[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2866":3,"related-2866":53},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":28,"search_phrases":34,"slug":37,"view_count":38,"doi":39,"paper":40,"created_at":52},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。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-09-17T00:00:00Z","论文",25,false,68,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,14,15,13,8,1,"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。",[25,26],{"name":10,"url":6},{"name":10,"url":27},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22816587",[29,30,31,32,33],"智慧农业","农业人工智能","农业物联网","精准农业","基因编辑",[35,36],"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",0,"10.5281\u002Fzenodo.22816586",{"doi":39,"openalex_id":41,"authors":42,"venue":10,"cited_by_count":38,"oa_url":6,"card":45,"direction":49,"ingested_from":51},"W7213515489",[43],{"name":44,"orcid":9},"Zorawar Singh",{"tldr":46,"method":47,"finding":48,"direction":49,"opportunity":50},"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。","智慧农业 \u002F 农业物联网","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。","openalex","2026-09-18T23:30:14.975692Z",{"total":54,"page":22,"page_size":54,"items":55},6,[56,101,151,187,219,258],{"id":57,"title":58,"url":59,"summary":60,"summary_zh":61,"content":9,"source_name":62,"source_url":59,"published_at":11,"category":12,"cover_url":9,"hotness":63,"is_selected":14,"score":64,"score_detail":65,"sources":70,"tags":72,"search_phrases":75,"slug":78,"view_count":38,"doi":79,"paper":80,"created_at":100},2802,"Advanced olive leaf area prediction using machine learning methods","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-71390-9","Abstract Accurate leaf area estimation is essential for understanding olive tree physiology, productivity, and stress adaptationThis study developed and evaluated machine learning models for non-destructive olive leaf area prediction using linear measurements (length and width) from 30 diverse cultivars at the Tarom Olive Research Station, Iran. Six machine learning algorithms, Artificial Neural Network (ANN), Support Vector Regression (SVR), Random Forest, Decision Tree, AdaBoost, and XGBoost were optimized using Bayesian optimization, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). Results demonstrated that PSO consistently outperformed other optimization methods across most models. The ANN model optimized with PSO achieved the highest prediction accuracy (R 2 = 0.9828, RMSE = 0.3009 cm 2 ). External validation using eight additional cultivars confirmed model generalizability, with the universal ANN model maintaining R 2 > 0.98. This study provides a robust, non-destructive methodology for olive leaf area estimation applicable across diverse cultivars, offering practical implications for precision agriculture, phenotyping, and orchard management under changing climatic conditions.","摘要 准确的叶面积估算对于理解油橄榄树的生理特性、生产力及逆境适应性至关重要。本研究在伊朗塔罗姆油橄榄研究站，利用来自30个不同品种的线性测量数据（长度和宽度），开发并评估了用于无损油橄榄叶面积预测的机器学习模型。采用贝叶斯优化、遗传算法（GA）和粒子群优化（PSO）对六种机器学习算法——人工神经网络（ANN）、支持向量回归（SVR）、随机森林、决策树、AdaBoost和XGBoost——进行了优化。