[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1261":3,"related-1261":93},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":21,"tags":23,"primary_topic":8,"secondary_topics":28,"classification":29,"search_phrases":64,"slug":67,"view_count":19,"doi":68,"paper":69,"created_at":92},1261,"Energy efficient routing protocols in WSN for precision and smart agriculture","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44354-026-00045-y","The rising global need for food has increased the demand for smart agricultural technologies that boost productivity while staying sustainable. Wireless Sensor Networks (WSNs) are central to Precision Agriculture (PA) by facilitating real-time tracking of environmental factors like soil moisture, temperature, and humidity. However, WSNs encounter notable challenges mainly concerning energy use, network lifespan, and efficiency in data transmission. This survey delivers a thorough PRISMA-based systematic review of energy-efficient routing protocols tailored for WSN in PA. Initially, 250 research studies were identified from major databases, and after removing duplicates and screening titles, 150 articles remained. After thorough checking 50 papers are removed as they are irrelevant to the research questions. 43 high quality research articles satisfied quality assessment criteria and published in between 2016-2025 are considered for full text review and detail analysis. The selected routing frameworks are classified into six categories: clustering based, optimization based, fuzzy based, machine learning based, security aware and hybrid approach. This study focuses on energy efficient routing protocol of WSN for precision and smart agriculture. It investigates performance of various Quality of Service (QoS) metrics like energy consumption, packet delivery ratio (PDR) and network lifetime. This study analysis impact of different types of protocol. The analysis demonstrates that clustering and optimization based frameworks provide maximum improvement in network lifetime and data delivery. Whereas, machine learning and hybrid approaches show better scalability, adaptability and intelligent routing under dynamic agriculture scenarios. While, security aware frameworks improve reliability under malicious attacks but increase computational overhead. Overall the study insights into the strengths and limitations of existing routing frameworks and concludes future enhancement of energy efficient routing protocol for precision and smart agriculture.",null,"Discover Networks","2026-08-30T00:00:00Z","论文",10,false,68,{"impact":16,"substance":17,"depth":16,"authority":12,"freshness":18,"relevant":19,"comment":20},18,20,2,1,"系统综述WSN节能路由协议在精准农业中的应用，分类详尽，对农业信息化有参考价值，但时效性较低。",[22],{"name":9,"url":6},[24,25,26,27],"智慧农业","精准农业","无线传感网络","节能路由",[],{"status":30,"locked":13,"version":31,"method":32,"evidence":33},"needs_review","2026-10-22.2","rules",{"candidates":34,"agricultural_evidence":38,"reason":63},[35,44,51,57],{"code":36,"score":37,"supported":38,"title":39,"abstract":41,"tags":43},"I1",16,true,[40],"smart agriculture",[40,42],"precision agriculture",[24,25],{"code":45,"score":46,"supported":38,"title":47,"abstract":48,"tags":50},"H2",3,[],[49],"soil",[],{"code":52,"score":46,"supported":38,"title":53,"abstract":54,"tags":56},"I2",[],[55],"machine learning",[],{"code":58,"score":46,"supported":38,"title":59,"abstract":60,"tags":62},"I5",[],[61],"wireless sensor",[],"多个产业证据相当，需要确认主归属",[65,66],"无线传感网络 智慧农业 精准农业 节能路由","无线传感网络 智慧农业","无线传感网络智慧农业精准农业节能路由-1261","10.1007\u002Fs44354-026-00045-y",{"doi":68,"openalex_id":70,"authors":71,"venue":9,"cited_by_count":82,"oa_url":83,"card":84,"direction":90,"ingested_from":91},"W7204735724",[72,74,77,80],{"name":73,"orcid":8},"Tumpa