[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3773":3,"related-3773":111},{"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":31,"secondary_topics":37,"classification":38,"search_phrases":78,"slug":81,"view_count":68,"doi":82,"paper":83,"created_at":110},3773,"Precision seed placement to advanced seed delivery: evolution, innovations, and future perspectives of seed tape systems for climate-smart agriculture","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffagro.2026.1952898","Seed placement is a critical determinant of crop establishment, resource use efficiency, and agricultural productivity. Traditional sowing methods are associated with poor seed uniformity, high sowing rates, and high labor requirements, creating a need for more sustainable sowing technologies. Seed tape technology has been demonstrated as a precision seed placement approach, particularly through paper, cellulose, natural fiber, and biodegradable polymer-based carriers. This review highlights the evolution of seed tapes from these established precision placement systems toward multifunctional and potentially intelligent seed delivery platforms for climate-smart agriculture. The review demonstrates technologies from emerging and future concepts, including established applications such as biodegradable carrier materials, precision seed placement, and mechanized seed tape systems. Emerging developments include functionalized and selected nano-enabled seed tapes incorporating hydrogels, nutrients, biological inputs, and nanomaterials, as well as smart sensing, AI, IoT, robotics, autonomous deployment, and real-time digital decision support. A technology-readiness framework is used to evaluate the maturity, agronomic relevance, sustainability potential, and commercialization prospects of these systems. Major challenges include manufacturing scale-up, formulation stability, regulatory requirements, economic viability, and field validation of advanced systems. Future research should focus on validating multifunctional materials and biological inputs, improving nano-enabled formulations, and developing digital and autonomous technologies for intelligent seed delivery. The transition from demonstrated precision seed placement to intelligent seed delivery should therefore be viewed as a technology-development trajectory rather than as a currently established integrated system. This review provides a perspective on the evolution of seed tape technology toward multifunctional and potentially intelligent seed delivery for sustainable and climate-smart crop production.","种子位置是决定作物定植、资源利用效率和农业生产力的关键因素。传统播种方式存在种子均匀度差、播种率高和劳动力需求大等问题，因此需要更可持续的播种技术。种子带技术已被证明是一种精准播种方法，尤其是通过纸、纤维素、天然纤维和可生物降解聚合物基载体实现。本文综述了种子带从这些已建立的精准放置系统向多功能乃至潜在智能化种子递送平台的发展历程，以服务于气候智慧型农业。综述展示了新兴和未来概念中的技术，包括已确立的应用，如可生物降解载体材料、精准种子放置和机械化种子带系统。新兴发展包括功能化和精选的纳米赋能种子带，其整合了水凝胶、养分、生物投入品和纳米材料，以及智能传感、人工智能、物联网、机器人技术、自主部署和实时数字决策支持。本文采用技术成熟度框架评估这些系统的成熟度、农艺相关性、可持续性潜力和商业化前景。主要挑战包括制造规模化、配方稳定性、监管要求、经济可行性以及先进系统的田间验证。未来研究应侧重于验证多功能材料和生物投入品、改进纳米赋能配方，以及开发用于智能种子递送的数字化和自主技术。因此，从已证明的精准种子放置向智能种子递送的转变应被视为一个技术发展轨迹，而非当前已建立的集成系统。本文综述了种子带技术向多功能乃至潜在智能化种子递送演进的观点，以服务于可持续和气候智慧型作物生产。",null,"Frontiers in Agronomy","2026-09-28T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,9,1,"综述系统梳理种子带从精准排种到智能种子输送的技术演进，提出技术成熟度评估框架，对气候智慧型农业与智能播种装备研发有参考价值，但属概念性综述、缺乏一手数据，未达重大突破层级。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","智能农机","气候智慧农业","精准播种","种子带",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":47},[44,52,59,66,73],{"code":45,"score":46,"supported":47,"title":48,"abstract":50,"tags":51},"I1",12,true,[49],"smart agriculture",[49],[26],{"code":32,"score":53,"supported":47,"title":54,"abstract":55,"tags":58},6,[],[56,57],"物联网","IoT",[],{"code":60,"score":61,"supported":47,"title":62,"abstract":63,"tags":65},"F5",4,[],[64],"机械化",[27],{"code":67,"score":68,"supported":47,"title":69,"abstract":70,"tags":72},"I2",3,[],[71],"decision