[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2625":3,"related-2625":151},{"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":23,"tags":25,"primary_topic":32,"secondary_topics":38,"classification":50,"search_phrases":128,"slug":131,"view_count":132,"doi":133,"paper":134,"created_at":150},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。",null,"Frontiers in Sustainable Food Systems","2026-09-16T00:00:00Z","论文",10,false,86,{"impact":17,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},22,19,14,9,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30,31],"农业人工智能","精准农业","基因编辑","气候韧性","智慧育种","多组学",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[39,45],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"F2","biotechnology","生物育种与基因技术","\u002Ftopic\u002Finputs-equipment\u002Fbiotechnology","F",{"code":46,"slug":47,"name":48,"path":49,"parent_code":37},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":51,"locked":14,"version":52,"method":53,"evidence":54},"classified","2026-10-22.2","rules",{"candidates":55,"agricultural_evidence":58},[56,65,71,78,86,92,100,107,114,121],{"code":33,"score":57,"supported":58,"title":59,"abstract":61,"tags":64},18,true,[60],"artificial intelligence",[62,63,60],"机器学习","machine learning",[26],{"code":40,"score":20,"supported":58,"title":66,"abstract":67,"tags":70},[],[68,69],"基因组选择","genomic selection",[28,31,30],{"code":46,"score":20,"supported":58,"title":72,"abstract":73,"tags":77},[],[74,75,76],"物联网","internet of things","IoT",[],{"code":79,"score":80,"supported":58,"title":81,"abstract":82,"tags":85},"H7",7,[],[83,84],"气候变化","climate change",[29],{"code":87,"score":80,"supported":58,"title":88,"abstract":89,"tags":91},"I1",[],[27,90],"precision agriculture",[27],{"code":93,"score":94,"supported":58,"title":95,"abstract":96,"tags":99},"A2",6,[],[97,98],"小麦","wheat",[],{"code":101,"score":94,"supported":58,"title":102,"abstract":103,"tags":106},"A3",[],[104,105],"玉米","maize",[],{"code":108,"score":94,"supported":58,"title":109,"abstract":110,"tags":113},"A4",[],[111,112],"大豆","soybean",[],{"code":115,"score":94,"supported":58,"title":116,"abstract":117,"tags":120},"A8",[],[118,119],"粮食安全","food security",[],{"code":122,"score":94,"supported":58,"title":123,"abstract":124,"tags":127},"I3",[],[125,126],"遥感","remote sensing",[],[129,130],"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",2,"10.3389\u002Ffsufs.2026.1931230",{"doi":133,"openalex_id":135,"authors":136,"venue":10,"cited_by_count":141,"oa_url":6,"card":142,"direction":148,"ingested_from":149},"W7213241547",[137,139],{"name":138,"orcid":9},"Surasreeta Paul",{"name":140,"orcid":9},"Sandeep Singh Rana",0,{"tldr":143,"method":144,"finding":145,"direction":146,"opportunity":147},"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。","农业人工智能与决策模型","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",{"total":94,"page":21,"page_size":94,"items":152},[153,254,313,393,472,572],{"id":154,"title":155,"url":156,"summary":157,"summary_zh":158,"content":9,"source_name":159,"source_url":156,"published_at":160,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":161,"score_detail":162,"sources":165,"tags":167,"primary_topic":169,"secondary_topics":170,"classification":178,"search_phrases":230,"slug":233,"view_count":141,"doi":234,"paper":235,"created_at":253},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)","2026-10-21T00:00:00Z",80,{"impact":57,"substance":17,"depth":57,"authority":163,"freshness":20,"relevant":21,"comment":164},13,"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。",[166],{"name":159,"url":156},[168,26,74,27,29],"智慧农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[171,172],{"code":46,"slug":47,"name":48,"path":49,"parent_code":37},{"code":173,"slug":174,"name":175,"path":176,"parent_code":177},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":51,"locked":14,"version":52,"method":53,"evidence":179},{"candidates":180,"agricultural_evidence":58},[181,189,194,200,206,210,217,224],{"code":33,"score":57,"supported":58,"title":182,"abstract":183,"tags":188},[60],[184,62,185,63,186,187],"深度学习","计算机视觉","deep