Pal",{"name":75,"orcid":76},"Sohail Saif","https:\u002F\u002Forcid.org\u002F0000-0002-7076-7640",{"name":78,"orcid":79},"Ramesh Saha","https:\u002F\u002Forcid.org\u002F0000-0003-0003-9450",{"name":81,"orcid":8},"Suparna Biswas",0,"https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44354-026-00045-y.pdf",{"tldr":85,"method":86,"finding":87,"direction":88,"opportunity":89},"系统综述了面向精准农业的WSN节能路由协议，分类比较其性能。","PRISMA系统综述，筛选2016-2025年43篇文献，分类为六种路由框架。","聚类和优化框架提升网络寿命和传输，机器学习和混合方法适应动态场景。","其他","可探索混合路由协议，结合机器学习与安全机制，平衡能耗、适应性和安全性。","智慧农业 \u002F 农业物联网","openalex","2026-09-01T04:03:11.623955Z",{"total":94,"page":19,"page_size":94,"items":95},6,[96,213,268,381,460,500],{"id":97,"title":98,"url":99,"summary":100,"summary_zh":101,"content":8,"source_name":102,"source_url":99,"published_at":103,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":104,"score_detail":105,"sources":110,"tags":112,"primary_topic":116,"secondary_topics":121,"classification":132,"search_phrases":188,"slug":191,"view_count":82,"doi":192,"paper":193,"created_at":212},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":16,"substance":106,"depth":16,"authority":107,"freshness":108,"relevant":19,"comment":109},22,13,9,"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。",[111],{"name":102,"url":99},[24,113,114,25,115],"农业人工智能","物联网","气候韧性",{"code":52,"slug":117,"name":118,"path":119,"parent_code":120},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[122,126],{"code":58,"slug":123,"name":124,"path":125,"parent_code":120},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":127,"slug":128,"name":129,"path":130,"parent_code":131},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":133,"locked":13,"version":31,"method":32,"evidence":134},"classified",{"candidates":135,"agricultural_evidence":38},[136,146,150,157,163,171,176,182],{"code":52,"score":16,"supported":38,"title":137,"abstract":139,"tags":145},[138],"artificial intelligence",[140,141,142,55,143,144],"深度学习","机器学习","计算机视觉","deep learning","computer vision",[113],{"code":36,"score":37,"supported":38,"title":147,"abstract":148,"tags":149},[42],[25,42],[24,25],{"code":58,"score":12,"supported":38,"title":151,"abstract":152,"tags":156},[],[114,153,154,155],"边缘计算","IoT","edge computing",[114],{"code":127,"score":108,"supported":38,"title":158,"abstract":159,"tags":162},[],[160,161,128],"灌溉","水资源",[],{"code":164,"score":165,"supported":38,"title":166,"abstract":167,"tags":170},"H7",7,[],[168,169],"气候变化","climate change",[115],{"code":45,"score":94,"supported":38,"title":172,"abstract":173,"tags":175},[],[174,49],"土壤",[],{"code":177,"score":46,"supported":38,"title":178,"abstract":179,"tags":181},"A7",[],[180],"间作",[],{"code":183,"score":46,"supported":38,"title":184,"abstract":185,"tags":187},"F3",[],[186],"养分管理",[],[189,190],"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":192,"openalex_id":194,"authors":195,"venue":102,"cited_by_count":82,"oa_url":99,"card":206,"direction":90,"ingested_from":91},"W7223121456",[196,199,201,203],{"name":197,"orcid":198},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":200,"orcid":8},"Oluwadare OYEBODE",{"name":202,"orcid":8},"Iretiayo