结果表明，在大多数模型中，PSO始终优于其他优化方法。经PSO优化后的ANN模型取得了最高的预测精度（R² = 0.9828，RMSE = 0.3009 cm²）。利用另外八个品种进行的外部验证证实了模型的泛化能力，通用ANN模型保持R² > 0.98。本研究为适用于不同品种的油橄榄叶面积估算提供了一种稳健的无损方法，为气候变化条件下的精准农业、表型分析和果园管理提供了实际应用价值。","Scientific Reports",10,73,{"impact":66,"substance":67,"depth":68,"authority":18,"freshness":63,"relevant":22,"comment":69},12,20,17,"基于30个品种的机器学习叶片面积无损预测研究，方法新颖、验证充分，对精准农业与表型分析有实用价值，但属细分领域技术进展，影响范围有限。",[71],{"name":62,"url":59},[29,30,73,32,74],"机器学习","表型分析",[76,77],"农业人工智能 智慧农业 机器学习 精准农业","农业人工智能 智慧农业","农业人工智能智慧农业机器学习精准农业-2802","10.1038\u002Fs41598-026-71390-9",{"doi":79,"openalex_id":81,"authors":82,"venue":62,"cited_by_count":38,"oa_url":59,"card":93,"direction":99,"ingested_from":51},"W7213449017",[83,86,88,90],{"name":84,"orcid":85},"Ahmad Reza Dadras","https:\u002F\u002Forcid.org\u002F0000-0001-8591-5813",{"name":87,"orcid":9},"Hossein Sabouri",{"name":89,"orcid":9},"Ali Tanhaei",{"name":91,"orcid":92},"Sayed Javad Sajadi","https:\u002F\u002Forcid.org\u002F0000-0002-6555-080X",{"tldr":94,"method":95,"finding":96,"direction":97,"opportunity":98},"用机器学习基于叶长宽非破坏性预测30个橄榄品种叶面积，PSO优化ANN精度最高。","30个品种叶长宽数据，六种ML算法结合贝叶斯、GA、PSO优化。","PSO优化ANN最优（R²=0.9828），外部8品种验证R²>0.98，通用性好。","农业遥感与作物表型","可拓展至多物种、多环境及无人机\u002F手机图像自动测量，构建通用叶面积表型平台。","农业人工智能与决策模型","2026-09-17T23:30:59.169744Z",{"id":102,"title":103,"url":104,"summary":105,"summary_zh":106,"content":9,"source_name":107,"source_url":104,"published_at":11,"category":12,"cover_url":9,"hotness":63,"is_selected":14,"score":108,"score_detail":109,"sources":112,"tags":114,"search_phrases":117,"slug":119,"view_count":38,"doi":120,"paper":121,"created_at":150},2801,"A comprehensive survey on machine vision applications in precision agriculture: current trends and future perspectives","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs41060-026-01278-4","A comprehensive survey on machine vision applications in precision agriculture: current trends and future perspectives。International Journal of Data Science and Analytics","精准农业中机器视觉应用的综合综述：当前趋势与未来展望。《国际数据科学与分析杂志》","International Journal of Data Science and Analytics",77,{"impact":17,"substance":67,"depth":68,"authority":20,"freshness":110,"relevant":22,"comment":111},9,"核心期刊发表的机器视觉精准农业综述，方法梳理与趋势判断具参考价值，但属综述类论文，产业影响有限。",[113],{"name":107,"url":104},[29,30,32,115,116],"遥感监测","机器视觉",[118,77],"农业人工智能 智慧农业 机器视觉 精准农业","农业人工智能智慧农业机器视觉精准农业-2801","10.1007\u002Fs41060-026-01278-4",{"doi":120,"openalex_id":122,"authors":123,"venue":107,"cited_by_count":38,"oa_url":9,"card":145,"direction":99,"ingested_from":51},"W7213471057",[124,126,128,130,133,135,137,140,143],{"name":125,"orcid":9},"Shirun Gu",{"name":127,"orcid":9},"Xinyuan Fan",{"name":129,"orcid":9},"Lihui Zhu",{"name":131,"orcid":132},"Caixia Song","https:\u002F\u002Forcid.org\u002F0000-0003-3897-7629",{"name":134,"orcid":9},"Lei Mu",{"name":136,"orcid":9},"Zichen Zhang",{"name":138,"orcid":139},"Rui Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-8634-3519",{"name":141,"orcid":142},"Tong Xu","https:\u002F\u002Forcid.org\u002F0000-0001-5564-192X",{"name":144,"orcid":9},"Zhiyuan