learning","computer vision",[26],{"code":87,"score":190,"supported":58,"title":191,"abstract":192,"tags":193},16,[90],[27,90],[168,27],{"code":46,"score":13,"supported":58,"title":195,"abstract":196,"tags":199},[],[74,197,76,198],"边缘计算","edge computing",[74],{"code":173,"score":20,"supported":58,"title":201,"abstract":202,"tags":205},[],[203,204,174],"灌溉","水资源",[],{"code":79,"score":80,"supported":58,"title":207,"abstract":208,"tags":209},[],[83,84],[29],{"code":211,"score":94,"supported":58,"title":212,"abstract":213,"tags":216},"H2",[],[214,215],"土壤","soil",[],{"code":218,"score":219,"supported":58,"title":220,"abstract":221,"tags":223},"A7",3,[],[222],"间作",[],{"code":225,"score":219,"supported":58,"title":226,"abstract":227,"tags":229},"F3",[],[228],"养分管理",[],[231,232],"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":234,"openalex_id":236,"authors":237,"venue":159,"cited_by_count":141,"oa_url":156,"card":248,"direction":148,"ingested_from":149},"W7223121456",[238,241,243,245],{"name":239,"orcid":240},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":242,"orcid":9},"Oluwadare OYEBODE",{"name":244,"orcid":9},"Iretiayo Adelaiye",{"name":246,"orcid":247},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":249,"method":250,"finding":251,"direction":146,"opportunity":252},"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":255,"title":256,"url":257,"summary":258,"summary_zh":259,"content":9,"source_name":260,"source_url":257,"published_at":261,"category":12,"cover_url":9,"hotness":262,"is_selected":14,"score":263,"score_detail":264,"sources":267,"tags":271,"primary_topic":274,"secondary_topics":275,"classification":277,"search_phrases":292,"slug":295,"view_count":21,"doi":9,"paper":296,"created_at":312},4672,"AI-BASED ENERGY EFFICIENT COMMUNICATION IN IOT SYSTEM","https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F309","The rapid growth of the Internet of Things (IoT) has led to a massive increase in the number of connected devices across various domains such as smart homes, healthcare, agriculture, and industrial automation. As these devices are often battery-powered and deployed in remote or large scale environments, energy efficiency has become a critical concern in IoT communication systems. Traditional IoT networks typically operate using fixed transmission power levels, which do not adapt to changing environmental or network conditions, resulting in unnecessary energy consumption and reduced battery life.To address these limitations, there is a growing need for intelligent and adaptive communication mechanisms that can dynamically optimize power usage without compromising reliability. In this context, Artificial Intelligence (AI)-based adaptive transmission power control has emerged as a promising solution. This review paper presents an analysis of AI-driven techniques that enable IoT devices to adjust their transmission power based on real-time conditions and network requirements.The study explores approaches such as machine learning-based prediction models, which utilize parameters like Received Signal Strength Indicator (RSSI), distance, interference levels, and packet loss rates to determine optimal transmission power. Additionally, the role of edge computing is examined, enabling faster decision-making with reduced latency. The integration of Low Power Wide Area Network (LPWAN) technologies is also discussed for supporting long-range and energy-efficient communication.Furthermore, the paper highlights how adaptive power control improves network performance by reducing energy wastage, minimizing interference, and extending device lifetime. These benefits are especially useful in applications like smart cities, precision agriculture, and industrial automation.Despite these advantages, challenges such as implementation complexity, resource constraints, and data privacy concerns remain. The paper also outlines future research directions, including the development of lightweight AI models and improved integration with emerging technologies.In conclusion, AIbased adaptive transmission power control significantly enhances energy efficiency and communication reliability, making it essential for the sustainable development of next- generation IoT systems. It also supports scalable and cost-effective deployment of IoT networks in diverse real- world environments.","物联网（IoT）的快速发展使得智能家居、医疗健康、农业和工业自动化等各个领域的联网设备数量大幅增加。