Adelaiye",{"name":204,"orcid":205},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":207,"method":208,"finding":209,"direction":210,"opportunity":211},"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。","农业人工智能与决策模型","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":214,"title":215,"url":216,"summary":217,"summary_zh":8,"content":8,"source_name":218,"source_url":216,"published_at":219,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":220,"score_detail":221,"sources":225,"tags":227,"primary_topic":230,"secondary_topics":234,"classification":235,"search_phrases":247,"slug":250,"view_count":82,"doi":251,"paper":252,"created_at":267},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":37,"substance":222,"depth":16,"authority":223,"freshness":12,"relevant":19,"comment":224},21,14,"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。",[226],{"name":218,"url":216},[24,113,25,228,229],"机器视觉","作物行检测",{"code":36,"slug":231,"name":232,"path":233,"parent_code":120},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":133,"locked":13,"version":31,"method":32,"evidence":236},{"candidates":237,"agricultural_evidence":38},[238,243],{"code":36,"score":239,"supported":38,"title":240,"abstract":241,"tags":242},5,[],[42],[24,25],{"code":52,"score":18,"supported":13,"title":244,"abstract":245,"tags":246},[],[],[113,228],[248,249],"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":251,"openalex_id":253,"authors":254,"venue":218,"cited_by_count":82,"oa_url":8,"card":8,"direction":8,"ingested_from":91},"W7223117828",[255,258,261,264],{"name":256,"orcid":257},"Esther Gascó","https:\u002F\u002Forcid.org\u002F0000-0003-1798-4483",{"name":259,"orcid":260},"Clara I. López-González","https:\u002F\u002Forcid.org\u002F0000-0002-2755-1692",{"name":262,"orcid":263},"Eva Besada-Portas","https:\u002F\u002Forcid.org\u002F0000-0001-6129-4653",{"name":265,"orcid":266},"Gonzalo Pájares","https:\u002F\u002Forcid.org\u002F0000-0003-0915-6282","2026-10-22T11:20:09.678587Z",{"id":269,"title":270,"url":271,"summary":272,"summary_zh":273,"content":8,"source_name":274,"source_url":271,"published_at":275,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":276,"score_detail":277,"sources":280,"tags":282,"primary_topic":287,"secondary_topics":293,"classification":294,"search_phrases":343,"slug":346,"view_count":82,"doi":347,"paper":348,"created_at":380},5480,"Sustainable Plant Disease Management: Current Advances, Challenges, and Future Directions","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu182010661","Sustainable plant disease management is essential for maintaining agricultural productivity, environmental health, and global food security. Climate change has increased crop vulnerability to emerging and re-emerging plant diseases, posing significant challenges for agricultural sustainability. Although synthetic chemical pesticides have long been effective for disease control, their extensive use is increasingly questioned because of environmental contamination, pathogen resistance, and potential risks to human and ecosystem health. Consequently, plant disease management (PDM) is shifting toward more sustainable and integrated approaches. Future strategies are expected to incorporate multiple disciplines, including: (i) biological control and microbiome engineering to enhance natural plant defense mechanisms; (ii) advanced molecular and genomic technologies, such as CRISPR\u002FCas systems, RNA interference (RNAi), and epigenome editing, to develop durable disease resistance; (iii) smart delivery systems for the precise application of antimicrobial compounds and defense elicitors; (iv) nanotechnology-based platforms for the targeted delivery of nutrients, pesticides, and biocontrol agents, improving efficacy while reducing chemical inputs; (v) digital and precision agriculture technologies, including remote sensing, unmanned aerial vehicles (UAVs), and artificial intelligence (AI)-based diagnostics, to facilitate real-time disease surveillance and targeted interventions; and (vi) agroecological practices such as crop diversification, soil health restoration, and climate-smart farming to enhance agroecosystem resilience. These innovations aim not only to suppress plant pathogens but also to build sustainable, adaptive, and resilient agricultural systems that address future environmental and biological challenges.","可持续的植物病害管理对于维持农业生产力、环境健康和全球粮食安全至关重要。