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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","论文",false,79,{"impact":361,"substance":321,"depth":361,"authority":416,"freshness":584,"relevant":455,"comment":585},9,"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。",[587],{"name":578,"url":575},[589,590,591,592,593],"智慧农业","农业人工智能","遥感","作物水分胁迫","精准灌溉",{"code":152,"slug":153,"name":154,"path":155,"parent_code":128},[596,597],{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"code":48,"slug":49,"name":50,"path":51,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":601},"classified","rules",{"candidates":602,"agricultural_evidence":605},[603,610,617,624,631,636,641,645,651],{"code":152,"score":604,"supported":605,"title":606,"abstract":607,"tags":609},15,true,[153],[608,153],"灌溉",[593],{"code":24,"score":385,"supported":605,"title":611,"abstract":612,"tags":616},[],[613,614,615],"机器学习","machine learning","artificial intelligence",[590],{"code":48,"score":584,"supported":605,"title":618,"abstract":619,"tags":623},[],[620,621,622],"物联网","internet of things","IoT",[],{"code":16,"score":625,"supported":605,"title":626,"abstract":627,"tags":630},7,[],[628,629],"数字孪生","digital twin",[589],{"code":32,"score":625,"supported":605,"title":632,"abstract":633,"tags":635},[],[591,634],"remote sensing",[591],{"code":216,"score":637,"supported":605,"title":638,"abstract":639,"tags":640},6,[],[218,217],[],{"code":224,"score":637,"supported":605,"title":642,"abstract":643,"tags":644},[],[226,225],[],{"code":240,"score":637,"supported":605,"title":646,"abstract":647,"tags":650},[],[648,649],"马铃薯","potato",[],{"code":286,"score":652,"supported":605,"title":653,"abstract":654,"tags":656},3,[],[655],"葡萄",[],[658,659],"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":661,"openalex_id":663,"authors":664,"venue":578,"cited_by_count":424,"oa_url":575,"card":680,"direction":686,"ingested_from":687},"W7223088921",[665,668,671,674,677],{"name":666,"orcid":667},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":669,"orcid":670},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":672,"orcid":673},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":675,"orcid":676},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":678,"orcid":679},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":681,"method":682,"finding":683,"direction":684,"opportunity":685},"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。","智慧农业 \u002F 农业物联网","openalex","2026-10-22T11:20:24.526683Z",{"id":690,"title":691,"url":692,"summary":693,"summary_zh":694,"content":11,"source_name":695,"source_url":692,"published_at":579,"category":580,"cover_url":11,"hotness":447,"is_selected":581,"score":424,"score_detail":696,"sources":698,"tags":702,"primary_topic":706,"secondary_topics":707,"classification":708,"search_phrases":725,"slug":728,"view_count":424,"doi":729,"paper":730,"created_at":740},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)",{"impact":424,"substance":424,"depth":424,"authority":424,"freshness":424,"relevant":424,"comment":697},"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。",[699,700],{"name":695,"url":692},{"name":695,"url":701},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23541648",[589,703,704,705],"网络安全","物联网安全","NIST标准",{"code":48,"slug":49,"name":50,"path":51,"parent_code":7},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":709},{"candidates":710,"agricultural_evidence":605},[711,716,721],{"code":48,"score":712,"supported":605,"title":713,"abstract":714,"tags":715},17,[622],[620,621,622],[],{"code":16,"score":625,"supported":605,"title":717,"abstract":718,"tags":720},[],[589,719],"smart agriculture",[589],{"code":56,"score":455,"supported":581,"title":722,"abstract":723,"tags":724},[],[],[703],[726,727],"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":729,"openalex_id":731,"authors":732,"venue":695,"cited_by_count":424,"oa_url":692,"card":735,"direction":686,"ingested_from":687},"W7223144955",[733],{"name":734,"orcid":11},"himesh suryawanshi",{"tldr":736,"method":737,"finding":738,"direction":686,"opportunity":739},"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":742,"title":743,"url":744,"summary":745,"summary_zh":746,"content":11,"source_name":695,"source_url":744,"published_at":579,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":747,"score_detail":748,"sources":750,"tags":752,"primary_topic":756,"secondary_topics":757,"classification":758,"search_phrases":779,"slug":782,"view_count":424,"doi":783,"paper":784,"created_at":794},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":173,"substance":416,"depth":385,"authority":584,"freshness":385,"relevant":455,"comment":749},"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。",[751],{"name":695,"url":744},[589,753,754,755],"农业物联网","智能灌溉","土壤墒情",{"code":48,"slug":49,"name":50,"path":51,"parent_code":7},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":759},{"candidates":760,"agricultural_evidence":605},[761,765,769,774],{"code":16,"score":604,"supported":605,"title":762,"abstract":763,"tags":764},[719],[589,719],[589],{"code":48,"score":604,"supported":605,"title":766,"abstract":767,"tags":768},[622],[620,622],[753],{"code":144,"score":625,"supported":605,"title":770,"abstract":771,"tags":773},[],[772,145],"土壤",[755],{"code":152,"score":400,"supported":605,"title":775,"abstract":776,"tags":778},[],[777],"水资源",[754],[780,781],"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":783,"openalex_id":785,"authors":786,"venue":695,"cited_by_count":424,"oa_url":744,"card":789,"direction":686,"ingested_from":687},"W7223137846",[787],{"name":788,"orcid":11},"Prachi Khairnar",{"tldr":790,"method":791,"finding":792,"direction":686,"opportunity":793},"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":796,"title":797,"url":798,"summary":799,"summary_zh":800,"content":11,"source_name":801,"source_url":798,"published_at":579,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":802,"score_detail":803,"sources":806,"tags":808,"primary_topic":811,"secondary_topics":812,"classification":815,"search_phrases":862,"slug":865,"view_count":424,"doi":866,"paper":867,"created_at":886},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":361,"substance":321,"depth":361,"authority":804,"freshness":584,"relevant":455,"comment":805},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。",[807],{"name":801,"url":798},[589,590,620,809,810],"精准农业","气候韧性",{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},[813,814],{"code":48,"slug":49,"name":50,"path":51,"parent_code":7},{"code":152,"slug":153,"name":154,"path":155,"parent_code":128},{"status":599,"locked":581,"version":4,"method":600,"evidence":816},{"candidates":817,"agricultural_evidence":605},[818,826,832,838,842,848,852,857],{"code":24,"score":361,"supported":605,"title":819,"abstract":820,"tags":825},[615],[821,613,822,614,823,824],"深度学习","计算机视觉","deep learning","computer vision",[590],{"code":16,"score":827,"supported":605,"title":828,"abstract":830,"tags":831},16,[829],"precision agriculture",[809,829],[589,809],{"code":48,"score":385,"supported":605,"title":833,"abstract":834,"tags":837},[],[620,835,622,836],"边缘计算","edge computing",[620],{"code":152,"score":584,"supported":605,"title":839,"abstract":840,"tags":841},[],[608,777,153],[],{"code":184,"score":625,"supported":605,"title":843,"abstract":844,"tags":847},[],[845,846],"气候变化","climate change",[810],{"code":144,"score":637,"supported":605,"title":849,"abstract":850,"tags":851},[],[772,145],[],{"code":256,"score":652,"supported":605,"title":853,"abstract":854,"tags":856},[],[855],"间作",[],{"code":96,"score":652,"supported":605,"title":858,"abstract":859,"tags":861},[],[860],"养分管理",[],[863,864],"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":866,"openalex_id":868,"authors":869,"venue":801,"cited_by_count":424,"oa_url":798,"card":880,"direction":686,"ingested_from":687},"W7223121456",[870,873,875,877],{"name":871,"orcid":872},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":874,"orcid":11},"Oluwadare