support",[],{"code":74,"score":21,"supported":14,"title":75,"abstract":76,"tags":77},"H7",[],[],[28],[79,80],"种子带 精准播种 智能种子输送","气候智慧农业 智慧农业 智能农机 精准播种","种子带精准播种智能种子输送-3773","10.3389\u002Ffagro.2026.1952898",{"doi":82,"openalex_id":84,"authors":85,"venue":10,"cited_by_count":102,"oa_url":6,"card":103,"direction":107,"ingested_from":109},"W7214664548",[86,89,92,95,97,99],{"name":87,"orcid":88},"Sourav Paramanik","https:\u002F\u002Forcid.org\u002F0009-0001-0628-4337",{"name":90,"orcid":91},"Poonguzhali Subramanian","https:\u002F\u002Forcid.org\u002F0000-0002-4282-6148",{"name":93,"orcid":94},"Mahalakshmi Mani","https:\u002F\u002Forcid.org\u002F0009-0001-6257-0928",{"name":96,"orcid":9},"Jothika Manokaran",{"name":98,"orcid":9},"Divya Nagarajan",{"name":100,"orcid":101},"Karthika Rajendran","https:\u002F\u002Forcid.org\u002F0000-0003-4175-2463",0,{"tldr":104,"method":105,"finding":106,"direction":107,"opportunity":108},"综述种子带技术从精准排种向多功能智能播种平台演进的路径与前景。","文献综述，结合技术成熟度框架评估材料、智能感知与自主播种技术。","种子带正从精准排种向集成纳米材料、AI与物联网的智能播种系统演进，但尚处技术开发阶段。","智慧农业 \u002F 农业物联网","可研究可降解种子带与AI\u002F物联网融合的智能播种系统，并开展田间验证与商业化评估。","openalex","2026-09-29T23:30:10.437986Z",{"total":53,"page":21,"page_size":53,"items":112},[113,197,308,361,417,507],{"id":114,"title":115,"url":116,"summary":117,"summary_zh":118,"content":9,"source_name":119,"source_url":116,"published_at":120,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":121,"score_detail":122,"sources":126,"tags":128,"primary_topic":132,"secondary_topics":138,"classification":144,"search_phrases":176,"slug":179,"view_count":102,"doi":9,"paper":180,"created_at":196},4670,"SMART FERTILIZER DISTRIBUTION SYSTEM: A COMPREHENSIVE REVIEW","https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F334","Agriculture is an important sector for food production and economic development, and the efficient use of fertilizers is essential for achieving better crop growth and productivity. Conventional fertilizer distribution methods largely depend on manual application, which can lead to uneven distribution, excessive fertilizer usage, increased labor requirements, and wastage of agricultural resources. The development of automation and Internet of Things (IoT) technologies has provided new opportunities for improving fertilizer distribution and management in agricultural fields. This review paper presents a comprehensive study of Smart Fertilizer Distribution Systems, focusing on the technologies, control methods, communication techniques, and approaches reported in existing research. Various systems using microcontrollers, IoT devices, wireless communication, web-based platforms, sensors, motor or pump control, driver circuits, and solenoid valves are reviewed. The working principles of automated fertilizer distribution systems and their role in controlling fertilizer flow and reducing manual intervention are discussed. Different communication technologies, including Wi-Fi and other wireless methods, are compared based on their applicability, cost, operating range, reliability, and control capability. The review also examines the major advantages and limitations of existing systems, such as automation, remote operation, improved fertilizer management, reduced labor, network dependency, power requirements, sensor accuracy, and system complexity. A comparative analysis of selected research works is presented to identify the techniques used, their contributions, and the limitations that remain to be addressed. The review further identifies research gaps and discusses future developments such as automatic fertilizer scheduling, multi-zone fertilizer distribution, real-time monitoring, cloud-based data storage, artificial intelligence, machine learning, and integration with renewable energy sources. The study concludes that smart fertilizer distribution can improve the efficiency, accuracy, and sustainability of fertilizer application and has significant potential for future precision agriculture applications.","农业是粮食生产和经济发展的重要部门，而肥料的高效利用对于实现作物更好生长和生产力提升至关重要。