由于这些设备通常由电池供电，且部署在偏远或大规模环境中，能效已成为物联网通信系统中的关键问题。传统物联网网络通常采用固定传输功率运行，无法适应不断变化的环境或网络条件，导致不必要的能耗和电池寿命缩短。为解决这些局限，人们日益需要能够在不影响可靠性的前提下动态优化功率使用的智能自适应通信机制。在此背景下，基于人工智能（AI）的自适应传输功率控制已成为一种有前景的解决方案。本综述论文分析了使物联网设备能够根据实时条件和网络需求调整传输功率的AI驱动技术。研究探讨了基于机器学习的预测模型等方法，这些方法利用接收信号强度指示（RSSI）、距离、干扰水平和丢包率等参数来确定最优传输功率。此外，还考察了边缘计算的作用，其能够以更低延迟实现更快决策。论文还讨论了低功耗广域网（LPWAN）技术的集成，以支持长距离和节能通信。进一步地，本文强调了自适应功率控制如何通过减少能量浪费、最小化干扰和延长设备寿命来改善网络性能。这些优势在智慧城市、精准农业和工业自动化等应用中尤为有用。尽管具有这些优势，实施复杂性、资源约束和数据隐私问题等挑战仍然存在。论文还概述了未来研究方向，包括开发轻量级AI模型以及与新兴技术的改进集成。总之，基于AI的自适应传输功率控制显著提升了能效和通信可靠性，使其成为下一代物联网系统可持续发展的关键。它还支持物联网网络在多样化现实环境中的可扩展且具有成本效益的部署。","International Journal of Mechanical Engineering Research and Technology","2026-10-08T00:00:00Z",25,47,{"impact":265,"substance":19,"depth":163,"authority":94,"freshness":94,"relevant":21,"comment":266},8,"综述性论文，方法层面有参考价值，但非农业专属突破，且期刊权威性一般，仅作主题聚合素材。",[268,269],{"name":260,"url":257},{"name":260,"url":270},"https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F310",[168,26,272,27,273],"农业物联网","低功耗通信",{"code":46,"slug":47,"name":48,"path":49,"parent_code":37},[276],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":278},{"candidates":279,"agricultural_evidence":58},[280,284,288],{"code":46,"score":57,"supported":58,"title":281,"abstract":282,"tags":283},[76],[74,197,75,76,198],[272],{"code":33,"score":13,"supported":58,"title":285,"abstract":286,"tags":287},[],[62,63,60],[26],{"code":87,"score":265,"supported":58,"title":289,"abstract":290,"tags":291},[],[27,90],[168,27],[293,294],"AI 物联网 传输功率控制","LPWAN 精准农业","AI物联网传输功率控制-4672",{"doi":9,"openalex_id":297,"authors":298,"venue":260,"cited_by_count":141,"oa_url":257,"card":307,"direction":148,"ingested_from":149},"W7221414266",[299,301,303,305],{"name":300,"orcid":9},"Shital Chavan",{"name":302,"orcid":9},"Anam Bedrekar",{"name":304,"orcid":9},"Sanika Babar",{"name":306,"orcid":9},"Prof. M. V. Naiknavare",{"tldr":308,"method":309,"finding":310,"direction":148,"opportunity":311},"综述AI自适应发射功率控制提升IoT通信能效，面向智慧农业等场景。","综述机器学习预测模型、边缘计算与LPWAN技术，基于RSSI等参数。","AI自适应功率控制可降低能耗、减少干扰并延长设备寿命，但存在实现复杂与隐私挑战。","可研究面向农田的轻量级AI功率控制模型，结合土壤墒情与作物需水优化能耗。","2026-10-11T23:30:16.397155Z",{"id":314,"title":315,"url":316,"summary":317,"summary_zh":318,"content":9,"source_name":319,"source_url":316,"published_at":320,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":321,"score_detail":322,"sources":327,"tags":329,"primary_topic":331,"secondary_topics":337,"classification":340,"search_phrases":374,"slug":377,"view_count":21,"doi":378,"paper":379,"created_at":392},4660,"IoT-Based Automatic Cotton Leaf Disease Detection, Infection Percentage Estimation, and Treatment Recommendation Using Raspberry PI","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23280692","Cotton is one of the most important commercial crops worldwide, and its productivity is significantly affected by various leaf diseases such as bacterial blight, alter aria leaf spot, Cercospora leaf spot, powdery mildew, and leaf curl disease. Early and accurate identification of these diseases is essential to minimize crop losses and improve yield. Traditional disease diagnosis relies on manual field inspection by experts, which is time-consuming, labour-intensive, and often impractical for large agricultural fields. The proposed system integrates a Raspberry Pi5, a high-resolution camera module, environmental sensors, and wireless communication technologies to create an intelligent crop monitoring platform. Images of cotton leaves are captured using the Raspberry Pi camera and processed through computer vision techniques for leaf segmentation and feature extraction. A lightweight deep learning model based on MobileNetV2 is employed to