OYEBODE",{"name":876,"orcid":11},"Iretiayo Adelaiye",{"name":878,"orcid":879},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":881,"method":882,"finding":883,"direction":884,"opportunity":885},"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":888,"title":889,"url":890,"summary":891,"summary_zh":892,"content":11,"source_name":695,"source_url":890,"published_at":579,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":893,"score_detail":894,"sources":896,"tags":898,"primary_topic":901,"secondary_topics":902,"classification":905,"search_phrases":931,"slug":934,"view_count":424,"doi":935,"paper":936,"created_at":946},5552,"INTEGRATING IoT, REMOTE SENSING, AND ARTIFICIAL INTELLIGENCE FOR SMART IRRIGATION: A REVIEW OF ARCHITECTURES, METHODS, AND CHALLENGES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531020","This research paper reviews the integration of Internet of Things (IoT), remote sensing, and Artificial Intelligence (AI) in smart irrigation systems. It discusses system architectures, intelligent irrigation techniques, automated water management, key challenges, and future research opportunities to improve water-use efficiency and promote sustainable agriculture.","本文综述了物联网（Internet of Things, IoT）、遥感与人工智能（Artificial Intelligence, AI）在智能灌溉系统中的融合应用，探讨了系统架构、智能灌溉技术、自动化水资源管理、关键挑战及未来研究方向，以提升用水效率并促进可持续农业发展。",75,{"impact":361,"substance":508,"depth":827,"authority":416,"freshness":584,"relevant":455,"comment":895},"系统综述物联网、遥感与AI融合的智能灌溉架构与方法，属智慧农业细分领域有实质增量的国际综述，值得入选。",[897],{"name":695,"url":890},[590,753,591,899,900],"智慧灌溉","节水农业",{"code":152,"slug":153,"name":154,"path":155,"parent_code":128},[903,904],{"code":48,"slug":49,"name":50,"path":51,"parent_code":7},{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":906},{"candidates":907,"agricultural_evidence":605},[908,913,917,921,925],{"code":152,"score":909,"supported":605,"title":910,"abstract":911,"tags":912},19,[153],[608,777,153],[899,900],{"code":48,"score":361,"supported":605,"title":914,"abstract":915,"tags":916},[622],[620,621,622],[753],{"code":32,"score":604,"supported":605,"title":918,"abstract":919,"tags":920},[634],[591,634],[591],{"code":24,"score":416,"supported":605,"title":922,"abstract":923,"tags":924},[615],[615],[590],{"code":192,"score":637,"supported":605,"title":926,"abstract":927,"tags":930},[],[928,929],"可持续农业","sustainable agriculture",[],[932,933],"IoT 遥感 AI 智能灌溉","农业人工智能 农业物联网 智慧灌溉 节水农业","IoT遥感AI智能灌溉-5552","10.5281\u002Fzenodo.23531020",{"doi":935,"openalex_id":937,"authors":938,"venue":695,"cited_by_count":424,"oa_url":890,"card":941,"direction":686,"ingested_from":687},"W7223090389",[939],{"name":940,"orcid":11},"Patil",{"tldr":942,"method":943,"finding":944,"direction":686,"opportunity":945},"综述物联网、遥感与人工智能融合的智能灌溉系统架构、方法与挑战。","文献综述，梳理IoT、遥感与AI集成的系统架构与智能灌溉技术。","融合三者可提升用水效率并推动可持续农业，但仍面临关键挑战。","可针对多源数据融合的实时灌溉决策模型与边缘部署开展实证研究。","2026-10-22T11:20:18.028252Z",{"id":948,"title":949,"url":950,"summary":951,"summary_zh":952,"content":11,"source_name":695,"source_url":950,"published_at":953,"category":580,"cover_url":11,"hotness":447,"is_selected":581,"score":954,"score_detail":955,"sources":958,"tags":962,"primary_topic":964,"secondary_topics":965,"classification":967,"search_phrases":995,"slug":998,"view_count":424,"doi":999,"paper":1000,"created_at":1017},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":321,"substance":956,"depth":712,"authority":804,"freshness":424,"relevant":455,"comment":957},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。",[959,960],{"name":695,"url":950},{"name":695,"url":961},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527988",[589,590,753,963,593],"区块链溯源",{"code":152,"slug":153,"name":154,"path":155,"parent_code":128},[966],{"code":48,"slug":49,"name":50,"path":51,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":968},{"candidates":969,"agricultural_evidence":605},[970,974,978,982,987,991],{"code":152,"score":604,"supported":605,"title":971,"abstract":972,"tags":973},[153],[608,153],[593],{"code":48,"score":385,"supported":605,"title":975,"abstract":976,"tags":977},[],[620,621,622],[753],{"code":24,"score":400,"supported":605,"title":979,"abstract":980,"tags":981},[],[615],[590],{"code":496,"score":652,"supported":605,"title":983,"abstract":984,"tags":986},[],[985],"小农户",[],{"code":465,"score":455,"supported":581,"title":988,"abstract":989,"tags":990},[],[],[963],{"code":16,"score":455,"supported":581,"title":992,"abstract":993,"tags":994},[],[],[589],[996,997],"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":999,"openalex_id":1001,"authors":1002,"venue":695,"cited_by_count":424,"oa_url":950,"card":1011,"direction":686,"ingested_from":687},"W7223034914",[1003,1006,1008],{"name":1004,"orcid":1005},"A. Shaji George","https:\u002F\u002Forcid.org\u002F0000-0002-8677-3682",{"name":1007,"orcid":11},"Dr.T.Baskar",{"name":1009,"orcid":1010},"P. Balaji Srikaanth","https:\u002F\u002Forcid.org\u002F0000-0003-4717-7367",{"tldr":1012,"method":1013,"finding":1014,"direction":1015,"opportunity":1016},"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",{"id":1019,"title":1020,"url":1021,"summary":1022,"summary_zh":1023,"content":11,"source_name":1024,"source_url":1021,"published_at":1025,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":329,"score_detail":1026,"sources":1028,"tags":1030,"primary_topic":1033,"secondary_topics":1034,"classification":1036,"search_phrases":1055,"slug":1058,"view_count":424,"doi":1059,"paper":1060,"created_at":1078},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","2026-10-19T00:00:00Z",{"impact":416,"substance":361,"depth":827,"authority":804,"freshness":173,"relevant":455,"comment":1027},"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。",[1029],{"name":1024,"url":1021},[589,620,809,1031,1032],"节水灌溉","LoRa",{"code":48,"slug":49,"name":50,"path":51,"parent_code":7},[1035],{"code":152,"slug":153,"name":154,"path":155,"parent_code":128},{"status":599,"locked":581,"version":4,"method":600,"evidence":1037},{"candidates":1038,"agricultural_evidence":605},[1039,1043,1047,1051],{"code":48,"score":361,"supported":605,"title":1040,"abstract":1041,"tags":1042},[622],[620,621,622],[620],{"code":152,"score":604,"supported":605,"title":1044,"abstract":1045,"tags":1046},[153],[608,153],[1031],{"code":144,"score":637,"supported":605,"title":1048,"abstract":1049,"tags":1050},[],[772,145],[],{"code":16,"score":69,"supported":581,"title":1052,"abstract":1053,"tags":1054},[],[],[589,809],[1056,1057],"ESP32 LoRa 智能灌溉","SX1278 精准灌溉","ESP32LoRa智能灌溉-5378","10.3390\u002Felectronics15204831",{"doi":1059,"openalex_id":1061,"authors":1062,"venue":1024,"cited_by_count":424,"oa_url":1021,"card":1073,"direction":686,"ingested_from":687},"W7222959710",[1063,1065,1067,1070],{"name":1064,"orcid":11},"Hanfoug Salah",{"name":1066,"orcid":11},"Riad Saidi",{"name":1068,"orcid":1069},"Abdelaziz Aouiche","https:\u002F\u002Forcid.org\u002F0000-0003-1378-7941",{"name":1071,"orcid":1072},"Yazeed Alkhrijah","https:\u002F\u002Forcid.org\u002F0000-0001-7352-003X",{"tldr":1074,"method":1075,"finding":1076,"direction":686,"opportunity":1077},"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",{"id":1080,"title":1081,"url":1082,"summary":1083,"summary_zh":1084,"content":11,"source_name":1085,"source_url":1082,"published_at":1025,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1086,"score_detail":1087,"sources":1089,"tags":1091,"primary_topic":1094,"secondary_topics":1095,"classification":1097,"search_phrases":1114,"slug":1117,"view_count":424,"doi":1118,"paper":1119,"created_at":1139},5376,"Agri-Synergy: A Hardware–Software Co-Designed Hybrid IoT System for Sensor Interface