传统的肥料分配方法在很大程度上依赖人工施用，这可能导致分配不均、肥料过量使用、劳动力需求增加以及农业资源浪费。自动化和物联网（IoT）技术的发展为改善农田肥料分配与管理提供了新的机遇。本文综述对智能肥料分配系统进行了全面研究，重点关注现有研究中报道的技术、控制方法、通信技术和方法。本文回顾了使用微控制器、物联网设备、无线通信、基于网络的平台、传感器、电机或泵控制、驱动电路和电磁阀的各种系统。讨论了自动化肥料分配系统的工作原理及其在控制肥料流量和减少人工干预方面的作用。基于适用性、成本、工作范围、可靠性和控制能力，比较了包括Wi-Fi和其他无线方法在内的不同通信技术。本综述还考察了现有系统的主要优势和局限性，如自动化、远程操作、改善肥料管理、减少劳动力、网络依赖、功率需求、传感器精度和系统复杂性。本文对选定的研究工作进行了比较分析，以识别所采用的技术、其贡献以及仍有待解决的局限性。本综述进一步指出了研究空白，并讨论了未来发展方向，如自动肥料调度、多区域肥料分配、实时监测、基于云的数据存储、人工智能、机器学习以及与可再生能源的集成。该研究得出结论，智能肥料分配可以提高肥料施用的效率、准确性和可持续性，并在未来精准农业应用中具有巨大潜力。","International Journal of Mechanical Engineering Research and Technology","2026-10-08T00:00:00Z",61,{"impact":46,"substance":123,"depth":124,"authority":46,"freshness":53,"relevant":21,"comment":125},16,15,"综述性论文系统梳理智能施肥系统的技术路线与研究空白，对智慧农业领域有参考价值，但属文献综述而非原创突破，时效性一般。",[127],{"name":119,"url":116},[26,129,130,131,27],"农业人工智能","精准施肥","农业物联网",{"code":133,"slug":134,"name":135,"path":136,"parent_code":137},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",[139,143],{"code":67,"slug":140,"name":141,"path":142,"parent_code":36},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":145},{"candidates":146,"agricultural_evidence":47},[147,153,160,165,172],{"code":133,"score":124,"supported":47,"title":148,"abstract":150,"tags":152},[149],"fertilizer",[151,149],"肥料",[130],{"code":67,"score":13,"supported":47,"title":154,"abstract":155,"tags":159},[],[156,157,158],"机器学习","machine learning","artificial intelligence",[129],{"code":32,"score":13,"supported":47,"title":161,"abstract":162,"tags":164},[],[56,163,57],"internet of things",[131],{"code":45,"score":166,"supported":47,"title":167,"abstract":168,"tags":171},7,[],[169,170],"精准农业","precision agriculture",[26],{"code":60,"score":21,"supported":14,"title":173,"abstract":174,"tags":175},[],[],[27],[177,178],"智能施肥系统 物联网","精准施肥 自动化","智能施肥系统物联网-4670",{"doi":9,"openalex_id":181,"authors":182,"venue":119,"cited_by_count":102,"oa_url":116,"card":191,"direction":107,"ingested_from":109},"W7221394563",[183,185,187,189],{"name":184,"orcid":9},"Anjali Manoj Kumbhar",{"name":186,"orcid":9},"Shruti Prashant Kulkarni",{"name":188,"orcid":9},"Snehal Santosh Khune",{"name":190,"orcid":9},"B. B. Godbole",{"tldr":192,"method":193,"finding":194,"direction":107,"opportunity":195},"综述智能施肥系统的技术、控制与通信方法，并指出研究空白与未来方向。","综述基于微控制器、物联网、无线通信、传感器与电磁阀的自动施肥系统研究。","智能施肥可提升施肥效率、精度与可持续性，但存在网络依赖、功耗与传感器精度等局限。","可探索AI驱动的自动施肥调度、多区域分配与云边协同的实时精准施肥系统。","2026-10-11T23:30:16.128775Z",{"id":198,"title":199,"url":200,"summary":201,"summary_zh":202,"content":9,"source_name":203,"source_url":200,"published_at":204,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":205,"score_detail":206,"sources":208,"tags":210,"primary_topic":214,"secondary_topics":220,"classification":223,"search_phrases":278,"slug":281,"view_count":102,"doi":282,"paper":283,"created_at":307},5564,"Dynamic thermal imaging for crop water stress detection across phenological stages: Enhancing irrigation scheduling precision","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.17728","Thermal remote sensing and derived indices such as the crop water stress index (CWSI) can assess crop water status and support precision irrigation. However, most operational and artificial intelligence (AI)-based approaches rely on fixed, season-long thresholds to interpret canopy–air temperature differences and often overlook the influence of crop phenology. This review synthesises evidence from grapevine, wheat, maize, potato and several other crops on how developmental stage affects the non-water-stressed baseline (NWSB), CWSI thresholds and irrigation-scheduling algorithms. It covers CWSI theory and estimation practices; crop-specific, phenology-dependent behaviour of baselines and thresholds; a comparison of threshold-based, machine learning, Internet of Things (IoT), digital twin and Food and Agriculture