automatically identify different cotton leaf diseases. In addition to disease classification, image segmentation techniques are used to calculate the percentage of infected leaf area, enabling the system to determine the severity level of infection. The system also collects environmental parameters such as temperature, humidity, soil moisture, and light intensity using IoT sensors. These parameters, along with disease diagnosis results, are transmitted to a cloud-based dashboard for real-time monitoring and analysis. Automated alerts can be sent to farmers through SMS or mobile notifications when severe infections are detected. Experimental evaluation is expected to achieve disease classification accuracy above 93%, infection area estimation error below 5%, and real-time inference within one second per image on the Raspberry Pi platform. The proposed solution offers a cost-effective, portable, and scalable approach for precision agriculture by combining artificial intelligence, image processing, and IoT technologies. The system enables early disease detection, accurate infection assessment, timely treatment recommendations, reduced pesticide usage, and improved cotton crop productivity, making it a valuable tool for smart farming applications.","棉花是全球最重要的经济作物之一，其产量受到多种叶部病害的显著影响，如细菌性枯萎病（bacterial blight）、链格孢叶斑病（alternaria leaf spot）、尾孢叶斑病（Cercospora leaf spot）、白粉病（powdery mildew）和卷叶病（leaf curl disease）。早期准确识别这些病害对于减少作物损失和提高产量至关重要。传统的病害诊断依赖专家进行人工田间巡检，这种方式耗时、费力，且对于大面积农田往往不切实际。所提出的系统集成了Raspberry Pi5、高分辨率摄像头模块、环境传感器和无线通信技术，构建了一个智能作物监测平台。利用Raspberry Pi摄像头采集棉花叶片图像，并通过计算机视觉技术进行叶片分割和特征提取。采用基于MobileNetV2的轻量级深度学习模型自动识别不同的棉花叶部病害。除病害分类外，还利用图像分割技术计算受感染叶面积百分比，使系统能够确定感染的严重程度。该系统还通过物联网传感器采集温度、湿度、土壤水分和光照强度等环境参数。这些参数连同病害诊断结果一起传输至基于云端的仪表板，用于实时监测和分析。当检测到严重感染时，可通过短信或移动通知向农民发送自动警报。实验评估预期在Raspberry Pi平台上实现高于93%的病害分类准确率、低于5%的感染面积估计误差，以及每张图像一秒以内的实时推理。所提出的解决方案结合了人工智能、图像处理和物联网技术，为精准农业提供了一种经济高效、便携且可扩展的方法。该系统能够实现病害早期检测、准确感染评估、及时治疗建议、减少农药使用并提高棉花作物产量，使其成为智慧农业应用中的一种有价值的工具。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-10T00:00:00Z",74,{"impact":190,"substance":323,"depth":324,"authority":325,"freshness":20,"relevant":21,"comment":326},20,17,12,"基于树莓派与轻量深度学习的棉花叶病检测与物联网监测方案，方法具体、指标明确，对智慧植保有参考价值。",[328],{"name":319,"url":316},[168,26,74,27,330],"棉花病害",{"code":332,"slug":333,"name":334,"path":335,"parent_code":336},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[338,339],{"code":46,"slug":47,"name":48,"path":49,"parent_code":37},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":341},{"candidates":342,"agricultural_evidence":58},[343,348,354,359,363,370],{"code":46,"score":344,"supported":58,"title":345,"abstract":346,"tags":347},15,[76],[74,76],[74],{"code":332,"score":19,"supported":58,"title":349,"abstract":351,"tags":353},[350],"cotton",[352,350],"棉花",[],{"code":87,"score":355,"supported":58,"title":356,"abstract":357,"tags":358},11,[],[168,27,90],[168,27],{"code":33,"score":13,"supported":58,"title":360,"abstract":361,"tags":362},[],[184,185,186,60,187],[26],{"code":364,"score":94,"supported":58,"title":365,"abstract":366,"tags":369},"F4",[],[367,368],"农药","pesticide",[],{"code":211,"score":94,"supported":58,"title":371,"abstract":372,"tags":373},[],[214,215],[],[375,376],"Raspberry Pi 棉花病害检测","MobileNetV2 棉花叶病识别","RaspberryPi棉花病害检测-4660","10.5281\u002Fzenodo.23280692",{"doi":378,"openalex_id":380,"authors":381,"venue":319,"cited_by_count":141,"oa_url":316,"card":387,"direction":148,"ingested_from":149},"W7221340722",[382,384],{"name":383,"orcid":9},"Deepak R Khadse",{"name":385,"orcid":386},"Pankaj Hiraman Zope","https:\u002F\u002Forcid.org\u002F0000-0003-3669-1440",{"tldr":388,"method":389,"finding":390,"direction":148,"opportunity":391},"基于树莓派与物联网，实现棉花叶病自动检测、感染率估算与防治推荐。","树莓派5+摄像头+环境传感器，MobileNetV2分类与图像分割，云端看板。","预期分类准确率超93%，感染面积误差低于5%，单图推理1秒内。","可探索边缘端轻量模型与多病害严重度分级、精准施药决策的闭环验证。","2026-10-11T23:30:07.876353Z",{"id":394,"title":395,"url":396,"summary":397,"summary_zh":398,"content":9,"source_name":319,"source_url":396,"published_at":399,"category":12,"cover_url":9,"hotness":262,"is_selected":14,"score":400,"score_detail":401,"sources":403,"tags":408,"primary_topic":411,"secondary_topics":412,"classification":419,"search_phrases":456,"slug":459,"view_count":21,"doi":460,"paper":461,"created_at":471},4573,"APPLICATION