Standardization and LLM-Driven Interactive Decision Support","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriengineering8100446","This paper presents Agri-Synergy, a hybrid Internet of Things (IoT) system featuring a hardware–software co-design to tackle sensor data heterogeneity and the barriers to effective AI-driven decision support in smart agriculture. Unlike software-based data harmonization, we propose a universal sensor printed circuit board (PCB) with RS485\u002FModbus interface for hardware unification, complemented by on-node edge processing that ensures standardized data from source. To lower the entry barrier for farmers, the system integrates the DeepSeek large language model (LLM) API, combining static agricultural knowledge with real-time sensor data, for context-aware, natural language-based decision support, functioning without local model deployment. Field deployment showed reference-verified sensor accuracy predominantly within manufacturer specifications, stable 4G connectivity during the 4-h benchmark but intermittent dropouts during the 20-day deployment, and remote-control success rates of 96% (normal) and 80% (weak signal at 300 m). Multi-node concurrent transmission resulted in average latency of 238.0 ± 27.1 ms and packet loss below 0.5%. The system achieves a 3–5× cost reduction compared to commercial platforms. These results demonstrate that addressing data heterogeneity from source through hardware unification and software edge processing, while leveraging AI to lower the barrier to system adoption, provides a more reliable foundation for data synchronization and accessible AI-driven analytics.","本文提出Agri-Synergy，一种采用软硬件协同设计的混合物联网（IoT）系统，旨在解决智慧农业中传感器数据异构性问题以及有效AI驱动决策支持的障碍。与基于软件的数据协调方式不同，我们提出了一种带有RS485\u002FModbus接口的通用传感器印刷电路板（PCB）以实现硬件统一，并辅以节点端边缘处理，确保从源头获得标准化数据。为降低农民的使用门槛，该系统集成了DeepSeek大语言模型（LLM）API，将静态农业知识与实时传感器数据相结合，提供上下文感知的、基于自然语言的决策支持，且无需本地部署模型即可运行。现场部署表明，经参考验证的传感器精度基本处于制造商规格范围内；在4小时基准测试期间4G连接稳定，但在20天部署期间出现间歇性断连；远程控制成功率分别为96%（正常信号）和80%（300 m处弱信号）。多节点并发传输的平均延迟为238.0 ± 27.1 ms，丢包率低于0.5%。与商业平台相比，该系统实现了3–5倍的成本降低。这些结果表明，通过硬件统一和软件边缘处理从源头解决数据异构性问题，同时利用AI降低系统采用门槛，可为数据同步和可及的AI驱动分析提供更可靠的基础。","AgriEngineering",76,{"impact":827,"substance":956,"depth":361,"authority":416,"freshness":584,"relevant":455,"comment":1088},"硬件统一接口与边缘处理结合大模型决策支持的农业物联网系统论文，方法新颖、有实地部署数据，但属细分技术进展，公共影响有限。",[1090],{"name":1085,"url":1082},[589,590,753,1092,1093],"大语言模型","传感器标准化",{"code":48,"slug":49,"name":50,"path":51,"parent_code":7},[1096],{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":1098},{"candidates":1099,"agricultural_evidence":605},[1100,1104,1110],{"code":48,"score":361,"supported":605,"title":1101,"abstract":1102,"tags":1103},[622],[620,621,622],[753],{"code":24,"score":827,"supported":605,"title":1105,"abstract":1107,"tags":1109},[1106],"decision support",[1108,1106],"large language model",[590,1092],{"code":16,"score":625,"supported":605,"title":1111,"abstract":1112,"tags":1113},[],[589,719],[589],[1115,1116],"Agri-Synergy 物联网 传感器","DeepSeek 决策支持","Agri-Synergy物联网传感器-5376","10.3390\u002Fagriengineering8100446",{"doi":1118,"openalex_id":1120,"authors":1121,"venue":1085,"cited_by_count":424,"oa_url":1082,"card":1134,"direction":686,"ingested_from":687},"W7222920424",[1122,1124,1126,1128,1130,1132],{"name":1123,"orcid":11},"Leqi Lai",{"name":1125,"orcid":11},"Langtao Duan",{"name":1127,"orcid":11},"Yuhao Chen",{"name":1129,"orcid":11},"Yuhang Zheng",{"name":1131,"orcid":11},"Jinyu Yang",{"name":1133,"orcid":11},"Mingyao Wen",{"tldr":1135,"method":1136,"finding":1137,"direction":686,"opportunity":1138},"提出软硬件协同的混合物联网系统，统一传感器接口并用大模型提供交互式决策支持。","通用RS485\u002FModbus传感器PCB、节点边缘处理、DeepSeek大模型A","传感器精度达标，多节点延迟238ms、丢包低于0.5%，成本降低3-5倍。","可研究弱信号下通信可靠性提升与边缘-云端LLM协同的长期田间鲁棒性。","2026-10-20T23:30:08.319698Z",{"id":1141,"title":1142,"url":1143,"summary":1144,"summary_zh":1145,"content":11,"source_name":1146,"source_url":1143,"published_at":1147,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":329,"score_detail":1148,"sources":1150,"tags":1152,"primary_topic":1155,"secondary_topics":1156,"classification":1159,"search_phrases":1197,"slug":1200,"view_count":424,"doi":11,"paper":1201,"created_at":1219},5313,"Real-Time IoT-Integrated Ensemble LSTM–Bi-LSTM Framework for Greenhouse Microclimate Forecasting","https:\u002F\u002Fwasrpublication.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F512","Accurate prediction of greenhouse microclimates is essential for optimizing crop growth and resource utilization in precision agriculture. The research presents an IoT-integrated ensemble deep learning framework for real-time greenhouse microclimate forecasting using Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks. Environmental variables, including temperature, light intensity, soil moisture, and humidity, were acquired through IoT sensor nodes deployed in a controlled greenhouse environment. A benchmark greenhouse dataset was first used for model development and hyperparameter optimization, while an original IoT-based dataset collected during cucumber, tomato, and pepper cultivation at the Federal University Wukari, Nigeria, was used for validation and real-world deployment. The data were preprocessed through noise filtering, missing value imputation, Z-score-based outlier removal, and Min–Max normalization. A weighted ensemble strategy was then applied to combine LSTM and Bi-LSTM predictions for improved robustness. Experimental results show that the Bi-LSTM model outperformed the LSTM model, achieving R² = 0.9981, MAE = 0.0088, RMSE = 0.0110, and MSE = 0.0001, compared to LSTM results of R² = 0.9956, MAE = 0.0133, RMSE = 0.0167, and MSE = 0.0003. Statistical validation using paired t-test, Wilcoxon signed-rank test, confidence intervals, and multi-run analysis confirmed that the performance improvement is statistically significant and robust. The proposed framework enables accurate real-time microclimate forecasting and intelligent environmental monitoring, providing a scalable solution for precision greenhouse management, improved crop productivity, optimized energy utilization, and efficient irrigation scheduling.","准确预测温室微气候对于精准农业中优化作物生长和资源利用至关重要。本研究提出了一种集成物联网的集成深度学习框架，利用长短期记忆网络（LSTM）和双向长短期记忆网络（Bi-LSTM）进行实时温室微气候预测。温度、光照强度、土壤湿度和湿度等环境变量通过部署在受控温室环境中的物联网传感器节点采集。研究首先使用基准温室数据集进行模型开发和超参数优化，随后使用在尼日利亚武卡里联邦大学黄瓜、番茄和辣椒种植期间采集的原始物联网数据集进行验证和实际部署。数据经过噪声滤波、缺失值插补、基于Z分数的异常值去除和最小—最大归一化预处理。然后采用加权集成策略融合LSTM和Bi-LSTM的预测结果以提高鲁棒性。实验结果表明，Bi-LSTM模型优于LSTM模型，其R² = 0.9981、MAE = 0.0088、RMSE = 0.0110、MSE = 0.0001，而LSTM的结果为R² = 0.9956、MAE = 0.0133、RMSE = 0.0167、MSE = 0.0003。采用配对t检验、Wilcoxon符号秩检验、置信区间和多轮运行分析进行的统计验证证实，性能提升具有统计显著性和鲁棒性。所提出的框架能够实现准确的实时微气候预测和智能环境监测，为精准温室管理、提高作物生产力、优化能源利用和高效灌溉调度提供了可扩展的解决方案。","World Journal of Multidisciplinary Studies","2026-10-16T00:00:00Z",{"impact":416,"substance":956,"depth":712,"authority":584,"freshness":173,"relevant":455,"comment":1149},"方法扎实、验证充分，但属单点应用研究，公共影响有限，适合作为技术前沿条目而非头条精选。",[1151],{"name":1146,"url":1143},[589,590,753,1153,1154],"温室环境","小气候预测",{"code":324,"slug":325,"name":326,"path":327,"parent_code":271},[1157,1158],{"code":48,"slug":49,"name":50,"path":51,"parent_code":7},{"code":279,"slug":280,"name":281,"path":282,"parent_code":271},{"status":599,"locked":581,"version":4,"method":600,"evidence":1160},{"candidates":1161,"agricultural_evidence":605},[1162,1166,1172,1181,1185,1189,1193],{"code":48,"score":604,"supported":605,"title":1163,"abstract":1164,"tags":1165},[622],[620,622],[753],{"code":324,"score":345,"supported":605,"title":1167,"abstract":1169,"tags":1171},[1168],"greenhouse",[1170,1168],"温室",[],{"code":279,"score":584,"supported":605,"title":1173,"abstract":1174,"tags":1180},[],[1175,1176,1177,1178,1179],"番茄","辣椒","黄瓜","tomato","cucumber",[],{"code":16,"score":625,"supported":605,"title":1182,"abstract":1183,"tags":1184},[],[809,829],[589],{"code":24,"score":625,"supported":605,"title":1186,"abstract":1187,"tags":1188},[],[821,823],[590],{"code":144,"score":637,"supported":605,"title":1190,"abstract":1191,"tags":1192},[],[772,145],[],{"code":152,"score":637,"supported":605,"title":1194,"abstract":1195,"tags":1196},[],[608,153],[],[1198,1199],"温室 