Organization (FAO)-56 approaches with respect to crop phenology; and a conceptual framework for phenology-aware scheduling. Across the reviewed crops, baseline behaviour generally varied systematically across the growing season, the relationship between CWSI and crop water status generally strengthened after canopy closure and CWSI measured during reproductive stages was generally most closely related to yield. A quantitative audit of 56 AI-, thermal-imaging-, or CWSI-based studies found that 78.6 % either did not explicitly incorporate crop phenology or relied on day of year as a temporal proxy, whereas only 5.4 % explicitly encoded phenological stage. The proposed framework considers phenology explicitly in baseline estimation, threshold setting and data fusion or machine learning for irrigation scheduling; the framework will require multi-year, multi-site field validation before operational use. Future research should prioritise standardised phenology definitions and long-term, multi-location studies to establish and validate stage-specific parameters.","热红外遥感及其衍生的作物水分胁迫指数（crop water stress index, CWSI）等指标可评估作物水分状况并支持精准灌溉。然而，大多数业务化方法和基于人工智能（artificial intelligence, AI）的方法依赖固定的全生育期阈值来解释冠层—空气温差，且往往忽视作物物候的影响。本文综述了葡萄、小麦、玉米、马铃薯及其他若干作物在发育阶段如何影响非水分胁迫基线（non-water-stressed baseline, NWSB）、CWSI阈值和灌溉调度算法的相关证据。综述内容涵盖CWSI理论与估算实践；基线和阈值的作物特异性、物候依赖性行为；基于阈值、机器学习、物联网（Internet of Things, IoT）、数字孪生及联合国粮农组织（Food and Agriculture Organization, FAO）-56等方法在作物物候方面的比较；以及物候感知调度的概念框架。在所综述的作物中，基线行为在整个生长季通常呈系统性变化，CWSI与作物水分状况之间的关系在冠层封垄后总体增强，且在生殖阶段测得的CWSI与产量的关系总体最为密切。对56项基于AI、热成像或CWSI的研究进行的定量审查发现，78.6%的研究要么未明确纳入作物物候，要么依赖年积日作为时间代理变量，而仅5.4%的研究明确编码了物候阶段。所提出的框架在基线估算、阈值设定以及用于灌溉调度的数据融合或机器学习中明确考虑物候；该框架在投入业务应用前尚需多年、多站点的田间验证。未来研究应优先建立标准化的物候定义，并开展长期、多地点研究以建立和验证阶段特异性参数。","Plant Science Today","2026-10-21T00:00:00Z",79,{"impact":17,"substance":18,"depth":17,"authority":46,"freshness":20,"relevant":21,"comment":207},"系统综述热成像与CWSI在物候期感知灌溉中的应用，含56项研究量化审计，方法学价值突出，但属综述类论文，影响偏学术层面。",[209],{"name":203,"url":200},[26,129,211,212,213],"遥感","作物水分胁迫","精准灌溉",{"code":215,"slug":216,"name":217,"path":218,"parent_code":219},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[221,222],{"code":67,"slug":140,"name":141,"path":142,"parent_code":36},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":224},{"candidates":225,"agricultural_evidence":47},[226,231,235,239,245,251,258,265,272],{"code":215,"score":124,"supported":47,"title":227,"abstract":228,"tags":230},[216],[229,216],"灌溉",[213],{"code":67,"score":13,"supported":47,"title":232,"abstract":233,"tags":234},[],[156,157,158],[129],{"code":32,"score":20,"supported":47,"title":236,"abstract":237,"tags":238},[],[56,163,57],[],{"code":45,"score":166,"supported":47,"title":240,"abstract":241,"tags":244},[],[242,243],"数字孪生","digital twin",[26],{"code":246,"score":166,"supported":47,"title":247,"abstract":248,"tags":250},"I3",[],[211,249],"remote sensing",[211],{"code":252,"score":53,"supported":47,"title":253,"abstract":254,"tags":257},"A2",[],[255,256],"小麦","wheat",[],{"code":259,"score":53,"supported":47,"title":260,"abstract":261,"tags":264},"A3",[],[262,263],"玉米","maize",[],{"code":266,"score":53,"supported":47,"title":267,"abstract":268,"tags":271},"A5",[],[269,270],"马铃薯","potato",[],{"code":273,"score":68,"supported":47,"title":274,"abstract":275,"tags":277},"B2",[],[276],"葡萄",[],[279,280],"热成像 作物水分胁迫","CWSI 