OF ARTIFICIAL INTELLIGENCE AND AUTOMATION TECHNOLOGIES IN MODERN AGRICULTURAL MACHINERY","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23255982","The rapid development of artificial intelligence (AI), robotics, computer vision, Internet of Things (IoT), satellite navigation, remote sensing, and autonomous control systems is transforming the design and operation of modern agricultural machinery. Agricultural machinery is increasingly evolving from conventional mechanical equipment into intelligent cyber-physical systems capable of sensing field conditions, processing large volumes of data, making operational decisions, and executing agricultural tasks with limited human intervention. This article examines the application of artificial intelligence and automation technologies in tractors, harvesters, sprayers, robotic platforms, unmanned aerial vehicles, and other agricultural machinery. The study is based on a systematic analysis of scientific publications, international institutional reports, and documented agricultural technology adoption data. Particular attention is given to autonomous navigation, machine vision, precision spraying, variable-rate application, crop monitoring, yield prediction, robotic weeding, harvesting, and safety systems. Evidence from the United States demonstrates substantial growth in precision-agriculture adoption: automated guidance systems were used on more than half of the acreage planted to several major crops, while in 2023 guidance autosteering was used by 52 percent of midsize farms and 70 percent of large-scale crop-producing farms. Scientific literature also demonstrates the growing role of machine learning in yield estimation, nitrogen management, disease detection, and robotic perception. However, high acquisition costs, inadequate rural connectivity, data governance issues, cybersecurity, technical complexity, and safety risks remain significant barriers. The study concludes that AI-enabled agricultural machinery can contribute to higher operational efficiency, more precise input application, reduced operator fatigue, improved resource management, and greater agricultural sustainability, provided that technological development is accompanied by appropriate infrastructure, human-capacity development, interoperability standards, and safety regulation.","人工智能（AI）、机器人技术、计算机视觉、物联网（IoT）、卫星导航、遥感及自主控制系统的快速发展，正在深刻改变现代农业机械的设计与运行方式。农业机械正日益从传统机械设备演变为智能信息物理系统，能够感知田间条件、处理海量数据、做出作业决策，并在有限人工干预下执行农业任务。本文探讨了人工智能与自动化技术在拖拉机、联合收割机、喷雾机、机器人平台、无人机及其他农业机械中的应用。研究基于对科学文献、国际机构报告及已记录的农业技术采用数据的系统分析。重点关注自主导航、机器视觉、精准喷雾、变量施用、作物监测、产量预测、机器人除草、收获及安全系统。来自美国的证据表明，精准农业的采用率大幅增长：自动导航系统已应用于多种主要作物一半以上的种植面积，而2023年，52%的中型农场和70%的大型作物生产农场使用了导航自动转向技术。科学文献也表明，机器学习在产量估算、氮素管理、病害检测和机器人感知中的作用日益增强。然而，高昂的购置成本、农村网络连接不足、数据治理问题、网络安全、技术复杂性及安全风险仍是重要障碍。研究结论认为，在技术发展的同时配套适当的基础设施、人力能力建设、互操作性标准和安全监管，人工智能赋能的农业机械能够有助于提高作业效率、实现更精准的投入施用、减轻操作人员疲劳、改善资源管理并增强农业可持续性。","2026-10-09T00:00:00Z",81,{"impact":17,"substance":323,"depth":57,"authority":163,"freshness":265,"relevant":21,"comment":402},"系统梳理AI与自动化在农机领域应用的综述性论文，含美国精准农业采纳率等实证数据，对智慧农业主题聚合有较高参考价值。",[404,405],{"name":319,"url":396},{"name":406,"url":407},"International Bulletin of Engineering and Technology","https:\u002F\u002Finternationalbulletins.com\u002Fintjour\u002Findex.php\u002Fibet\u002Farticle\u002Fview\u002F2468",[168,26,409,272,27,410],"农业机器人","自动驾驶农机",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[413,418],{"code":414,"slug":415,"name":416,"path":417,"parent_code":44},"F5","machinery","农机装备与机械化","\u002Ftopic\u002Finputs-equipment\u002Fmachinery",{"code":46,"slug":47,"name":48,"path":49,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":420},{"candidates":421,"agricultural_evidence":58},[422,426,433,437,441,446,451],{"code":33,"score":57,"supported":58,"title":423,"abstract":424,"tags":425},[60],[62,185,63,60,187],[26],{"code":414,"score":324,"supported":58,"title":427,"abstract":429,"tags":432},[428],"agricultural