小气候 LSTM 预测","尼日利亚 温室 IoT 黄瓜 番茄","温室小气候LSTM预测-5313",{"doi":11,"openalex_id":1202,"authors":1203,"venue":1146,"cited_by_count":424,"oa_url":11,"card":1214,"direction":686,"ingested_from":687},"W7222909837",[1204,1206,1208,1210,1212],{"name":1205,"orcid":11},"Siman Emmanuel*",{"name":1207,"orcid":11},"Edward Onyebueke Agu",{"name":1209,"orcid":11},"Musa Gani",{"name":1211,"orcid":11},"Oladunjoye John Abiodun",{"name":1213,"orcid":11},"Anagu Emmanuel John",{"tldr":1215,"method":1216,"finding":1217,"direction":686,"opportunity":1218},"提出物联网集成LSTM与Bi-LSTM加权集成框架，实时预测温室微气候。","IoT传感器采集温光湿数据，经预处理后用LSTM-Bi-LSTM加权集成建模。","Bi-LSTM优于LSTM，R²达0.9981，统计检验证实性能提升显著稳健。","可探索边缘端轻量化部署与多作物跨温室迁移学习，提升实时预测泛化性。","2026-10-19T23:30:13.295236Z",{"id":1221,"title":1222,"url":1223,"summary":1224,"summary_zh":1225,"content":11,"source_name":1226,"source_url":1223,"published_at":1227,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1228,"score_detail":1229,"sources":1231,"tags":1233,"primary_topic":1237,"secondary_topics":1238,"classification":1239,"search_phrases":1255,"slug":1258,"view_count":424,"doi":1259,"paper":1260,"created_at":1274},5309,"A Multi-Layer Contextual Model for Managing Data Overload in Smart Hydroponics","https:\u002F\u002Fdoi.org\u002F10.32890\u002Fjict2026.25.4.4","The widespread use of the Internet of Things (IoT) in hydroponic agriculture allows continuous monitoring of environmental conditions and provides real-time information to support crop management. However, the increasing amount of sensor data generated makes the monitoring process more complex as users need to interpret multiple environmental parameters simultaneously. Existing hydroponic monitoring systems focus on data collection and visualization, while the management and information delivery aspects to reduce data overload are still limited. This study proposes a multi-layer contextual model to manage environmental information in an intelligent hydroponic monitoring system. The model consists of five interconnected layers (Power, Sensor, Communication, Database and Visualization), which support the process of acquisition, validation, management and contextual data delivery. To validate the proposed model, a solar-powered IoT hydroponic monitoring system integrated with an Augmented Reality (AR) application was developed based on Situation Awareness Theory. Using the System Usability Scale (SUS), the prototype was assessed by three domain experts. The results achieved an average usability score of 85.83, which reflects excellent usability. The results indicate that the proposed model improves organisation and presentation of environmental information by prioritising contextually relevant data rather than displaying all available sensor readings at once. This enables users to identify important environmental conditions efficiently and supports more effective monitoring. This approach helps to reduce data overload and provides a practical framework that can support the development of IoT-based smart agriculture applications in the future through the integration of anomaly detection techniques and predictive analytics.","物联网（IoT）在水培农业中的广泛使用使得环境条件的持续监测成为可能，并提供实时信息以支持作物管理。然而，传感器数据量的不断增加使监测过程更加复杂，因为用户需要同时解读多个环境参数。现有的水培监测系统侧重于数据采集与可视化，而在减少数据过载的管理与信息传递方面仍然有限。本研究提出一种多层上下文模型，用于管理智能水培监测系统中的环境信息。该模型由五个相互关联的层级（供电、传感器、通信、数据库和可视化）组成，支持数据的采集、验证、管理和上下文传递过程。为验证所提出的模型，基于态势感知理论开发了一套集成了增强现实（AR）应用的太阳能物联网水培监测系统。采用系统可用性量表（SUS）由三位领域专家对原型进行评估，结果获得平均可用性得分85.83，反映出优异的可用性。结果表明，所提出的模型通过优先呈现上下文相关的数据而非一次性显示所有可用的传感器读数，改善了环境信息的组织与呈现方式。这使用户能够高效识别重要的环境条件，并支持更有效的监测。该方法有助于减少数据过载，并提供了一个实用框架，未来可通过集成异常检测技术和预测分析来支持基于物联网的智慧农业应用的开发。","Journal of Information and Communication Technology","2026-10-18T00:00:00Z",70,{"impact":416,"substance":508,"depth":827,"authority":804,"freshness":584,"relevant":455,"comment":1230},"提出五层情境化模型，并用太阳能物联网与AR原型验证，SUS 85.83，方法新颖、结论可靠，但属单点原型研究，产业影响有限。",[1232],{"name":1226,"url":1223},[589,620,1234,1235,1236],"水培","增强现实","数据过载",{"code":48,"slug":49,"name":50,"path":51,"parent_code":7},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":1240},{"candidates":1241,"agricultural_evidence":605},[1242,1246,1250],{"code":48,"score":385,"supported":605,"title":1243,"abstract":1244,"tags":1245},[],[620,621,622],[620],{"code":16,"score":625,"supported":605,"title":1247,"abstract":1248,"tags":1249},[],[589,719],[589],{"code":324,"score":400,"supported":605,"title":1251,"abstract":1252,"tags":1254},[],[1253],"hydroponic",[1234],[1256,1257],"智能水培 物联网 监测系统","增强现实 水培 数据过载","智能水培物联网监测系统-5309","10.32890\u002Fjict2026.25.4.4",{"doi":1259,"openalex_id":1261,"authors":1262,"venue":1226,"cited_by_count":424,"oa_url":1223,"card":1269,"direction":686,"ingested_from":687},"W7222865525",[1263,1266],{"name":1264,"orcid":1265},"Ruziana Mohamad Rasli","https:\u002F\u002Forcid.org\u002F0009-0006-7187-513X",{"name":1267,"orcid":1268},"Sobihatun Nur Abdul Salam","https:\u002F\u002Forcid.org\u002F0000-0002-6751-9187",{"tldr":1270,"method":1271,"finding":1272,"direction":686,"opportunity":1273},"提出五层情境模型管理水培IoT环境数据，结合AR应用减少数据过载。","五层情境模型+太阳能IoT水培监测系统+AR应用，SUS量表由3位专家评估。","原型SUS均分85.83，情境化优先展示关键数据可有效缓解数据过载。","可延伸至异常检测与预测分析融合，并开展多用户大规模实证验证。","2026-10-19T23:30:07.917393Z",{"id":1276,"title":1277,"url":1278,"summary":1279,"summary_zh":1280,"content":11,"source_name":1281,"source_url":1278,"published_at":1025,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1282,"score_detail":1283,"sources":1285,"tags":1287,"primary_topic":1291,"secondary_topics":1292,"classification":1295,"search_phrases":1316,"slug":1319,"view_count":455,"doi":1320,"paper":1321,"created_at":1334},5308,"Geopolitics and food security: A comprehensive review of emerging risks and responses","https:\u002F\u002Fdoi.org\u002F10.51200\u002Fjsffs.v2i2.7666","This review paper examines the relationship between geopolitics and food security through a Systematic Literature Review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework. A total of 15 peer-reviewed journal articles published between 2015 and 2026 were reviewed to identify emerging geopolitical risks and responses affecting global food systems. The study synthesizes literature on armed conflicts, trade restrictions, climate change, supply chain disruptions, energy market instability, and technological transformation in agriculture. The findings identified four major themes. First, geopolitical conflicts and protectionist trade policies significantly disrupt global food supply chains and increase food price volatility. Second, climate-related shock such as droughts, floods, and extreme weather events intensifies food insecurity, particularly in import-dependent and developing economies. Third, weaknesses in global logistics and energy dependence expose food systems to external shocks and market instability. Fourth, technological innovations, including smart farming, precision agriculture, and digital monitoring systems, play an increasingly important role in strengthening agricultural productivity, improving resource efficiency, and enhancing resilience against geopolitical and environmental uncertainties. The policy implications of this study include strengthening regional food reserves, diversifying import sources, investing in climate-resilient agriculture, promoting the adoption of smart farming technologies, and enhancing institutional cooperation between governments and international organizations. The relevance of smart farming is increasingly evident, as precision agriculture, Internet of Things (IoT)-based monitoring systems, and data-driven farming technologies play a critical role in improving productivity, reducing resource inefficiency, and enhancing food system resilience under geopolitical stress. The study contributes to a broader understanding of how geopolitical dynamics shape food security and provides recommendations for building sustainable, resilient, and technology-driven food systems in an increasingly uncertain global environment.","本文通过遵循系统评价与荟萃分析首选报告条目（Preferred Reporting Items for Systematic Reviews and Meta-Analyses）框架的系统文献综述，考察地缘政治与粮食安全之间的关系。