灌溉调度","热成像作物水分胁迫-5564","10.14719\u002Fpst.17728",{"doi":282,"openalex_id":284,"authors":285,"venue":203,"cited_by_count":102,"oa_url":200,"card":301,"direction":107,"ingested_from":109},"W7223088921",[286,289,292,295,298],{"name":287,"orcid":288},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":290,"orcid":291},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":293,"orcid":294},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":296,"orcid":297},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":299,"orcid":300},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":302,"method":303,"finding":304,"direction":305,"opportunity":306},"综述物候期对热成像CWSI阈值的影响，提出物候感知灌溉调度框架。","综述葡萄、小麦、玉米等作物的热遥感CWSI研究，并审计56项AI\u002F热成像研究。","78.6%研究未显式纳入物候，仅5.4%编码物候期；CWSI与产量在生殖期关联最强。","农业遥感与作物表型","可开展多站点多年物候期特异性CWSI阈值标定与标准化物候定义研究。","2026-10-22T11:20:24.526683Z",{"id":309,"title":310,"url":311,"summary":312,"summary_zh":313,"content":9,"source_name":314,"source_url":311,"published_at":204,"category":12,"cover_url":9,"hotness":315,"is_selected":14,"score":102,"score_detail":316,"sources":318,"tags":322,"primary_topic":326,"secondary_topics":327,"classification":328,"search_phrases":345,"slug":348,"view_count":102,"doi":349,"paper":350,"created_at":360},5555,"IoT Security Challenges and Solutions: Threats, Standards and Layered Protection","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23542095","The Internet of Things (IoT) has become an important part of modern life. Smart homes, wearable devices, healthcare systems, industrial machines, and smart agriculture use connected devices to collect and exchange information. Although IoT makes many activities easier, it also introduces security and privacy risks. Weak passwords, insecure communication, outdated firmware, unauthorized access, and data leakage can expose devices and users to cyberattacks. This paper examines major security challenges faced by IoT systems and discusses practical solutions based on established cybersecurity guidance. It synthesizes three complementary perspectives: academic survey research on IoT security, the National Institute of Standards and Technology (NIST) IoT security baseline, and the OWASP Internet of Things security guidance. The study explains common threats, reviews relevant standards, and proposes a layered protection model covering devices, networks, applications, data, and system management. It concludes that IoT security should be considered throughout the device lifecycle rather than added only after a problem occurs","物联网（IoT）已成为现代生活的重要组成部分。智能家居、可穿戴设备、医疗保健系统、工业机械和智慧农业均利用互联设备采集和交换信息。尽管物联网使许多活动更加便捷，但也带来了安全与隐私风险。弱密码、不安全的通信、过时的固件、未经授权的访问以及数据泄露，都可能使设备和用户暴露于网络攻击之中。本文考察了物联网系统面临的主要安全挑战，并基于既有的网络安全指南讨论了切实可行的解决方案。研究综合了三个互补视角：关于物联网安全的学术调查研究、美国国家标准与技术研究院（NIST）物联网安全基线，以及OWASP物联网安全指南。本研究阐释了常见威胁，梳理了相关标准，并提出了一种涵盖设备、网络、应用、数据和系统管理的分层防护模型。研究结论认为，物联网安全应在设备全生命周期中加以考虑，而非仅在问题出现后才予以补加。","Zenodo (CERN European Organization for Nuclear Research)",25,{"impact":102,"substance":102,"depth":102,"authority":102,"freshness":102,"relevant":102,"comment":317},"通用物联网安全综述，仅泛泛提及智慧农业，与三农及农业信息化主题关联薄弱，不建议入选。",[319,320],{"name":314,"url":311},{"name":314,"url":321},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23541648",[26,323,324,325],"网络安全","物联网安全","NIST标准",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":329},{"candidates":330,"agricultural_evidence":47},[331,336,340],{"code":32,"score":332,"supported":47,"title":333,"abstract":334,"tags":335},17,[57],[56,163,57],[],{"code":45,"score":166,"supported":47,"title":337,"abstract":338,"tags":339},[],[26,49],[26],{"code":341,"score":21,"supported":14,"title":342,"abstract":343,"tags":344},"I6",[],[],[323],[346,347],"物联网安全 智慧农业 网络安全 NIST标准","物联网安全 智慧农业","物联网安全智慧农业网络安全NIST标准-5555","10.5281\u002Fzenodo.23542095",{"doi":349,"openalex_id":351,"authors":352,"venue":314,"cited_by_count":102,"oa_url":311,"card":355,"direction":107,"ingested_from":109},"W7223144955",[353],{"name":354,"orcid":9},"himesh suryawanshi",{"tldr":356,"method":357,"finding":358,"direction":107,"opportunity":359},"综述物联网安全挑战，结合学术与NIST、OWASP标准提出分层防护模型。","综合学术综述、NIST物联网安全基线与OWASP指南，构建分层防护模型。","物联网安全需覆盖设备全生命周期，而非事后补救。","可将通用分层防护模型落地到农业物联网场景，研究低成本设备与田间网络的轻量安全方案。","2026-10-22T11:20:18.423667Z",{"id":362,"title":363,"url":364,"summary":365,"summary_zh":366,"content":9,"source_name":314,"source_url":364,"published_at":204,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":367,"score_detail":368,"sources":371,"tags":373,"primary_topic":376,"secondary_topics":377,"classification":378,"search_phrases":401,"slug":404,"view_count":102,"doi":405,"paper":406,"created_at":416},5554,"Smart