557,"method":558,"finding":559,"direction":107,"opportunity":560},"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":563,"title":564,"url":565,"summary":566,"summary_zh":8,"content":8,"source_name":567,"source_url":565,"published_at":568,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":569,"score_detail":570,"sources":572,"tags":574,"primary_topic":577,"secondary_topics":581,"classification":582,"search_phrases":594,"slug":597,"view_count":102,"doi":598,"paper":599,"created_at":614},5539,"Robust and transparent crop-row detection from RGB field imagery for vision-based agricultural guidance systems","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10473-z","Robust and transparent crop-row detection from RGB field imagery for vision-based agricultural guidance systems。Precision Agriculture","Precision Agriculture","2026-10-22T00:00:00Z",79,{"impact":503,"substance":235,"depth":153,"authority":124,"freshness":12,"relevant":20,"comment":571},"核心期刊新论文，提出鲁棒且可解释的RGB作物行检测方法，对视觉导航农机具参考价值明确，但属细分技术进展，未达重大突破层级。",[573],{"name":567,"url":565},[240,25,27,575,576],"机器视觉","作物行检测",{"code":58,"slug":578,"name":579,"path":580,"parent_code":136},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":149,"locked":13,"version":33,"method":34,"evidence":583},{"candidates":584,"agricultural_evidence":40},[585,590],{"code":58,"score":586,"supported":40,"title":587,"abstract":588,"tags":589},5,[],[180],[240,27],{"code":63,"score":98,"supported":13,"title":591,"abstract":592,"tags":593},[],[],[25,575],[595,596],"RGB 作物行检测","农业人工智能 作物行检测 智慧农业 机器视觉","RGB作物行检测-5539","10.1007\u002Fs11119-026-10473-z",{"doi":598,"openalex_id":600,"authors":601,"venue":567,"cited_by_count":102,"oa_url":8,"card":8,"direction":8,"ingested_from":224},"W7223117828",[602,605,608,611],{"name":603,"orcid":604},"Esther Gascó","https:\u002F\u002Forcid.org\u002F0000-0003-1798-4483",{"name":606,"orcid":607},"Clara I. López-González","https:\u002F\u002Forcid.org\u002F0000-0002-2755-1692",{"name":609,"orcid":610},"Eva Besada-Portas","https:\u002F\u002Forcid.org\u002F0000-0001-6129-4653",{"name":612,"orcid":613},"Gonzalo Pájares","https:\u002F\u002Forcid.org\u002F0000-0003-0915-6282","2026-10-22T11:20:09.678587Z"]