共 review 了2015年至2026年间发表的15篇同行评审期刊论文，以识别影响全球粮食系统的新兴地缘政治风险与应对措施。本研究综合了关于武装冲突、贸易限制、气候变化、供应链中断、能源市场不稳定以及农业技术转型的文献。研究结果识别出四大主题。第一，地缘政治冲突和保护主义贸易政策显著扰乱全球粮食供应链并加剧粮食价格波动。第二，干旱、洪水和极端天气事件等气候相关冲击加剧粮食不安全，尤其是在依赖进口的经济体和发展中经济体中。第三，全球物流的薄弱环节和能源依赖使粮食系统暴露于外部冲击和市场不稳定之中。第四，包括智慧农业、精准农业和数字监测系统在内的技术创新，在增强农业生产力、提高资源效率以及增强应对地缘政治和环境不确定性的韧性方面发挥着日益重要的作用。本研究的政策启示包括加强区域粮食储备、多元化进口来源、投资气候韧性农业、促进智慧农业技术的采用，以及加强政府与国际组织之间的制度合作。智慧农业的相关性日益明显，因为精准农业、基于物联网（IoT）的监测系统和数据驱动农业技术在提高生产力、减少资源低效以及增强地缘政治压力下粮食系统韧性方面发挥着关键作用。本研究有助于更广泛地理解地缘政治动态如何塑造粮食安全，并为在日益不确定的全球环境中构建可持续、有韧性且技术驱动的粮食系统提供建议。","Journal of Smart Farming and Food Security",77,{"impact":361,"substance":508,"depth":712,"authority":804,"freshness":584,"relevant":455,"comment":1284},"系统综述梳理地缘政治与粮食安全风险，并强调智慧农业的韧性价值，具政策与产业参考意义，但属综述性论文，非突破性成果。",[1286],{"name":1281,"url":1278},[589,1288,1289,809,1290],"农业供应链","粮食安全","地缘政治",{"code":263,"slug":264,"name":265,"path":266,"parent_code":200},[1293,1294],{"code":184,"slug":185,"name":186,"path":187,"parent_code":128},{"code":48,"slug":49,"name":50,"path":51,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":1296},{"candidates":1297,"agricultural_evidence":605},[1298,1303,1307,1312],{"code":263,"score":604,"supported":605,"title":1299,"abstract":1301,"tags":1302},[1300],"food security",[1289,1300],[1289],{"code":16,"score":524,"supported":605,"title":1304,"abstract":1305,"tags":1306},[],[589,809,829],[589,809],{"code":184,"score":584,"supported":605,"title":1308,"abstract":1309,"tags":1311},[],[845,1310,846],"干旱",[],{"code":48,"score":584,"supported":605,"title":1313,"abstract":1314,"tags":1315},[],[620,621,622],[],[1317,1318],"地缘政治 粮食安全","智慧农业 粮食系统韧性","地缘政治粮食安全-5308","10.51200\u002Fjsffs.v2i2.7666",{"doi":1320,"openalex_id":1322,"authors":1323,"venue":1281,"cited_by_count":424,"oa_url":1278,"card":1329,"direction":686,"ingested_from":687},"W7222902031",[1324,1326],{"name":1325,"orcid":11},"Parameswaran Mahendran",{"name":1327,"orcid":1328},"Wong Sing Yun","https:\u002F\u002Forcid.org\u002F0000-0001-5776-8852",{"tldr":1330,"method":1331,"finding":1332,"direction":686,"opportunity":1333},"系统综述地缘政治风险对粮食安全的影响及应对，强调智慧农业的韧性作用。","按PRISMA框架系统综述2015-2026年15篇同行评议文献。","冲突、贸易限制、气候与能源冲击威胁粮食安全，智慧农业可提升韧性与效率。","可量化研究物联网与精准农业在地缘政治冲击下对粮食系统韧性的实证贡献。","2026-10-19T23:30:07.850994Z",{"id":1336,"title":1337,"url":1338,"summary":1339,"summary_zh":1340,"content":11,"source_name":1341,"source_url":1338,"published_at":1342,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1343,"score_detail":1344,"sources":1346,"tags":1348,"primary_topic":1351,"secondary_topics":1352,"classification":1354,"search_phrases":1369,"slug":1372,"view_count":424,"doi":1373,"paper":1374,"created_at":1393},5237,"Integrasi Framework Laravel dan Android pada Sistem IoT untuk Monitoring Parameter Fisika-Kimia di Greenhouse","https:\u002F\u002Fdoi.org\u002F10.36257\u002Fteknika.v20i3.12040","Pengembangan teknologi pertanian presisi (precision agriculture) pada ekosistem greenhouse menggunakan sistem hidroponik menghadapi tantangan teknis dalam menjaga stabilitas parameter lingkungan mikro secara simultan. Penelitian ini bertujuan untuk mengintegrasikan framework Laravel dan platform Android ke dalam sistem Internet of Things (IoT) untuk memantau parameter fisika-kimia lingkungan, meliputi suhu udara, kelembaban, intensitas cahaya, dan kadar karbon dioksida (CO2), pada budidaya pakcoy (Brassica rapa L.) di greenhouse hidroponik. Fokus utama penelitian ini terletak pada perancangan arsitektur backend RESTful API menggunakan Laravel untuk mengelola penyimpanan basis data riwayat jangka panjang serta menyinkronkan transmisi data dari multi-sensor (DHT22, LDR, dan sensor gas CO2) yang memiliki karakteristik sampling rate berbeda. Di sisi pengguna, aplikasi Android dan antarmuka web dikembangkan sebagai frontend yang mengonsumsi data melalui serialisasi format JSON menggunakan metode polling HTTP GET real-time, serta menyediakan tiga mode kendali aktuator, yaitu manual, otomatis berbasis ambang batas sensor, dan penjadwalan. Pengujian fungsionalitas dan kinerja sistem dilakukan melalui visualisasi antarmuka pada dasbor situs web neliti-sk.darmajaya.ac.id serta evaluasi transmisi data nirkabel. Infrastruktur backend berbasis Laravel terbukti stabil dalam menangani fluktuasi frekuensi data sensor yang kompleks. Berdasarkan evaluasi log data selama 24 jam pengujian kontinu pada rentang 14 Agustus 2026 (00.00–23.00 WIB), sistem berhasil merekam seluruh 24 titik data per jam (100%) tanpa satupun jam yang terlewat. Integrasi ini menghadirkan solusi pemantauan sekaligus pengendalian pertanian presisi yang andal, terintegrasi, dan memiliki skalabilitas tinggi bagi operator greenhouse, meskipun masih memerlukan mekanisme offline logging untuk mengantisipasi gangguan konektivitas atau pemadaman listrik di masa mendatang.","在温室生态系统中利用水培系统开发精准农业（precision agriculture）技术，面临着同时维持微环境参数稳定性的技术挑战。本研究旨在将Laravel框架和Android平台集成到物联网（IoT）系统中，以监测水培温室中小白菜（Brassica rapa L.）栽培环境的物理化学参数，包括空气温度、湿度、光照强度和二氧化碳（CO2）浓度。本研究的主要重点在于设计基于Laravel的RESTful API后端架构，以管理长期历史数据库的存储，并同步具有不同采样率特征的多传感器（DHT22、LDR和CO2气体传感器）的数据传输。在用户端，开发了Android应用和Web界面作为前端，通过HTTP GET实时轮询方法以JSON格式序列化消费数据，并提供三种执行器控制模式，即手动、基于传感器阈值的自动控制和定时调度。系统功能与性能测试通过neliti-sk.darmajaya.ac.id网站仪表板的界面可视化以及无线数据传输评估进行。基于Laravel的后端基础设施在处理复杂的传感器数据频率波动方面表现出稳定性。根据2026年8月14日（00:00–23:00 WIB）连续24小时测试期间的数据日志评估，系统成功记录了每小时全部24个数据点（100%），无一小时遗漏。该集成提供了一种可靠、集成且具有高可扩展性的精准农业监测与控制解决方案，适用于温室操作人员，但未来仍需离线日志记录机制以应对连接中断或停电情况。","TEKNIKA","2026-10-17T00:00:00Z",56,{"impact":173,"substance":361,"depth":827,"authority":637,"freshness":173,"relevant":455,"comment":1345},"论文将Laravel与Android集成用于水培温室多参数监测，方法具体、数据连续24小时完整，但属单点应用研究，公共影响有限，适合作为技术案例而非每日精选头条。",[1347],{"name":1341,"url":1338},[589,620,809,1349,1350],"温室大棚","水培蔬菜",{"code":324,"slug":325,"name":326,"path":327,"parent_code":271},[1353],{"code":48,"slug":49,"name":50,"path":51,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":1355},{"candidates":1356,"agricultural_evidence":605},[1357,1361,1365],{"code":48,"score":361,"supported":605,"title":1358,"abstract":1359,"tags":1360},[622],[620,621,622],[620],{"code":324,"score":345,"supported":605,"title":1362,"abstract":1363,"tags":1364},[1168],[1170,1168],[],{"code":16,"score":173,"supported":605,"title":1366,"abstract":1367,"tags":1368},[],[809,829],[589,809],[1370,1371],"Laravel Android 温室物联网","小白菜 水培 温室监测","LaravelAndroid温室物联网-5237","10.36257\u002Fteknika.v20i3.12040",{"doi":1373,"openalex_id":1375,"authors":1376,"venue":1341,"cited_by_count":424,"oa_url":1387,"card":1388,"direction":686,"ingested_from":687},"W7222850582",[1377,1379,1382,1385],{"name":1378,"orcid":11},"Ramadanzah Ramadanzah",{"name":1380,"orcid":1381},"Dodi Yudo Setyawan","https:\u002F\u002Forcid.org\u002F0000-0003-2205-7617",{"name":1383,"orcid":1384},"Nurfiana Nurfiana","https:\u002F\u002Forcid.org\u002F0000-0002-6149-0918",{"name":1386,"orcid":11},"Refi Arioen","https:\u002F\u002Fjurnal.polsri.ac.id\u002Findex.php\u002Fteknika\u002Farticle\u002Fdownload\u002F12040\u002F4368",{"tldr":1389,"method":1390,"finding":1391,"direction":686,"opportunity":1392},"集成Laravel与Android的IoT系统，实时监测温室水培小白菜的温湿度、光照和CO2。","用Laravel构建RESTful API后端，Android与Web前端通过J","后端稳定处理异构采样率数据，24小时连续测试每小时数据完整记录率100%。","可研究断网或断电下的离线缓存与数据补传机制，提升温室IoT系统鲁棒性。","2026-10-18T23:30:07.964809Z",{"id":1395,"title":1396,"url":1397,"summary":1398,"summary_zh":1399,"content":11,"source_name":1400,"source_url":1397,"published_at":1342,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":299,"score_detail":1401,"sources":1403,"tags":1405,"primary_topic":1407,"secondary_topics":1408,"classification":1410,"search_phrases":1425,"slug":1428,"view_count":424,"doi":1429,"paper":1430,"created_at":1453},5236,"A