Agriculture using IoT","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531185","The Smart Agriculture Using IoT system uses different sensors installed in the agricultural field to collect data about soil and environmental conditions. The collected data is sent to a microcontroller, such as an ESP32 or Arduino with a suitable communication module. When the soil moisture level becomes lower than the required level, the system can automatically switch on the water pump. When sufficient moisture is detected, the pump switches off. Farmers can also monitor field conditions through a mobile application or web dashboard. This system reduces manual work and helps farmers use water and other resources efficiently.","基于物联网的智慧农业系统利用安装在农田中的各类传感器采集土壤与环境状况数据。采集到的数据被传送至微控制器，如ESP32或配备合适通信模块的Arduino。当土壤湿度低于所需水平时，系统可自动开启水泵；当检测到湿度充足时，水泵则关闭。农民还可通过移动应用程序或网页仪表盘监测田间状况。该系统减少了人工劳作，帮助农民高效利用水资源及其他资源。",49,{"impact":369,"substance":46,"depth":13,"authority":20,"freshness":13,"relevant":21,"comment":370},8,"论文介绍基于ESP32\u002FArduino的土壤湿度自动灌溉与远程监测方案，属常规技术实现，方法新颖性有限，但主题贴合智慧农业且时效性强，可作为一般资讯收录。",[372],{"name":314,"url":364},[26,131,374,375],"智能灌溉","土壤墒情",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":379},{"candidates":380,"agricultural_evidence":47},[381,385,389,396],{"code":45,"score":124,"supported":47,"title":382,"abstract":383,"tags":384},[49],[26,49],[26],{"code":32,"score":124,"supported":47,"title":386,"abstract":387,"tags":388},[57],[56,57],[131],{"code":390,"score":166,"supported":47,"title":391,"abstract":392,"tags":395},"H2",[],[393,394],"土壤","soil",[375],{"code":215,"score":61,"supported":47,"title":397,"abstract":398,"tags":400},[],[399],"水资源",[374],[402,403],"ESP32 土壤湿度 自动灌溉","农业物联网 移动端监测","ESP32土壤湿度自动灌溉-5554","10.5281\u002Fzenodo.23531185",{"doi":405,"openalex_id":407,"authors":408,"venue":314,"cited_by_count":102,"oa_url":364,"card":411,"direction":107,"ingested_from":109},"W7223137846",[409],{"name":410,"orcid":9},"Prachi Khairnar",{"tldr":412,"method":413,"finding":414,"direction":107,"opportunity":415},"基于物联网传感器实现农田土壤湿度自动监测与灌溉控制。","部署土壤与环境传感器，经ESP32\u002FArduino采集数据并联动水泵。","系统可自动启停水泵并通过手机或网页端远程监控，节水省工。","可引入多源传感融合与预测性灌溉决策，弥补仅靠阈值控制的局限。","2026-10-22T11:20:18.299106Z",{"id":418,"title":419,"url":420,"summary":421,"summary_zh":422,"content":9,"source_name":423,"source_url":420,"published_at":204,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":424,"score_detail":425,"sources":428,"tags":430,"primary_topic":432,"secondary_topics":433,"classification":436,"search_phrases":482,"slug":485,"view_count":102,"doi":486,"paper":487,"created_at":506},5553,"Artificial Intelligence–Driven Precision Agriculture Systems: Engineering Frameworks for Climate-Resilient Food Production","https:\u002F\u002Fdoi.org\u002F10.53982\u002Fajerd.2026.01.14-j","Climate change, soil issues, water scarcity, and the increasing need for food worldwide mean we need smart farming systems that can manage resources flexibly. Old farming methods used the same inputs and simple rules for decisions. AI can help us manage resources better by adapting and optimizing them as we deal with complex environmental factors. This paper looks closely at AI-powered precision agriculture systems designed to make food production more resilient to changing weather. The authors reviewed research from 2021 to 2026 in agricultural engineering, computer engineering, and environmental systems. The authors examined how machine learning, deep learning, reinforcement learning, computer vision, robotics, and IoT sensors are used for predicting crop yields, spotting diseases, improving irrigation, managing nutrients, and automating field tasks. Our analysis shows that hybrid deep learning models (CNN-LSTM) are better than single models for predicting crop yields over