气候变化加剧了作物对新发和再发植物病害的脆弱性，给农业可持续性带来了重大挑战。尽管合成化学农药长期以来在病害防控中行之有效，但其广泛使用正日益受到质疑，原因包括环境污染、病原菌抗药性以及对人类和生态系统健康的潜在风险。因此，植物病害管理（plant disease management, PDM）正转向更加可持续和综合的途径。未来的策略预计将融合多个学科，包括：(i) 生物防治和微生物组工程，以增强植物天然防御机制；(ii) 先进的分子和基因组技术，如CRISPR\u002FCas系统、RNA干扰（RNAi）和表观基因组编辑，以培育持久抗病性；(iii) 用于精准施用抗菌化合物和防御诱导子的智能递送系统；(iv) 基于纳米技术的平台，用于靶向递送养分、农药和生物防治剂，在提高功效的同时减少化学投入；(v) 数字和精准农业技术，包括遥感、无人机（UAVs）和基于人工智能（AI）的诊断，以促进实时病害监测和靶向干预；以及(vi) 农业生态实践，如作物多样化、土壤健康恢复和气候智慧型农业，以增强农业生态系统的韧性。这些创新不仅旨在抑制植物病原菌，还旨在构建可持续、适应性强且具有韧性的农业系统，以应对未来的环境和生物挑战。","Sustainability","2026-10-20T00:00:00Z",81,{"impact":106,"substance":17,"depth":16,"authority":107,"freshness":278,"relevant":19,"comment":279},8,"系统综述可持续植物病害管理六大技术方向，涵盖AI诊断、纳米递送与基因编辑，对智慧农业与绿色防控具参考价值。",[281],{"name":274,"url":271},[24,25,283,284,285,286],"基因编辑","植物病害","生物防治","纳米技术",{"code":288,"slug":289,"name":290,"path":291,"parent_code":292},"F4","crop-protection","农药与绿色防控","\u002Ftopic\u002Finputs-equipment\u002Fcrop-protection","F",[],{"status":133,"locked":13,"version":31,"method":32,"evidence":295},{"candidates":296,"agricultural_evidence":38},[297,301,306,313,317,321,328,334,338],{"code":36,"score":278,"supported":38,"title":298,"abstract":299,"tags":300},[],[25,42],[24,25],{"code":288,"score":165,"supported":38,"title":302,"abstract":303,"tags":305},[],[304,285],"农药",[285],{"code":307,"score":94,"supported":38,"title":308,"abstract":309,"tags":312},"A8",[],[310,311],"粮食安全","food security",[],{"code":45,"score":94,"supported":38,"title":314,"abstract":315,"tags":316},[],[174,49],[],{"code":164,"score":94,"supported":38,"title":318,"abstract":319,"tags":320},[],[168,169],[],{"code":322,"score":94,"supported":38,"title":323,"abstract":324,"tags":327},"I3",[],[325,326],"遥感","remote sensing",[],{"code":329,"score":46,"supported":38,"title":330,"abstract":331,"tags":333},"H6",[],[332],"农业生态",[],{"code":52,"score":46,"supported":38,"title":335,"abstract":336,"tags":337},[],[138],[],{"code":339,"score":19,"supported":13,"title":340,"abstract":341,"tags":342},"F2",[],[],[283],[344,345],"CRISPR RNAi 作物抗病","基因编辑 智慧农业 植物病害 生物防治","CRISPRRNAi作物抗病-5480","10.3390\u002Fsu182010661",{"doi":347,"openalex_id":349,"authors":350,"venue":274,"cited_by_count":82,"oa_url":271,"card":375,"direction":90,"ingested_from":91},"W7223044776",[351,354,357,360,363,366,369,372],{"name":352,"orcid":353},"Karim M. Hassan","https:\u002F\u002Forcid.org\u002F0000-0002-1241-2307",{"name":355,"orcid":356},"Basma T. Abd-Elhalim","https:\u002F\u002Forcid.org\u002F0000-0002-3009-332X",{"name":358,"orcid":359},"Mohamed K. Abou El-Nasr","https:\u002F\u002Forcid.org\u002F0000-0003-1451-833X",{"name":361,"orcid":362},"Essam Yosef Abdul-Hafeez","https:\u002F\u002Forcid.org\u002F0000-0003-4744-4672",{"name":364,"orcid":365},"Ahmed N. Abdelhamid","https:\u002F\u002Forcid.org\u002F0000-0002-1326-7885",{"name":367,"orcid":368},"Mostafa Abdelkader","https:\u002F\u002Forcid.org\u002F0000-0003-2339-5087",{"name":370,"orcid":371},"Mohamed Abd El-Hamed Nasser","https:\u002F\u002Forcid.org\u002F0000-0002-0328-2969",{"name":373,"orcid":374},"Mahmoud A. A. Ali","https:\u002F\u002Forcid.org\u002F0000-0002-9062-0048",{"tldr":376,"method":377,"finding":378,"direction":90,"opportunity":379},"综述植物病害可持续管理的多学科前沿进展、挑战与未来方向。","文献综述，整合生物防治、基因编辑、纳米递送、数字农业与生态农艺。","未来病害管理需融合生物、分子、纳米与数字技术构建韧性农业系统。","遥感、无人机与AI诊断的实时病害监测闭环仍缺田间验证与低成本落地研究。","2026-10-21T23:30:09.242484Z",{"id":382,"title":383,"url":384,"summary":385,"summary_zh":386,"content":8,"source_name":387,"source_url":384,"published_at":388,"category":11,"cover_url":8,"hotness":12,"is_selected":38,"score":389,"score_detail":390,"sources":396,"tags":398,"primary_topic":400,"secondary_topics":401,"classification":402,"search_phrases":428,"slug":431,"view_count":419,"doi":432,"paper":433,"created_at":459},5382,"Modeling