cross-domain review of artificial intelligence techniques for iot security, computer networks, image processing and smart agriculture in connected environments","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44163-026-02500-9","The rapid digitization of modern systems has increased the demand for intelligent, adaptive, and data-driven solutions across connected systems. However, these domains face persistent challenges such as heterogeneous datasets, limited labeled data, real-time processing constraints, privacy concerns, model interpretability, and poor generalization across environments. This review examines the application of major Artificial Intelligence (AI) techniques for solving classification, prediction, optimization, anomaly detection, routing, image analysis, and decision-support problems. The empirical patterns across the literature are summarized cautiously across studies. General lessons that emerged across the reviewed domains were also presented. Overall, the review provides researchers with a concise foundation for understanding current AI methods, their practical limitations, and future research directions in the aspect of connected environments. This article has tried to clarify various aspects and overall issues related to the use of AI techniques in computer networks, image processing and computer vision, smart agriculture and related intelligent systems. The authors believe that this is a comprehensive and integrated survey of these AI techniques in a number of application domains, with an emphasis on their practical suitability, their current empirical limitations, and future research directions. Thus, the article is informative to researchers in the field and may be a guide to future research on the development of robust, interpretable, scalable, and context-aware AI-based connected systems.","现代系统的快速数字化增加了对互联系统中智能、自适应和数据驱动解决方案的需求。然而，这些领域面临诸如异构数据集、标注数据有限、实时处理约束、隐私问题、模型可解释性以及跨环境泛化能力差等持续挑战。本综述考察了主要人工智能（Artificial Intelligence, AI）技术在解决分类、预测、优化、异常检测、路由、图像分析和决策支持问题中的应用。本文谨慎地总结了文献中的实证规律。文中还提出了在所综述领域中浮现的一般性经验教训。总体而言，本综述为研究人员提供了一个简明的基础，以理解当前AI方法、其实际局限性以及互联环境方面的未来研究方向。本文试图阐明与AI技术在计算机网络、图像处理与计算机视觉、智慧农业及相关智能系统中的应用有关的各个方面和总体问题。作者认为，这是对若干应用领域中这些AI技术的全面且综合的调查，重点在于其实际适用性、当前实证局限性和未来研究方向。因此，本文对该领域的研究人员具有参考价值，并可为开发稳健、可解释、可扩展且具有情境感知能力的基于AI的互联系统的未来研究提供指导。","Discover Artificial Intelligence",{"impact":173,"substance":416,"depth":385,"authority":416,"freshness":584,"relevant":455,"comment":1402},"跨领域AI综述，涉及智慧农业但仅为其一分支，方法层面无突破性新结论，公共价值有限，不宜进入每日精选。",[1404],{"name":1400,"url":1397},[589,590,822,704,1406],"智能系统",{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},[1409],{"code":48,"slug":49,"name":50,"path":51,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":1411},{"candidates":1412,"agricultural_evidence":605},[1413,1417,1421],{"code":24,"score":909,"supported":605,"title":1414,"abstract":1415,"tags":1416},[615],[822,615,824],[590,822],{"code":16,"score":604,"supported":605,"title":1418,"abstract":1419,"tags":1420},[719],[589,719],[589],{"code":48,"score":173,"supported":605,"title":1422,"abstract":1423,"tags":1424},[622],[],[],[1426,1427],"AI 物联网安全 智慧农业","农业人工智能 物联网安全 计算机视觉 智慧农业","AI物联网安全智慧农业-5236","10.1007\u002Fs44163-026-02500-9",{"doi":1429,"openalex_id":1431,"authors":1432,"venue":1400,"cited_by_count":424,"oa_url":1397,"card":1448,"direction":686,"ingested_from":687},"W7222847048",[1433,1436,1439,1442,1445],{"name":1434,"orcid":1435},"Bashir Olaniyi Sadiq","https:\u002F\u002Forcid.org\u002F0000-0001-7868-4076",{"name":1437,"orcid":1438},"Mohammed Dahiru Buhari","https:\u002F\u002Forcid.org\u002F0000-0002-1076-216X",{"name":1440,"orcid":1441},"IBRAHIM ADABARA","https:\u002F\u002Forcid.org\u002F0000-0001-8949-7540",{"name":1443,"orcid":1444},"Enerst Edozie","https:\u002F\u002Forcid.org\u002F0000-0002-7616-0962",{"name":1446,"orcid":1447},"Asaba Shabiluh","https:\u002F\u002Forcid.org\u002F0000-0001-6342-9099",{"tldr":1449,"method":1450,"finding":1451,"direction":686,"opportunity":1452},"综述AI在物联网安全、计算机网络、图像处理与智慧农业中的应用与局限。","跨领域文献综述，归纳分类、预测、优化、异常检测等方法。","各领域普遍受限于异构数据、标注少、实时性与可解释性不足。","可聚焦农业物联网中少标注、跨环境泛化的可解释实时AI模型研究。","2026-10-18T23:30:07.908595Z",{"id":1455,"title":1456,"url":1457,"summary":1458,"summary_zh":1459,"content":11,"source_name":1460,"source_url":1457,"published_at":1147,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":893,"score_detail":1461,"sources":1463,"tags":1465,"primary_topic":1468,"secondary_topics":1469,"classification":1470,"search_phrases":1497,"slug":1500,"view_count":424,"doi":1501,"paper":1502,"created_at":1512},5159,"Integration of IoT And Satellite Data for Real-Time Agricultural Crop Stress Monitoring Using Spatio-Temporal Transformers","https:\u002F\u002Fdoi.org\u002F10.31127\u002Ftuje.1871227","This research work presents an integrated framework for real-time crop stress monitoring that combines ground-based Internet of Things (IoT) sensors and multi-temporal satellite remote sensing data. Due to growing environmental uncertainty and limited resources, the early identification of crop stress is crucial for maximizing agricultural productivity. IoT sensors yield localized, real-time measurements of key variables like soil moisture, temperature, and humidity, whereas satellite images yield wide-area vegetation and thermal indicators (e.g., NDVI, EVI, LST). The solution utilizes a Spatio-Temporal Transformer (ST-Transformer) model to integrate these heterogeneous data streams to make early crop disease and irrigation stress detection. By integrating spatial satellite imagery with temporal IoT sequences, STT leverages attention mechanisms to offer a more holistic analysis, which translates to an impressive accuracy of 88% and a solid F1-Score of 0.855. This dual capacity to concurrently handle spatial and temporal dimensions allows STT to effectively generalize across diverse crop stress categories, providing insights that are both timely and actionable","本研究工作提出了一种集成框架，用于结合地面物联网（IoT）传感器与多时相卫星遥感数据实现作物胁迫的实时监测。由于环境不确定性日益加剧且资源有限，作物胁迫的早期识别对于最大化农业生产力至关重要。物联网传感器可提供土壤湿度、温度和湿度等关键变量的局部实时测量，而卫星图像则可提供广域植被与热指标（如NDVI、EVI、LST）。该方案利用时空Transformer（ST-Transformer）模型整合这些异构数据流，以实现作物病害与灌溉胁迫的早期检测。通过将空间卫星影像与时间物联网序列相结合，STT借助注意力机制提供更全面的分析，从而实现了88%的显著准确率和0.855的稳健F1分数。这种同时处理空间与时间维度的双重能力使STT能够有效泛化于多种作物胁迫类别，提供既及时又可操作的洞察。","Turkish Journal of Engineering",{"impact":827,"substance":956,"depth":712,"authority":416,"freshness":584,"relevant":455,"comment":1462},"该研究将物联网与多时相卫星数据融合，用时空Transformer实现作物胁迫早期识别，方法新颖、指标明确，属智慧农业细分领域有实质增量的技术进展，值得入选。",[1464],{"name":1460,"url":1457},[589,590,620,1466,1467],"作物胁迫","遥感监测",{"code":48,"slug":49,"name":50,"path":51,"parent_code":7},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":1471},{"candidates":1472,"agricultural_evidence":605},[1473,1477,1481,1485,1489,1493],{"code":48,"score":361,"supported":605,"title":1474,"abstract":1475,"tags":1476},[622],[620,621,622],[620],{"code":32,"score":625,"supported":605,"title":1478,"abstract":1479,"tags":1480},[],[591,634],[1467],{"code":144,"score":637,"supported":605,"title":1482,"abstract":1483,"tags":1484},[],[772,145],[],{"code":152,"score":637,"supported":605,"title":1486,"abstract":1487,"tags":1488},[],[608,153],[],{"code":16,"score":455,"supported":581,"title":1490,"abstract":1491,"tags":1492},[],[],[589],{"code":24,"score":455,"supported":581,"title":1494,"abstract":1495,"tags":1496},[],[],[590],[1498,1499],"ST-Transformer 作物胁迫","IoT 卫星 遥感 作物监测","ST-Transformer作物胁迫-5159","10.31127\u002Ftuje.1871227",{"doi":1501,"openalex_id":1503,"authors":1504,"venue":1460,"cited_by_count":424,"oa_url":1457,"card":1507,"direction":686,"ingested_from":687},"W7222730658",[1505],{"name":1506,"orcid":11},"Sunitha