Zhang",{"tldr":146,"method":147,"finding":148,"direction":99,"opportunity":149},"综述机器视觉在精准农业中的应用现状与未来趋势。","文献综述，梳理机器视觉在精准农业中的技术路线。","机器视觉已广泛用于作物监测、病虫害识别等，但落地仍受数据与算力限制。","可聚焦轻量化模型与边缘部署，解决田间实时性与数据稀缺问题。","2026-09-17T23:30:54.103781Z",{"id":152,"title":153,"url":154,"summary":155,"summary_zh":156,"content":9,"source_name":10,"source_url":154,"published_at":157,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":108,"score_detail":158,"sources":160,"tags":164,"search_phrases":167,"slug":170,"view_count":38,"doi":171,"paper":172,"created_at":186},2773,"IoT-Enabled Sensor Applications in Smart Healthcare, Smart Agriculture, Environmental Monitoring and Smart Energy & Safety Monitoring","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22795428","The Internet of Things (IoT) is becoming a fundamental technology for the development of sensorized intelligent systems in several domains of society. This paper presents a structured literature review on architectures for sensing and intelligence in the scope of four research areas: Smart Healthcare, Smart Agriculture, Environmental Monitoring, and Smart Energy and Safety Monitoring. Recent journal articles, systematic reviews, and conference papers were analysed to report sensor modalities, communication architectures, and learning algorithms, namely machine learning (ML) and deep learning (DL), that have been proposed in literature for each of the above research areas. A consolidated inventory of algorithms that have been reported in the above surveyed works, as well as a comparison of sensing technologies, learning techniques, and their corresponding applications, are also presented. Finally, open challenges that have been found to be recurring, i.e., sensor calibration and drift, energy efficiency, security and privacy, heterogeneous data, and the gap between proof-of-concept prototypes and large-scale scalable and practical implementations, as well as future research directions, are outlined. The purpose of this work is to provide a useful, structured literature review that can serve as a background and reference for researchers interested in sensorized IoT applications using algorithmic approaches.","物联网（IoT）正在成为社会发展多个领域中传感智能系统开发的基础性技术。本文针对四个研究领域——智能医疗、智能农业、环境监测以及智能能源与安全监测——中的传感与智能架构进行了结构化文献综述。通过分析近期期刊论文、系统性综述和会议论文，报告了上述各研究领域中文献所提出的传感器模态、通信架构以及学习算法，即机器学习（ML）和深度学习（DL）。本文还提供了在上述综述工作中所报道算法的综合清单，以及传感技术、学习技术及其相应应用的对比。最后，概述了反复出现的开放挑战，即传感器校准与漂移、能效、安全与隐私、异构数据，以及概念验证原型与大规模可扩展实际实现之间的差距，并指出了未来研究方向。本工作的目的是提供一份有用的、结构化的文献综述，为对使用算法方法的传感物联网应用感兴趣的研究人员提供背景和参考。","2026-09-16T00:00:00Z",{"impact":17,"substance":67,"depth":68,"authority":20,"freshness":110,"relevant":22,"comment":159},"系统性文献综述，覆盖智慧农业等四大领域的传感与智能架构，方法梳理与开放挑战总结扎实，对农业物联网研究有参考价值，但非农业专属突破性成果。",[161,162],{"name":10,"url":154},{"name":10,"url":163},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22795429",[29,30,31,165,166],"传感器","环境监测",[168,169],"农业人工智能 农业物联网 智慧农业 环境监测","农业人工智能 农业物联网","农业人工智能农业物联网智慧农业环境监测-2773","10.5281\u002Fzenodo.22795428",{"doi":171,"openalex_id":173,"authors":174,"venue":10,"cited_by_count":38,"oa_url":154,"card":181,"direction":49,"ingested_from":51},"W7213433550",[175,177,179],{"name":176,"orcid":9},"Mrs.C.Nithya",{"name":178,"orcid":9},"Mr.M.K.Sampath",{"name":180,"orcid":9},"Mrs. P.Raga