the global potential of smart farming innovations","https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.2530081123","Smart farming innovations can improve agricultural productivity, resilience, and environmental sustainability, yet their real-world impact hinges on their diffusion dynamics, which in turn depend on their economic viability. We present here a global assessment of the profitability of smart farming innovation across croplands at a spatial resolution of ~5 km from the farmers’ and the societal perspectives. We cover application mapping for precision agriculture, automated application of inputs, autonomous weeding machines, GPS steering assistance systems, and autonomous irrigation systems. For this, we compiled a globally comprehensive dataset of smart farming innovation adoption locations (N = 4,422) based on a combination of a survey (N = 2,677) and a meta-analysis (N = 1,745) and combined them with remotely sensed and statistical data to train an ensemble of five geospatial machine learning models (generalized linear models, random forests, support vector machines, maximum entropy, and boosted regression trees), which show the global distribution of the different innovations’ profitability and its overlap with major environmental issues the innovations can help to mitigate. Simulations of future socioeconomic pathways combined with assumptions about future agri-environmental policies reveal that, especially in middle-income countries, the potential of smart farming will grow over time.","智慧农业创新能够提升农业生产力、韧性和环境可持续性，然而其现实影响取决于扩散动态，而扩散动态又取决于其经济可行性。本文从农民和社会两个视角，以约5公里的空间分辨率对全球农田范围内智慧农业创新的盈利能力进行了评估。研究涵盖精准农业的应用测绘、投入品的自动化施用、自主除草机械、GPS转向辅助系统以及自主灌溉系统。为此，我们基于一项调查（N = 2,677）和一项元分析（N = 1,745）相结合，编制了全球范围内较为全面的智慧农业创新采用地点数据集（N = 4,422），并将其与遥感和统计数据相结合，用于训练由五种地理空间机器学习模型（广义线性模型、随机森林、支持向量机、最大熵和提升回归树）组成的集成模型，这些模型揭示了不同创新盈利能力的全球分布及其与这些创新有助于缓解的主要环境问题的重叠情况。对未来社会经济路径的模拟结合对未来农业环境政策的假设表明，智慧农业的潜力将随时间推移而增长，尤其是在中等收入国家。","Proceedings of the National Academy of Sciences","2026-10-19T00:00:00Z",93,{"impact":391,"substance":392,"depth":393,"authority":394,"freshness":108,"relevant":19,"comment":395},26,24,19,15,"PNAS 全球尺度智慧农业盈利性评估，数据规模与方法新颖，结论对智慧农业推广具政策参考价值，值得进入每日精选。",[397],{"name":387,"url":384},[24,113,25,325,399],"全球评估",{"code":52,"slug":117,"name":118,"path":119,"parent_code":120},[],{"status":133,"locked":13,"version":31,"method":32,"evidence":403},{"candidates":404,"agricultural_evidence":38},[405,410,414,418,423],{"code":36,"score":406,"supported":38,"title":407,"abstract":408,"tags":409},11,[],[24,25,42],[24,25],{"code":52,"score":165,"supported":38,"title":411,"abstract":412,"tags":413},[],[141,55],[113],{"code":127,"score":94,"supported":38,"title":415,"abstract":416,"tags":417},[],[160,128],[],{"code":322,"score":419,"supported":38,"title":420,"abstract":421,"tags":422},4,[],[325],[325],{"code":288,"score":46,"supported":38,"title":424,"abstract":425,"tags":427},[],[426],"除草",[],[429,430],"农业人工智能 全球评估 智慧农业 精准农业","农业人工智能 全球评估","农业人工智能全球评估智慧农业精准农业-5382","10.1073\u002Fpnas.2530081123",{"doi":432,"openalex_id":434,"authors":435,"venue":387,"cited_by_count":82,"oa_url":384,"card":454,"direction":90,"ingested_from":91},"W7222974188",[436,439,442,445,448,451],{"name":437,"orcid":438},"Shahrear Roman","https:\u002F\u002Forcid.org\u002F0009-0006-5659-4292",{"name":440,"orcid":441},"Hadi Hadi","https:\u002F\u002Forcid.org\u002F0000-0002-2410-9751",{"name":443,"orcid":444},"Wyclife Agumba Oluoch","https:\u002F\u002Forcid.org\u002F0000-0003-0803-9279",{"name":446,"orcid":447},"David