machinery",[430,431,428],"农业机械","收割机",[],{"code":46,"score":13,"supported":58,"title":434,"abstract":435,"tags":436},[],[74,75,76],[272],{"code":122,"score":94,"supported":58,"title":438,"abstract":439,"tags":440},[],[125,126],[],{"code":87,"score":442,"supported":58,"title":443,"abstract":444,"tags":445},5,[],[27],[168,27],{"code":364,"score":219,"supported":58,"title":447,"abstract":448,"tags":450},[],[449],"除草",[],{"code":452,"score":21,"supported":14,"title":453,"abstract":454,"tags":455},"F6",[],[],[409],[457,458],"农业机械 人工智能 自动化","精准农业 自动驾驶 农机","农业机械人工智能自动化-4573","10.5281\u002Fzenodo.23255982",{"doi":460,"openalex_id":462,"authors":463,"venue":319,"cited_by_count":141,"oa_url":396,"card":466,"direction":148,"ingested_from":149},"W7221138004",[464],{"name":465,"orcid":9},"Rahimjonov Shoxrux Shuhratjon o'g'li",{"tldr":467,"method":468,"finding":469,"direction":148,"opportunity":470},"系统综述AI与自动化技术在现代农机中的应用现状、效益与障碍。","系统分析科学文献、国际机构报告及美国精准农业采用数据。","AI农机可提升效率与可持续性，但成本、连接性、数据治理与安全仍是主要障碍。","可研究低成本AI农机方案、农村连接与数据治理标准，及人机协同安全监管框架。","2026-10-10T23:30:13.826410Z",{"id":473,"title":474,"url":475,"summary":476,"summary_zh":477,"content":9,"source_name":478,"source_url":475,"published_at":479,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":480,"score_detail":481,"sources":483,"tags":485,"primary_topic":488,"secondary_topics":489,"classification":496,"search_phrases":548,"slug":551,"view_count":141,"doi":552,"paper":553,"created_at":571},4302,"BIG DATA IN AGRONOMY: TECHNOLOGIES, APPLICATIONS, BENEFITS AND CHALLENGES","https:\u002F\u002Fdoi.org\u002F10.4238\u002Fk6hcgw77","Big Data is becoming an important component of modern agronomy by enabling the integration, processing and analysis of large, diverse and continuously generated datasets for improved agricultural decision-making. Agricultural Big Data is generated from multiple sources, including remote sensing satellites, Unmanned Aerial Vehicles (UAVs), Internet of Things (IoT) sensors, weather stations, farm machinery, field observations and historical databases. The integration of these data sources with Geographic Information Systems (GIS), cloud computing, Artificial Intelligence (AI) and Machine Learning (ML) enables the identification of spatial and temporal patterns that are difficult to detect using conventional approaches. Big Data applications in agronomy include climate and weather prediction, pest and disease management, soil health and nutrient management, irrigation scheduling, crop yield prediction, precision agriculture, crop insurance and weed management. Examples from India and other regions demonstrate the application of satellite imagery, UAV-based sensing, IoT systems, crop-growth models and machine-learning algorithms for field-scale and regional agricultural decision making. Big Data analytics can improve resource-use efficiency, support predictive and real-time monitoring, strengthen agricultural risk assessment and facilitate site-specific management. However, effective implementation is constrained by challenges such as high infrastructure and operational costs, data quality and interoperability issues, data privacy and ownership concerns, limited rural digital infrastructure, inadequate technical skills and increasing requirements for data storage and computing. Therefore, the effective integration of Big Data with agronomic knowledge and decision-support systems can contribute to more precise, efficient and sustainable agricultural production while addressing emerging challenges related to climate variability, resource scarcity and food security.","大数据正成为现代农学的重要组成部分，它能够整合、处理和分析大规模、多样化且持续生成的数据集，从而改进农业决策。农业大数据来源于多种渠道，包括遥感卫星、无人机（UAV）、物联网（IoT）传感器、气象站、农业机械、田间观测和历史数据库。将这些数据源与地理信息系统（GIS）、云计算、人工智能（AI）和机器学习（ML）相结合，能够识别传统方法难以发现的空间和时间模式。大数据在农学中的应用包括气候与天气预测、病虫害管理、土壤健康与养分管理、灌溉调度、作物产量预测、精准农业、作物保险和杂草管理。来自印度及其他地区的案例展示了卫星影像、基于无人机的遥感、物联网系统、作物生长模型和机器学习算法在田块尺度和区域农业决策中的应用。大数据分析可以提高资源利用效率，支持预测性和实时监测，加强农业风险评估，并促进因地制宜的管理。然而，有效实施受到诸多挑战的制约，如高昂的基础设施和运营成本、数据质量和互操作性问题、数据隐私和所有权担忧、农村数字基础设施有限、技术技能不足以及数据存储和计算需求不断增加。