Thankappan",{"tldr":1508,"method":1509,"finding":1510,"direction":686,"opportunity":1511},"融合IoT传感器与多时相卫星数据，用时空Transformer实现作物胁迫实时监测。","IoT土壤温湿度数据+卫星NDVI\u002FEVI\u002FLST，构建ST-Transform","模型准确率达88%、F1为0.855，可早期识别病害与灌溉胁迫。","可探索多源异构数据在真实田块尺度上的轻量化部署与跨区域迁移泛化。","2026-10-17T23:30:13.663599Z",{"id":1514,"title":1515,"url":1516,"summary":1517,"summary_zh":1518,"content":11,"source_name":1519,"source_url":1520,"published_at":1521,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1343,"score_detail":1522,"sources":1524,"tags":1526,"primary_topic":1528,"secondary_topics":1529,"classification":1530,"search_phrases":1559,"slug":1562,"view_count":652,"doi":1563,"paper":1564,"created_at":1575},5085,"AgriSense: IoT-Driven Precision Agriculture for National Food Sustainability and Development","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23335305","Precision agriculture integrates digital technologies to improve agricultural resource management, productivity, and sustainability, while the Internet of Things (IoT) enables real-time monitoring of soil and environmental conditions through connected sensors. In Nigeria, inefficient water and fertilizer use, declining soil health, climate variability, and limited access to timely agricultural information constrain sustainable food production. This study presents AgriSense, an IoT-driven precision agriculture platform designed to support data-informed farm management under Nigerian agricultural conditions. The study adopts a mixed-method research framework incorporating descriptive, experimental, and analytical approaches, with proposed data sources including IoT sensor measurements, farmer surveys and interviews, field observations, and relevant weather information. The AgriSense platform integrates soil-moisture, nitrogen-phosphorus-potassium (N-P-K), soil-pH, temperature, and humidity monitoring with farm and field management, sensor-status tracking, automated alerts, agronomic recommendations, and reporting functions. The demonstrated interface represents agricultural fields and crops across Asaba Demonstration Farm, Jos Highland Farm, and Sokoto Smart Farm. Its threshold-based alerts identify conditions associated with low soil moisture, low nitrogen, acidic soil, high temperature, and sensor connectivity faults, linking selected conditions to recommended management actions. The available demonstration establishes the platform’s integrated monitoring and decision-support functionality. However, because the interface identifies its environment as a live simulation, the displayed information does not independently establish improvements in crop yield, input-use efficiency, soil health, or farmer income. The study provides a technological foundation for subsequent field validation of IoT-enabled precision agriculture and its potential contribution to sustainable food security in Nigeria.","精准农业（precision agriculture）融合数字技术，以改善农业资源管理、生产力和可持续性，而物联网（Internet of Things, IoT）则通过互联传感器实现对土壤和环境条件的实时监测。在尼日利亚，水资源和肥料利用效率低下、土壤健康状况下降、气候多变以及难以及时获取农业信息等因素制约了可持续粮食生产。本研究提出AgriSense——一个由物联网驱动的精准农业平台，旨在支持尼日利亚农业条件下的数据驱动型农场管理。本研究采用混合方法研究框架，融合描述性、实验性和分析性方法，拟采用的数据来源包括物联网传感器测量数据、农民问卷调查与访谈、田间观察以及相关天气信息。AgriSense平台将土壤湿度、氮磷钾（N-P-K）、土壤pH值、温度和湿度监测与农场及田块管理、传感器状态追踪、自动警报、农艺建议和报告功能集成在一起。所展示的界面呈现了阿萨巴示范农场（Asaba Demonstration Farm）、乔斯高原农场（Jos Highland Farm）和索科托智慧农场（Sokoto Smart Farm）的农田和作物。其基于阈值的警报可识别与土壤湿度低、氮含量低、土壤酸性、高温和传感器连接故障相关的情况，并将选定情况与建议的管理措施相关联。现有演示验证了该平台的集成监测与决策支持功能。然而，由于该界面将其环境标识为实时模拟，所显示的信息并不能独立证明作物产量、投入品使用效率、土壤健康或农民收入方面的改善。本研究为后续对物联网赋能精准农业及其对尼日利亚可持续粮食安全潜在贡献的田间验证提供了技术基础。","Tech-Sphere Journal for Pure and Applied Sciences","https:\u002F\u002Fstem.techspherejournals.com\u002Findex.php\u002Ftsjpas\u002Farticle\u002Fview\u002F80","2026-10-13T00:00:00Z",{"impact":173,"substance":827,"depth":345,"authority":584,"freshness":584,"relevant":455,"comment":1523},"论文提出面向尼日利亚的IoT精准农业平台AgriSense，集成土壤水分、N-P-K、pH等监测与阈值预警，但界面为模拟环境，尚未验证增产增效，属技术原型阶段，参考价值有限。",[1525],{"name":1519,"url":1520},[589,753,1289,809,1527],"土壤墒情监测",{"code":48,"slug":49,"name":50,"path":51,"parent_code":7},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":1531},{"candidates":1532,"agricultural_evidence":605},[1533,1537,1541,1545,1551,1555],{"code":48,"score":361,"supported":605,"title":1534,"abstract":1535,"tags":1536},[622],[620,621,622],[753],{"code":16,"score":827,"supported":605,"title":1538,"abstract":1539,"tags":1540},[829],[809,829],[589,809],{"code":263,"score":625,"supported":605,"title":1542,"abstract":1543,"tags":1544},[],[1289,1300],[1289],{"code":96,"score":637,"supported":605,"title":1546,"abstract":1547,"tags":1550},[],[1548,1549],"肥料","fertilizer",[],{"code":144,"score":637,"supported":605,"title":1552,"abstract":1553,"tags":1554},[],[772,145],[],{"code":152,"score":652,"supported":605,"title":1556,"abstract":1557,"tags":1558},[],[777],[],[1560,1561],"AgriSense 物联网 精准农业","尼日利亚 智慧农业 平台","AgriSense物联网精准农业-5085","10.5281\u002Fzenodo.23335305",{"doi":1563,"openalex_id":1565,"authors":1566,"venue":1519,"cited_by_count":424,"oa_url":1520,"card":1570,"direction":686,"ingested_from":687},"W7221676154",[1567],{"name":1568,"orcid":1569},"Innocent Uzougbo Onwuegbuzie","https:\u002F\u002Forcid.org\u002F0000-0002-1582-1739",{"tldr":1571,"method":1572,"finding":1573,"direction":686,"opportunity":1574},"提出AgriSense物联网精准农业平台，用于尼日利亚农田监测与决策支持。","混合方法框架，集成土壤水分、N-P-K、pH、温湿度传感器与阈值告警。","平台实现监测与决策支持功能，但界面为模拟，尚未验证产量与效益提升。","可开展真实田间验证，量化IoT精准农业对产量、投入效率和土壤健康的影响。","2026-10-16T23:30:14.088827Z",{"id":1577,"title":1578,"url":1579,"summary":1580,"summary_zh":1581,"content":11,"source_name":1582,"source_url":1579,"published_at":1583,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":893,"score_detail":1584,"sources":1586,"tags":1588,"primary_topic":1590,"secondary_topics":1591,"classification":1593,"search_phrases":1625,"slug":1628,"view_count":424,"doi":1629,"paper":1630,"created_at":1653},5077,"A Trust-Boundary Reference Architecture for Secure Cyber-Physical Agriculture: Design and Software-Based Proof-of-Concept for Smart Irrigation","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs26206545","Smart irrigation is an operational technology (OT) problem as much as an Internet of Things (IoT) one: software decisions act directly on valves, pumps, pressure and water delivery, so a spoofed reading or a replayed command has physical consequences. This paper presents a technology-neutral reference architecture that makes those consequences controllable. It is organised into seven functional layers and three cross-cutting planes, six trust-boundary classes that fix which properties must be re-established at each crossing, and an auditable crown-asset inventory with an explicit trust-level rubric. Two governing execution principles enforce this structure: local safety authority, which keeps final actuation permission at the field gateway, remote terminal unit (RTU) or programmable logic controller (PLC), and feedback-aware execution, which treats a command as complete only when independent physical feedback confirms it. Building on process-aware and safety-supervision ideas from industrial control, the architecture makes physical confirmation a mandatory completion condition at the local enforcement point, which catches a fully authorised command that is nonetheless physically unsafe, a failure mode that authorisation checks alone cannot detect. These properties are examined through a deterministic, openly available software proof-of-concept that verifies the design against its own acceptance criteria; it is not a field validation. The software executes six attack and failure scenarios plus one nominal-operation control against an intentionally unsecured negative control and a secure controller, under identical, controlled conditions: the negative control is compromised in all 6 attack scenarios and the secure controller in 0\u002F6, with 0\u002F1 false positives on legitimate operation. Analytically, the design costs 21 bytes and 50–60% additional Long Range Wide Area Network (LoRaWAN) time-on-air per record, and 78% of single-step perturbations of the trust-scoring inputs leave asset classification unchanged: the actuator class (A2) is completely stable, and the gateway\u002FRTU\u002FPLC class (A3) is stable under 6 of 7 admissible perturbations (86%), changing only when its credential\u002Fupdate authority reaches the trust-anchor threshold. A sensor-level mapping from sampling rate, measurement drift and physical fault modes to the edge-verification controls they dictate, provisions for secure time, counter recovery and the choice between fail-closed and fail-open safe states, an illustrative hypothetical worked deployment example, and a transfer of the model to greenhouse, fertigation, livestock-watering and cold-chain settings show how the architecture could be applied in practice.","智能灌溉既是一个物联网(IoT)问题，也是一个运营技术(OT)问题：软件决策直接作用于阀门、水泵、压力和水流输送，因此被欺骗的读数或被重放的命令会产生物理后果。