Zilberman","https:\u002F\u002Forcid.org\u002F0000-0001-5743-5134",{"name":449,"orcid":450},"Robert Finger","https:\u002F\u002Forcid.org\u002F0000-0002-0634-5742",{"name":452,"orcid":453},"David Wuepper","https:\u002F\u002Forcid.org\u002F0000-0002-1344-6023",{"tldr":455,"method":456,"finding":457,"direction":90,"opportunity":458},"全球5公里尺度评估五类智慧农业创新在农田的盈利性与扩散潜力。","整合4422个采用点调查与元分析数据，用五种地理空间机器学习集成建模。","智慧农业盈利性空间差异显著，中等收入国家未来潜力将随社会经济路径增长。","可结合区域政策与农户异质性，细化盈利性预测并评估环境协同效益。","2026-10-20T23:30:08.754594Z",{"id":461,"title":462,"url":463,"summary":464,"summary_zh":8,"content":8,"source_name":465,"source_url":463,"published_at":388,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":466,"score_detail":467,"sources":470,"tags":472,"primary_topic":475,"secondary_topics":476,"classification":477,"search_phrases":489,"slug":492,"view_count":82,"doi":493,"paper":494,"created_at":499},5380,"Automation, Robotics, and Precision Agriculture Transformation Through Smart Harvest Systems","https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.17987.98089","Automation, Robotics, and Precision Agriculture Transformation Through Smart Harvest Systems。OpenAlex","OpenAlex",56,{"impact":16,"substance":278,"depth":12,"authority":468,"freshness":278,"relevant":19,"comment":469},12,"主题契合智慧农业，但摘要仅为标题重复、缺乏方法与数据细节，信息增量有限，暂不建议进入每日精选。",[471],{"name":465,"url":463},[24,473,25,474],"农业机器人","智能采收",{"code":36,"slug":231,"name":232,"path":233,"parent_code":120},[],{"status":133,"locked":13,"version":31,"method":32,"evidence":478},{"candidates":479,"agricultural_evidence":38},[480,484],{"code":36,"score":107,"supported":38,"title":481,"abstract":482,"tags":483},[42],[42],[24,25],{"code":485,"score":19,"supported":13,"title":486,"abstract":487,"tags":488},"F6",[],[],[473],[490,491],"农业机器人 智慧农业 智能采收 精准农业","农业机器人 智慧农业","农业机器人智慧农业智能采收精准农业-5380","10.13140\u002Frg.2.2.17987.98089",{"doi":493,"openalex_id":495,"authors":496,"venue":8,"cited_by_count":82,"oa_url":463,"card":8,"direction":90,"ingested_from":91},"W7222971426",[497],{"name":498,"orcid":8},"Joydeep Dey","2026-10-20T23:30:08.600540Z",{"id":501,"title":502,"url":503,"summary":504,"summary_zh":505,"content":8,"source_name":506,"source_url":503,"published_at":388,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":507,"score_detail":508,"sources":510,"tags":512,"primary_topic":515,"secondary_topics":516,"classification":518,"search_phrases":538,"slug":541,"view_count":82,"doi":542,"paper":543,"created_at":561},5378,"IoT-Enabled Smart Irrigation Using ESP32 and LoRa for Precision Water Management in Connectivity-Limited Regions","https:\u002F\u002Fdoi.org\u002F10.3390\u002Felectronics15204831","In this paper, a new method for irrigation systems in agriculture has been reported. This work aims to design and implement a smart irrigation system based on the ESP32 platform and Internet of Things (IoT) technologies for optimizing water usage management. With the rapid growth of Internet of Things (IoT) technologies, where devices are connected and can be controlled through the internet, it is now feasible to design smart irrigation systems based on real-time environmental data to deliver optimal water use with minimal human intervention. In Sahara areas, where communication networks and internet connections are lacking, alternative communication protocols are necessary for data transfer. LoRa technology based on the SX1278 transceiver provides long-range, low-power communication that can achieve multi-kilometer ranges (up to 10–15 km in rural areas) under ideal conditions: line-of-sight, high antenna height, low data rate, and open rural environments between distributed sensor nodes and a central gateway. However, this