因此，将大数据与农学知识和决策支持系统有效整合，有助于实现更精准、高效和可持续的农业生产，同时应对气候变异性、资源稀缺和粮食安全等方面的新兴挑战。","Genetics and Molecular Research","2026-10-05T00:00:00Z",76,{"impact":57,"substance":323,"depth":324,"authority":163,"freshness":265,"relevant":21,"comment":482},"系统综述农业大数据技术、应用与挑战，信息密度高，对智慧农业决策有参考价值，但属综述性论文，非突破性成果。",[484],{"name":478,"url":475},[168,26,486,27,487],"农业大数据","遥感监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[490,495],{"code":491,"slug":492,"name":493,"path":494,"parent_code":37},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":46,"slug":47,"name":48,"path":49,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":497},{"candidates":498,"agricultural_evidence":58},[499,503,510,514,518,522,526,530,534,540,544],{"code":33,"score":13,"supported":58,"title":500,"abstract":501,"tags":502},[],[62,63,60],[26],{"code":491,"score":20,"supported":58,"title":504,"abstract":505,"tags":509},[],[506,507,508],"地理信息","GIS","geographic information",[],{"code":46,"score":20,"supported":58,"title":511,"abstract":512,"tags":513},[],[74,75,76],[],{"code":87,"score":265,"supported":58,"title":515,"abstract":516,"tags":517},[],[27,90],[168,27],{"code":122,"score":80,"supported":58,"title":519,"abstract":520,"tags":521},[],[125,126],[487],{"code":115,"score":94,"supported":58,"title":523,"abstract":524,"tags":525},[],[118,119],[],{"code":211,"score":94,"supported":58,"title":527,"abstract":528,"tags":529},[],[214,215],[],{"code":173,"score":94,"supported":58,"title":531,"abstract":532,"tags":533},[],[203,174],[],{"code":535,"score":536,"supported":58,"title":537,"abstract":538,"tags":539},"I6",4,[],[486],[486],{"code":225,"score":219,"supported":58,"title":541,"abstract":542,"tags":543},[],[228],[],{"code":414,"score":219,"supported":58,"title":545,"abstract":546,"tags":547},[],[430],[],[549,550],"农业大数据 遥感 物联网","机器学习 作物产量预测","农业大数据遥感物联网-4302","10.4238\u002Fk6hcgw77",{"doi":552,"openalex_id":554,"authors":555,"venue":478,"cited_by_count":141,"oa_url":475,"card":566,"direction":148,"ingested_from":149},"W7220374836",[556,558,560,562,564],{"name":557,"orcid":9},"Kodavali Aswini",{"name":559,"orcid":9},"Raju Marimuthu",{"name":561,"orcid":9},"Guru Gurusamy",{"name":563,"orcid":9},"Kannan Pandian",{"name":565,"orcid":9},"Anandham Rangasamy",{"tldr":567,"method":568,"finding":569,"direction":146,"opportunity":570},"综述农学大数据的技术、应用、效益与挑战，强调多源数据融合支撑精准农业决策。","整合遥感、无人机、物联网、GIS、云计算与AI\u002FML的多源农业数据分析。","大数据可提升资源效率与实时监测，但受成本、数据质量、隐私和农村数字基建制约。","可研究低成本边缘计算与数据互操作标准，破解小农户场景下大数据落地瓶颈。","2026-10-06T23:30:11.880721Z",{"id":573,"title":574,"url":575,"summary":576,"summary_zh":577,"content":9,"source_name":578,"source_url":575,"published_at":579,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":580,"score_detail":581,"sources":583,"tags":585,"primary_topic":587,"secondary_topics":588,"classification":594,"search_phrases":614,"slug":617,"view_count":132,"doi":618,"paper":619,"created_at":631},4058,"Sustainability intelligence: Integrating Artificial Intelligence, the Internet of Things, and Digital Twins for resource-efficient and climate-resilient systems","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.nxsust.2026.100535","Sustainability has become one of the defining scientific and societal challenges of the twenty-first century. Climate change, rapid urbanization, population growth, increasing energy demand, and declining natural resources require decision-making that is continuous, adaptive, and supported by reliable environmental intelligence. Conventional monitoring systems, which often rely on periodic measurements and isolated analytical tools, are no longer sufficient for managing highly dynamic environmental systems. Recent advances in Artificial Intelligence (AI), the Internet of Things (IoT), edge computing, and Digital Twins provide unprecedented opportunities to transform sustainability management from reactive monitoring into intelligent, predictive, and autonomous decision-making. IoT enables continuous environmental sensing, AI extracts knowledge from large heterogeneous datasets, edge intelligence supports