space and time. Reinforcement learning algorithms are also better at adjusting irrigation cycles when weather is unpredictable. Using edge computing for system design reduces delays and improves real-time decision-making compared to traditional cloud-based systems. However, there are still big challenges with understanding how AI models work, having enough diverse data, ensuring cybersecurity, setting common standards, and making these systems affordable, especially in developing countries. To tackle these problems, we suggest a framework that combines AI analysis, distributed sensor networks, adaptive controls, and climate risk assessments. The authors provided critical insights into essential research gaps and outline a technological roadmap towards achieving sustainable, resilient and intelligent agricultural systems.","气候变化、土壤问题、水资源短缺以及全球日益增长的粮食需求，意味着我们需要能够灵活管理资源的智能农业系统。传统农业方法对相同的投入采用固定模式，并依赖简单规则进行决策。人工智能可以帮助我们更好地管理资源，通过适应和优化资源利用来应对复杂的环境因素。本文深入探讨了人工智能驱动的精准农业系统，旨在使粮食生产对不断变化的天气更具韧性。作者综述了2021年至2026年间农业工程、计算机工程和环境系统领域的研究。作者考察了机器学习、深度学习、强化学习、计算机视觉、机器人技术及物联网传感器如何被用于作物产量预测、病害识别、灌溉优化、养分管理和田间作业自动化。我们的分析表明，混合深度学习模型（CNN-LSTM）在时空作物产量预测方面优于单一模型。在天气不可预测的情况下，强化学习算法在调整灌溉周期方面也表现更优。与传统基于云的系统相比，采用边缘计算进行系统设计可降低延迟并改善实时决策能力。然而，在理解人工智能模型的工作原理、获取足够多样化的数据、确保网络安全、制定通用标准以及使这些系统具备可负担性方面，仍存在重大挑战，尤其是在发展中国家。为应对这些问题，我们提出了一个融合人工智能分析、分布式传感器网络、自适应控制和气候风险评估的框架。作者对关键研究空白提供了重要见解，并勾勒了实现可持续、韧性和智能农业系统的技术路线图。","ABUAD Journal of Engineering Research and Development (AJERD)",80,{"impact":17,"substance":18,"depth":17,"authority":426,"freshness":20,"relevant":21,"comment":427},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。",[429],{"name":423,"url":420},[26,129,56,169,431],"气候韧性",{"code":67,"slug":140,"name":141,"path":142,"parent_code":36},[434,435],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":215,"slug":216,"name":217,"path":218,"parent_code":219},{"status":39,"locked":14,"version":40,"method":41,"evidence":437},{"candidates":438,"agricultural_evidence":47},[439,447,451,457,461,467,471,477],{"code":67,"score":17,"supported":47,"title":440,"abstract":441,"tags":446},[158],[442,156,443,157,444,445],"深度学习","计算机视觉","deep learning","computer vision",[129],{"code":45,"score":123,"supported":47,"title":448,"abstract":449,"tags":450},[170],[169,170],[26,169],{"code":32,"score":13,"supported":47,"title":452,"abstract":453,"tags":456},[],[56,454,57,455],"边缘计算","edge computing",[56],{"code":215,"score":20,"supported":47,"title":458,"abstract":459,"tags":460},[],[229,399,216],[],{"code":74,"score":166,"supported":47,"title":462,"abstract":463,"tags":466},[],[464,465],"气候变化","climate change",[431],{"code":390,"score":53,"supported":47,"title":468,"abstract":469,"tags":470},[],[393,394],[],{"code":472,"score":68,"supported":47,"title":473,"abstract":474,"tags":476},"A7",[],[475],"间作",[],{"code":133,"score":68,"supported":47,"title":478,"abstract":479,"tags":481},[],[480],"养分管理",[],[483,484],"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":486,"openalex_id":488,"authors":489,"venue":423,"cited_by_count":102,"oa_url":420,"card":500,"direction":107,"ingested_from":109},"W7223121456",[490,493,495,497],{"name":491,"orcid":492},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":494,"orcid":9},"Oluwadare OYEBODE",{"name":496,"orcid":9},"Iretiayo Adelaiye",{"name":498,"orcid":499},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":501,"method":502,"finding":503,"direction":504,"opportunity":505},"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":508,"title":509,"url":510,"summary":511,"summary_zh":512,"content":9,"source_name":314,"source_url":510,"published_at":513,"category":12,"cover_url":9,"hotness":315,"is_selected":14,"score":514,"score_detail":515,"sources":518,"tags":522,"primary_topic":524,"secondary_topics":525,"classification":527,"search_phrases":557,"slug":560,"view_count":102,"doi":561,"paper":562,"created_at":579},5474,"Real-World Impact: Transforming Agriculture with