Keerthana",{"tldr":182,"method":183,"finding":184,"direction":49,"opportunity":185},"综述物联网传感与智能在医疗、农业、环境、能源安全四大领域的架构、算法与应用。","结构化文献综述，分析传感器模态、通信架构及机器学习\u002F深度学习算法。","梳理了各领域算法清单，指出传感器校准漂移、能效、安全隐私等共性挑战。","农业物联网中传感器校准漂移与跨域异构数据融合的轻量化算法研究尚存空白。","2026-09-17T23:30:13.984507Z",{"id":188,"title":189,"url":190,"summary":191,"summary_zh":192,"content":9,"source_name":193,"source_url":190,"published_at":194,"category":12,"cover_url":9,"hotness":63,"is_selected":14,"score":195,"score_detail":196,"sources":199,"tags":201,"search_phrases":203,"slug":206,"view_count":38,"doi":207,"paper":208,"created_at":218},2652,"ARTIFICIAL INTELLIGENCE IN HIGHER EDUCATION: TRANSFORMING TEACHING, LEARNING, AND STUDENT ENGAGEMENT","https:\u002F\u002Fdoi.org\u002F10.65725\u002Fijhlt\u002F1\u002F2\u002F001","Efficient irrigation management is essential for sustainable agriculture, particularly in the context of increasing freshwater scarcity and the growing imperative to optimize crop productivity. Conventional irrigation practices rely predominantly on fixed time schedules or manual field assessments, which frequently induce over-irrigation, root-zone nutrient leaching, under-irrigation water stress, and substantial resource inefficiency. This paper proposes an Intelligent Irrigation Management System that integrates Internet of Things (IoT) sensing architectures, multi-parameter environmental telemetry, and supervised machine learning (ML) algorithms to facilitate dynamic, data-driven, and automated irrigation control. The proposed system continuously acquires real-time field data—including soil moisture, ambient temperature, relative humidity, soil temperature, and rainfall—via deployed sensor nodes managed by an ESP32 microcontroller pipeline. The telemetry stream is transmitted through low-power communication channels to a centralized processing engine, where a Random Forest classification model evaluates multidimensional soil-environmental interactions to predict immediate irrigation requirements. The predicted states feed into an automated actuation layer that directly modulates a solenoid-valve and water-pump relay, forming a closed-loop feedback pipeline. Evaluated against traditional threshold-based and schedule-driven approaches, the proposed IoT-ML framework demonstrates superior operational responsiveness, minimizes unnecessary water application, and offers a robust, scalable architectural template for modern precision agriculture.","高效灌溉管理对可持续农业至关重要，尤其是在淡水日益稀缺、优化作物生产力需求不断增长的背景下。传统灌溉实践主要依赖固定时间表或人工田间评估，这常常导致过度灌溉、根区养分淋失、灌溉不足引起的水分胁迫以及严重的资源低效。本文提出了一种智能灌溉管理系统，该系统集成了物联网（IoT）感知架构、多参数环境遥测以及监督式机器学习（ML）算法，以实现动态、数据驱动和自动化的灌溉控制。所提出的系统通过由ESP32微控制器管道管理的部署传感器节点，持续采集实时田间数据——包括土壤湿度、环境温度、相对湿度、土壤温度和降雨量。遥测数据流通过低功耗通信信道传输至集中处理引擎，其中随机森林分类模型评估多维土壤-环境相互作用，以预测即时灌溉需求。预测状态输入自动执行层，直接调节电磁阀和水泵继电器，形成闭环反馈管道。