theoretical range cannot be assumed in practice. Field tests under standard farm deployment conditions (ground-level placement and vegetation interference) showed that the link was lost at approximately 120 m, which validates the system at the scale of a field plot. This project used Arduino (version 2.3.7) software to program the NodeMCU (ESP32) board to link the board with the Blynk cloud application. The project is used to monitor the temperature, humidity, and soil moisture and do the command for the irrigation system, with master and slave cards using LoRa technology as a communication tool to replace communication systems like WiFi or cellular networks in deserted or remote areas, which might not offer sufficient coverage or could be too expensive to build. To address this challenge, this paper presents the design and implementation of a smart irrigation system based on the ESP32 microcontroller and LoRa (SX1278) technology, with the long-term objective of helping in the modernization of traditional agriculture and environmental sustainability. Extending the coverage to larger fields and Sahara areas would require elevated antennas, optimized LoRa parameters (spreading factor and bandwidth), multi-hop relay nodes, or multiple gateways; these strategies are proposed as future work.","本文报道了一种农业灌溉系统的新方法。本工作旨在设计和实现一种基于ESP32平台和物联网（Internet of Things, IoT）技术的智能灌溉系统，以优化用水管理。随着物联网技术的快速发展，设备得以互联并可通过互联网进行控制，如今基于实时环境数据设计智能灌溉系统，以在最少人工干预下实现最优用水已成为可能。在撒哈拉地区，由于缺乏通信网络和互联网连接，数据传输需要采用替代通信协议。基于SX1278收发器的LoRa技术可提供远距离、低功耗通信，在理想条件下（视距传输、天线高度较高、低数据速率以及分布式传感器节点与中心网关之间开阔的农村环境），其通信距离可达数公里（农村地区可达10–15 km）。然而，这一理论距离在实际中并不能假定成立。在标准农田部署条件下（地面放置和植被干扰）进行的现场测试表明，链路在约120 m处即发生中断，这验证了系统在田块尺度上的可行性。本项目使用Arduino（版本2.3.7）软件对NodeMCU（ESP32）开发板进行编程，将开发板与Blynk云应用相连接。该项目用于监测温度、湿度和土壤水分，并向灌溉系统发送指令，主从卡采用LoRa技术作为通信工具，以替代WiFi或蜂窝网络等通信系统——这些系统在荒漠或偏远地区可能覆盖不足，或建设成本过高。为应对这一挑战，本文提出了基于ESP32微控制器和LoRa（SX1278）技术的智能灌溉系统的设计与实现，其长期目标是助力传统农业的现代化和环境可持续性。要将覆盖范围扩展至更大田块和撒哈拉地区，需要架高天线、优化LoRa参数（扩频因子和带宽）、采用多跳中继节点或部署多个网关；这些策略被提出作为未来工作。","Electronics",67,{"impact":468,"substance":16,"depth":37,"authority":107,"freshness":278,"relevant":19,"comment":509},"面向无网络覆盖地区的ESP32+LoRa智能灌溉系统，含实地链路测试数据，方法有参考价值但属细分技术进展，未达每日精选门槛。",[511],{"name":506,"url":503},[24,114,25,513,514],"节水灌溉","LoRa",{"code":58,"slug":123,"name":124,"path":125,"parent_code":120},[517],{"code":127,"slug":128,"name":129,"path":130,"parent_code":131},{"status":133,"locked":13,"version":31,"method":32,"evidence":519},{"candidates":520,"agricultural_evidence":38},[521,526,530,534],{"code":58,"score":16,"supported":38,"title":522,"abstract":523,"tags":525},[154],[114,524,154],"internet of things",[114],{"code":127,"score":394,"supported":38,"title":527,"abstract":528,"tags":529},[128],[160,128],[513],{"code":45,"score":94,"supported":38,"title":531,"abstract":532,"tags":533},[],[174,49],[],{"code":36,"score":18,"supported":13,"title":535,"abstract":536,"tags":537},[],[],[24,25],[539,540],"ESP32 LoRa 智能灌溉","SX1278 精准灌溉","ESP32LoRa智能灌溉-5378","10.3390\u002Felectronics15204831",{"doi":542,"openalex_id":544,"authors":545,"venue":506,"cited_by_count":82,"oa_url":503,"card":556,"direction":90,"ingested_from":91},"W7222959710",[546,548,550,553],{"name":547,"orcid":8},"Hanfoug Salah",{"name":549,"orcid":8},"Riad Saidi",{"name":551,"orcid":552},"Abdelaziz Aouiche","https:\u002F\u002Forcid.org\u002F0000-0003-1378-7941",{"name":554,"orcid":555},"Yazeed Alkhrijah","https:\u002F\u002Forcid.org\u002F0000-0001-7352-003X",{"tldr":557,"method":558,"finding":559,"direction":90,"opportunity":560},"基于ESP32与LoRa构建智能灌溉系统，在无网络覆盖区实现精准用水管理。","ESP32采集温湿度和土壤水分，经LoRa(SX1278)传输至网关并接入Bly","实际农田条件下LoRa链路仅约120米即中断，远低于理论公里级。","可研究多跳中继、天线高度与扩频参数优化，提升偏远农田LoRa组网覆盖与可靠性。","2026-10-20T23:30:08.464791Z"]