Technology A Multi-Stakeholder Review of Smart Monitoring, Precision Irrigation, and Digital Value-Chain Integration","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23510500","There's a deliberate and significant transition from input to information. This article combines peer-reviewed evidence and public-sector data on six interrelated technology clusters that are fundamentally changing on-farm and off-farm decision making artificial intelligence (AI) based Farm Management Platforms, Internet of Things (IoT) smart irrigation, aerial and satellite crop monitoring, blockchain-enabled supply-chain digitisation, sustainability and carbon-tracking instruments, and farm data analytics dashboards. In the literature examined, precision tools have been correlated with 15% to 30% gains in crop yields, 30% to 55% of water saved during irrigation, and 10% to 20% of operational costs reduced, all with a range of variability based on the crop, the geographical region, and the size of the farm. It examines the mechanisms of these effects on the one hand and the differential rates of benefit, on the other, between large commercial farms and smallholders, and the policy and infrastructural conditions that drive actual adoption. It also highlights areas of current evidence which are lacking, such as limited long horizon field trials, poor interoperability standards, and lack of social outcome data. The article ends with a discussion on how to turn pilots into lasting, system-level change in food production, both from the government and enterprise side, as well as from the perspective of small and medium producers.","从投入要素到信息的转变是有意为之且意义重大的。本文结合经同行评审的证据与公共部门数据，聚焦六个相互关联、正在从根本上改变农场内外决策方式的技术集群：基于人工智能（AI）的农场管理平台、物联网（IoT）智能灌溉、航空与卫星作物监测、区块链赋能的供应链数字化、可持续性与碳追踪工具，以及农场数据分析仪表盘。在所考察的文献中，精准工具与作物产量提升15%至30%、灌溉用水节约30%至55%、运营成本降低10%至20%相关联，且这些效果因作物、地理区域和农场规模而存在不同程度的差异。本文一方面考察这些效应的作用机制，另一方面分析大型商业农场与小农户之间受益程度的差异，以及驱动实际采用的政策与基础设施条件。文章还指出了当前证据的薄弱之处，如缺乏长期田间试验、互操作性标准不完善以及社会成效数据缺失。文章最后讨论了如何从政府与企业两侧，以及中小生产者视角出发，将试点项目转化为粮食生产中持久、系统层面的变革。","2026-11-25T00:00:00Z",73,{"impact":18,"substance":516,"depth":332,"authority":426,"freshness":102,"relevant":21,"comment":517},21,"多利益相关方综述整合六类技术集群的量化效益与采纳差异，信息增量足、产业参考价值高，但发布于一个月后，时效性不足，暂不宜进入每日精选。",[519,520],{"name":314,"url":510},{"name":314,"url":521},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527988",[26,129,131,523,213],"区块链溯源",{"code":215,"slug":216,"name":217,"path":218,"parent_code":219},[526],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":528},{"candidates":529,"agricultural_evidence":47},[530,534,538,542,548,553],{"code":215,"score":124,"supported":47,"title":531,"abstract":532,"tags":533},[216],[229,216],[213],{"code":32,"score":13,"supported":47,"title":535,"abstract":536,"tags":537},[],[56,163,57],[131],{"code":67,"score":61,"supported":47,"title":539,"abstract":540,"tags":541},[],[158],[129],{"code":543,"score":68,"supported":47,"title":544,"abstract":545,"tags":547},"G2",[],[546],"小农户",[],{"code":549,"score":21,"supported":14,"title":550,"abstract":551,"tags":552},"E4",[],[],[523],{"code":45,"score":21,"supported":14,"title":554,"abstract":555,"tags":556},[],[],[26],[558,559],"Zenodo 智慧农业 多利益相关方综述","精准灌溉 物联网 区块链 供应链","Zenodo智慧农业多利益相关方综述-5474","10.5281\u002Fzenodo.23510500",{"doi":561,"openalex_id":563,"authors":564,"venue":314,"cited_by_count":102,"oa_url":510,"card":573,"direction":107,"ingested_from":109},"W7223034914",[565,568,570],{"name":566,"orcid":567},"A. Shaji George","https:\u002F\u002Forcid.org\u002F0000-0002-8677-3682",{"name":569,"orcid":9},"Dr.T.Baskar",{"name":571,"orcid":572},"P. Balaji Srikaanth","https:\u002F\u002Forcid.org\u002F0000-0003-4717-7367",{"tldr":574,"method":575,"finding":576,"direction":577,"opportunity":578},"综述六大农业数字技术集群对农场决策的变革及实际效益与采纳差异。","综合同行评议证据与公共部门数据，多利益相关方综述六类技术。","精准技术可增产15-30%、节水30-55%、降本10-20%，但小农获益不均。","数字乡村与农业信息化","缺乏长期田间试验、互操作标准与社会效益数据，小农采纳机制是研究空白。","2026-10-21T23:30:08.684798Z"]