与传统基于阈值和时间表驱动的方法相比，所提出的IoT-ML框架展现出更优的运行响应能力，最大限度地减少了不必要的灌溉用水，并为现代精准农业提供了一种稳健、可扩展的架构模板。","INTERNATIONAL JOURNAL OF HUMANITIES AND LEARNING TECHNOLOGY INNOVATION (IJHLT)","2026-09-15T00:00:00Z",72,{"impact":197,"substance":17,"depth":68,"authority":20,"freshness":21,"relevant":22,"comment":198},16,"论文提出IoT与随机森林融合的闭环智能灌溉系统，方法完整、数据驱动，对节水农业有参考价值，但标题与摘要主题不符需核实。",[200],{"name":193,"url":190},[29,31,73,202,32],"智能灌溉",[204,205],"农业物联网 智慧农业 智能灌溉 机器学习","农业物联网 智慧农业","农业物联网智慧农业智能灌溉机器学习-2652","10.65725\u002Fijhlt\u002F1\u002F2\u002F001",{"doi":207,"openalex_id":209,"authors":210,"venue":193,"cited_by_count":38,"oa_url":9,"card":213,"direction":49,"ingested_from":51},"W7213277724",[211],{"name":212,"orcid":9},"M. Rathamani",{"tldr":214,"method":215,"finding":216,"direction":49,"opportunity":217},"提出融合物联网传感与随机森林的智能灌溉系统，实现数据驱动的自动灌溉控制。","ESP32传感器节点采集土壤温湿度等数据，随机森林分类预测灌溉需求。","相比传统定时或阈值方法，该系统响应更优、减少不必要灌溉，可扩展性强。","可探索多模态数据融合与边缘智能，提升灌溉决策的实时性与泛化能力。","2026-09-16T23:30:16.194086Z",{"id":220,"title":221,"url":222,"summary":223,"summary_zh":224,"content":9,"source_name":225,"source_url":222,"published_at":194,"category":12,"cover_url":9,"hotness":63,"is_selected":14,"score":195,"score_detail":226,"sources":228,"tags":230,"search_phrases":233,"slug":236,"view_count":38,"doi":237,"paper":238,"created_at":257},2642,"Artificial Intelligence for Environmental Resilience and Sustainable Innovation","https:\u002F\u002Fdoi.org\u002F10.68012\u002Fair.v1i2.239","Environmental challenges such as climate change, resource depletion, biodiversity loss, and increasing pollution require innovative and adaptive solutions to strengthen environmental resilience. Artificial intelligence (AI) has emerged as a transformative technology capable of improving environmental monitoring, prediction, and decision-making processes. This study examines the role of artificial intelligence in enhancing environmental resilience and promoting sustainable innovation across various sectors, including natural resource management, climate adaptation, waste management, environmental conservation, and smart environmental systems. This study adopts a Systematic Literature Review (SLR) methodology integrated with bibliometric analysis and thematic content analysis to identify research trends, conceptual developments, and emerging themes related to AI applications in environmental sustainability. The selected studies were analyzed using VOSviewer, Tableau, and Microsoft Excel to visualize knowledge structures, thematic relationships, and research patterns. The findings reveal that AI significantly contributes to environmental resilience by enabling advanced data analysis, early detection of environmental risks, predictive assessment, resource optimization, and evidence-based decision-making. Furthermore, AI-driven innovations support sustainable practices through renewable energy optimization, precision agriculture, waste management improvement, water resource monitoring, and smart city development. However, challenges remain regarding data quality, technological accessibility, computational requirements, ethical considerations, and responsible governance. The study concludes that AI has substantial potential to accelerate environmental resilience and sustainable innovation when supported by appropriate policies, stakeholder collaboration, and long-term sustainability strategies. By integrating intelligent technologies with responsible environmental management approaches, AI can serve as a strategic enabler in addressing complex ecological challenges and supporting a more adaptive, resilient, and sustainable future.","气候变化、资源枯竭、生物多样性丧失和污染加剧等环境挑战，需要创新性和适应性的解决方案来增强环境韧性。