low-latency analytics, while Digital Twins create dynamic virtual representations of physical systems capable of simulation and optimization. This critical integrative review examines recent developments in AI-enabled sustainability across smart energy systems, intelligent water management, precision agriculture, transportation, smart buildings, circular economy, and climate resilience. Rather than reviewing these technologies independently, the paper analyzes their interactions within an integrated digital ecosystem and discusses emerging topics including Green AI, carbon-aware computing, explainable AI, federated learning, cybersecurity, and sustainability governance. The review method combines a structured multi-database search with targeted evidence and standards updates through June 2026. Building upon the reviewed literature, the paper introduces the Sustainability Intelligence Framework (SIF), a conceptual seven-layer architecture integrating IoT sensing, edge intelligence, AI analytics, Digital Twins, decision intelligence, and sustainability assessment within a continuous feedback cycle. The proposed framework provides a systems-level perspective for designing adaptive, resilient, and resource-efficient infrastructures capable of supporting future sustainable development. The synthesis identifies continuous, auditable sustainability assessment (Layer 7), rather than the underlying six-layer IoT\u002Fcyber–physical stack, as the framework’s distinct contribution. It also shows that reported benefits remain difficult to compare because field validation, system boundaries, rebound effects, and lifecycle digital burdens are inconsistently reported.","可持续性已成为二十一世纪具有决定性意义的科学与社会挑战之一。气候变化、快速城市化、人口增长、能源需求上升以及自然资源减少，要求决策必须持续、自适应，并由可靠的环境智能提供支撑。传统监测系统往往依赖周期性测量和孤立分析工具，已不足以管理高度动态的环境系统。人工智能（AI）、物联网（IoT）、边缘计算和数字孪生（Digital Twins）的最新进展，为将可持续性管理从被动监测转变为智能、预测性和自主决策提供了前所未有的机遇。物联网能够实现持续环境感知，人工智能可从大规模异构数据集中提取知识，边缘智能支持低时延分析，而数字孪生则创建物理系统的动态虚拟表示，以支持仿真与优化。这篇批判性综合综述考察了AI赋能可持续性在智能能源系统、智能水管理、精准农业、交通、智能建筑、循环经济和气候韧性等领域的最新进展。本文并未孤立地评述这些技术，而是分析它们在集成数字生态系统中的相互作用，并讨论绿色AI、碳感知计算、可解释AI、联邦学习、网络安全和可持续性治理等新兴议题。综述方法结合了结构化多数据库检索，并通过截至2026年6月的针对性证据与标准更新加以补充。在所评述文献的基础上，本文提出可持续性智能框架（Sustainability Intelligence Framework, SIF），这是一个概念性七层架构，在持续反馈循环中整合物联网感知、边缘智能、AI分析、数字孪生、决策智能和可持续性评估。所提出的框架为设计能够支持未来可持续发展的自适应、韧性和资源高效型基础设施提供了系统层面视角。该综合指出，框架的独特贡献在于持续、可审计的可持续性评估（第7层），而非底层六层物联网\u002F网络—物理技术栈。研究还表明，由于现场验证、","Next Sustainability","2026-10-02T00:00:00Z",78,{"impact":57,"substance":323,"depth":57,"authority":163,"freshness":20,"relevant":21,"comment":582},"该综述提出七层可持续智能框架，系统整合AI、物联网与数字孪生，对智慧农业与气候韧性基础设施具有较高参考价值，但偏概念性、缺乏实证数据。",[584],{"name":578,"url":575},[168,26,74,586,27],"数字孪生",{"code":46,"slug":47,"name":48,"path":49,"parent_code":37},[589,590],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":79,"slug":591,"name":592,"path":593,"parent_code":177},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"status":51,"locked":14,"version":52,"method":53,"evidence":595},{"candidates":596,"agricultural_evidence":58},[597,601,605,609],{"code":46,"score":57,"supported":58,"title":598,"abstract":599,"tags":600},[75],[74,197,75,76,198],[74],{"code":87,"score":325,"supported":58,"title":602,"abstract":603,"tags":604},[],[27,586,90],[586,168,27],{"code":33,"score":325,"supported":58,"title":606,"abstract":607,"tags":608},[60],[60],[26],{"code":79,"score":20,"supported":58,"title":610,"abstract":611,"tags":613},[],[83,84,612],"climate resilience",[],[615,616],"农业人工智能 数字孪生 智慧农业 精准农业","农业人工智能 数字孪生","农业人工智能数字孪生智慧农业精准农业-4058","10.1016\u002Fj.nxsust.2026.100535",{"doi":618,"openalex_id":620,"authors":621,"venue":578,"cited_by_count":141,"oa_url":575,"card":625,"direction":148,"ingested_from":149},"W7216004668",[622],{"name":623,"orcid":624},"Wael Maged Badawy","https:\u002F\u002Forcid.org\u002F0000-0003-0251-9124",{"tldr":626,"method":627,"finding":628,"direction":629,"opportunity":630},"综述AI、物联网与数字孪生融合的可持续性智能，提出七层SIF框架。","多数据库结构化检索，整合IoT、边缘计算、AI与数字孪生文献。","持续可审计的可持续性评估是框架核心贡献，但实地验证仍难比较。","其他","农业场景下SIF七层框架的田间验证与可审计碳\u002F资源评估方法尚属空白。","2026-10-03T23:30:09.950820Z"]