人工智能（AI）已成为一种变革性技术，能够改善环境监测、预测和决策过程。本研究探讨了人工智能在增强环境韧性和促进各领域可持续创新中的作用，涵盖自然资源管理、气候适应、废物管理、环境保护和智能环境系统等。本研究采用系统性文献综述（SLR）方法，并结合文献计量分析和主题内容分析，以识别与人工智能在环境可持续性中应用相关的研究趋势、概念发展和新兴主题。所选研究使用VOSviewer、Tableau和Microsoft Excel进行分析，以可视化知识结构、主题关系和研究模式。研究结果表明，人工智能通过实现先进数据分析、环境风险早期检测、预测性评估、资源优化和循证决策，显著促进了环境韧性。此外，人工智能驱动的创新通过可再生能源优化、精准农业、废物管理改进、水资源监测和智慧城市发展，支持可持续实践。然而，在数据质量、技术可及性、计算需求、伦理考量和负责任治理方面仍存在挑战。研究结论认为，在适当的政策、利益相关者协作和长期可持续战略支持下，人工智能具有加速环境韧性和可持续创新的巨大潜力。通过将智能技术与负责任的环境管理方法相结合，人工智能可以作为应对复杂生态挑战、支持更具适应性、韧性和可持续未来的战略赋能者。","AI Innovation and Resilience for the Environment (AIR)",{"impact":17,"substance":17,"depth":197,"authority":66,"freshness":21,"relevant":22,"comment":227},"系统综述类论文，梳理AI在环境韧性与可持续创新中的应用，涵盖精准农业、水资源监测等方向，方法规范但结论偏综述性，对农业信息化有参考价值。",[229],{"name":225,"url":222},[29,30,231,32,232],"可持续发展","环境韧性",[234,235],"农业人工智能 可持续发展 智慧农业 环境韧性","农业人工智能 可持续发展","农业人工智能可持续发展智慧农业环境韧性-2642","10.68012\u002Fair.v1i2.239",{"doi":237,"openalex_id":239,"authors":240,"venue":225,"cited_by_count":38,"oa_url":222,"card":251,"direction":49,"ingested_from":51},"W7213274173",[241,243,246,249],{"name":242,"orcid":9},"Sri Poedji Lestari",{"name":244,"orcid":245},"Rosa Lesmana","https:\u002F\u002Forcid.org\u002F0009-0002-5308-6023",{"name":247,"orcid":248},"Sabil Maulana Fauzi","https:\u002F\u002Forcid.org\u002F0009-0003-1833-1027",{"name":250,"orcid":9},"Alexander Johnson Johnson",{"tldr":252,"method":253,"finding":254,"direction":255,"opportunity":256},"系统综述AI在环境韧性与可持续创新中的应用、趋势与挑战。","系统文献综述结合文献计量与主题分析，用VOSviewer等可视化。","AI通过数据分析、风险预警和资源优化显著增强环境韧性，但存在数据与治理挑战。","农业绿色发展与碳","可聚焦AI在精准农业与碳减排协同中的实证研究，弥补数据质量与治理机制空白。","2026-09-16T23:30:11.835233Z",{"id":259,"title":260,"url":261,"summary":262,"summary_zh":263,"content":9,"source_name":264,"source_url":261,"published_at":157,"category":12,"cover_url":9,"hotness":63,"is_selected":14,"score":265,"score_detail":266,"sources":270,"tags":272,"search_phrases":276,"slug":279,"view_count":38,"doi":280,"paper":281,"created_at":293},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",86,{"impact":267,"substance":267,"depth":268,"authority":18,"freshness":110,"relevant":22,"comment":269},22,19,"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。",[271],{"name":264,"url":261},[30,32,33,273,274,275],"气候韧性","智慧育种","多组学",[277,278],"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","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":280,"openalex_id":282,"authors":283,"venue":264,"cited_by_count":38,"oa_url":261,"card":288,"direction":49,"ingested_from":51},"W7213241547",[284,286],{"name":285,"orcid":9},"Surasreeta Paul",{"name":287,"orcid":9},"Sandeep Singh Rana",{"tldr":289,"method":290,"finding":291,"direction":99,"opportunity":292},"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。","2026-09-16T23:30:07.496425Z"]