本文提出了一种技术中立的参考架构，使这些后果可控。该架构由七个功能层和三个横切面、六类信任边界(规定每次跨越边界时必须重新建立哪些属性)，以及一份可审计的核心资产清单(配有明确的信任等级评定标准)组成。两条主导性执行原则保障了这一结构：本地安全授权，即将最终执行许可保留在田间网关、远程终端单元(RTU)或可编程逻辑控制器(PLC)处；以及反馈感知执行，即只有当独立的物理反馈予以确认时，才将命令视为完成。该架构以工业控制中的过程感知和安全监督思想为基础，将物理确认设为本地执行点的强制性完成条件，从而能够捕获完全获得授权但在物理上仍不安全的命令——这是仅靠授权检查无法检测到的失效模式。这些特性通过一个确定性、公开可用的软件概念验证进行了检验，该验证依据其自身的验收标准对设计进行评估；这不是现场验证。该软件在相同且受控的条件下，针对一个故意不设防护的阴性对照和一个安全控制器，执行了六种攻击与故障场景以及一项标称运行对照：阴性对照在所有6种攻击场景中均被攻破，而安全控制器为0\u002F6，对合法运行的误报为0\u002F1。分析表明，该设计每条记录额外增加21字节和50–60%的LoRaWAN(LoRaWAN)空中时间，且信任评分输入的78%单步扰动不会改变资产分类：执行器类(A2)完全稳定，网关\u002FRTU\u002FPLC类(A3)在7种可允许扰动中有6种保持稳定(86%)，仅当其凭证\u002F更新权限达到信任锚阈值时才会改变。还提出了从采样率、测量漂移和物理故障模式到其所决定的边缘验证控制的传感器级映射，以及安全时间、计数器恢复和故障关闭与故障打开安全状态之间选择的保障措施。","Sensors","2026-10-15T00:00:00Z",{"impact":416,"substance":321,"depth":361,"authority":345,"freshness":584,"relevant":455,"comment":1585},"面向智慧灌溉的信任边界参考架构论文，方法新颖、含软件验证与量化开销数据，但属概念验证、非田间实证，产业影响有限，可作为技术前沿精选。",[1587],{"name":1582,"url":1579},[1589,589,753,754,703],"数字乡村",{"code":152,"slug":153,"name":154,"path":155,"parent_code":128},[1592],{"code":48,"slug":49,"name":50,"path":51,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":1594},{"candidates":1595,"agricultural_evidence":605},[1596,1600,1604,1608,1612,1617,1621],{"code":152,"score":604,"supported":605,"title":1597,"abstract":1598,"tags":1599},[153],[608,153],[754],{"code":48,"score":385,"supported":605,"title":1601,"abstract":1602,"tags":1603},[],[620,621,622],[753],{"code":324,"score":652,"supported":605,"title":1605,"abstract":1606,"tags":1607},[],[1168],[],{"code":332,"score":652,"supported":605,"title":1609,"abstract":1610,"tags":1611},[],[333],[],{"code":96,"score":652,"supported":605,"title":1613,"abstract":1614,"tags":1616},[],[1615],"fertigation",[],{"code":56,"score":69,"supported":581,"title":1618,"abstract":1619,"tags":1620},[],[],[1589,703],{"code":16,"score":455,"supported":581,"title":1622,"abstract":1623,"tags":1624},[],[],[589],[1626,1627],"智能灌溉 信任边界 参考架构","LoRaWAN 灌溉 安全","智能灌溉信任边界参考架构-5077","10.3390\u002Fs26206545",{"doi":1629,"openalex_id":1631,"authors":1632,"venue":1582,"cited_by_count":424,"oa_url":1579,"card":1648,"direction":686,"ingested_from":687},"W7222165876",[1633,1636,1639,1642,1645],{"name":1634,"orcid":1635},"Alvertos Ioannis Mourikis","https:\u002F\u002Forcid.org\u002F0009-0009-2386-2803",{"name":1637,"orcid":1638},"Iosif Polenakis","https:\u002F\u002Forcid.org\u002F0000-0002-6427-5519",{"name":1640,"orcid":1641},"Christos Sarantidis","https:\u002F\u002Forcid.org\u002F0009-0003-9486-8483",{"name":1643,"orcid":1644},"Romanos Kalamatianos","https:\u002F\u002Forcid.org\u002F0009-0000-6939-5424",{"name":1646,"orcid":1647},"Ioannis Karydis","https:\u002F\u002Forcid.org\u002F0000-0002-9470-2729",{"tldr":1649,"method":1650,"finding":1651,"direction":686,"opportunity":1652},"提出面向智慧灌溉的安全参考架构，用软件验证可抵御重放与欺骗攻击。","七层三面参考架构、信任边界分类、确定性软件概念验证与LoRaWAN开销分析。","安全控制器在6种攻击场景下0次被攻破，负控制全部被攻破，且误报率为0。","可研究边缘验证控制与采样率、漂移、故障模式的映射，以及安全时间与失效安全状态选择。","2026-10-16T23:30:08.098924Z",{"id":1655,"title":1656,"url":1657,"summary":1658,"summary_zh":1659,"content":11,"source_name":1660,"source_url":1657,"published_at":1521,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1661,"score_detail":1662,"sources":1664,"tags":1666,"primary_topic":1669,"secondary_topics":1670,"classification":1671,"search_phrases":1690,"slug":1693,"view_count":69,"doi":1694,"paper":1695,"created_at":1712},4997,"Adaptive Reinforcement Learning Models for Autonomous Resource Optimization","https:\u002F\u002Fdoi.org\u002F10.17148\u002Fijarcce.2026.15960","Adaptive Reinforcement Learning (ARL) has become a paradigm shift of autonomous optimization of resources in dynamic and uncertain settings.The conventional management methods of resources are based on inflexible models and rule-based decision making systems which are not responsive to the environmental changes, system needs or operational limitations.Adaptive reinforcement learning models deal with such constraints by allowing systems to adopt optimal resource allocation policies by a continuous interaction with the environment, adaptation through feedback and long run optimization of rewards.The models are especially useful when working with such systems as smart grids, autonomous vehicles, cloud computing, smart agriculture, and intelligent infrastructure systems.ARL ensures the efficient use of the computational, energy, and physical resources through the introduction of adaptive learning mechanisms, multi-agent coordination, and real-time optimization of decisions.Recent progress in deep reinforcement learning, multi-agent reinforcement learning as well as hybrid intelligent frameworks has greatly contributed in increasing the scale, convergence rate and accuracy of decisions.Besides, the incorporation with edge computing, digital twins, and Internet of Things (IoT) technologies allows managing the resources in a decentralized and autonomous way.This paper provides an in-depth review of adaptive reinforcement learning frameworks in the context of autonomous resource optimization, their approaches, functionality, and industrial use.Experimental evidence has shown that learning about adaptive reinforcement has great efficacy in streamlining processes and operating at a lower cost, the system remains stable and autonomous decision making with complex resource management settings is successful.","自适应强化学习（Adaptive Reinforcement Learning, ARL）已成为动态不确定环境下自主资源优化的一种范式转变。传统的资源管理方法基于刚性模型和规则驱动的决策系统，无法对环境变化、系统需求或运行限制做出响应。自适应强化学习模型通过允许系统与环境持续交互、借助反馈进行自适应调整以及长期优化奖励，从而获得最优资源配置策略，以此应对上述约束。此类模型在处理智能电网、自动驾驶车辆、云计算、智慧农业和智能基础设施系统等场景时尤为有效。ARL通过引入自适应学习机制、多智能体协调以及实时决策优化，确保计算资源、能源和物理资源的高效利用。深度强化学习、多智能体强化学习以及混合智能框架方面的最新进展，极大地推动了决策规模的扩大、收敛速度的提升和决策精度的提高。此外，与边缘计算、数字孪生和物联网（IoT）技术的融合，使得以去中心化和自主方式管理资源成为可能。本文对面向自主资源优化的自适应强化学习框架进行了深入综述，涵盖其方法、功能及工业应用。实验证据表明，自适应强化学习在简化流程和降低运行成本方面具有显著功效，系统保持稳定，并能在复杂资源管理场景中成功实现自主决策。","IJARCCE",58,{"impact":416,"substance":345,"depth":604,"authority":385,"freshness":625,"relevant":455,"comment":1663},"综述性论文，方法层面有参考价值，但缺乏具体农业场景数据与实证，公共影响有限。",[1665],{"name":1660,"url":1657},[1589,589,590,1667,1668],"强化学习","资源优化",{"code":48,"slug":49,"name":50,"path":51,"parent_code":7},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":1672},{"candidates":1673,"agricultural_evidence":605},[1674,1678,1682,1686],{"code":16,"score":385,"supported":605,"title":1675,"abstract":1676,"tags":1677},[],[589,628,719],[589],{"code":48,"score":584,"supported":605,"title":1679,"abstract":1680,"tags":1681},[],[620,835,621,622,836],[],{"code":24,"score":69,"supported":581,"title":1683,"abstract":1684,"tags":1685},[],[],[590,1667],{"code":56,"score":455,"supported":581,"title":1687,"abstract":1688,"tags":1689},[],[],[1589],[1691,1692],"自适应强化学习 资源优化","智慧农业 强化学习","自适应强化学习资源优化-4997","10.17148\u002Fijarcce.2026.15960",{"doi":1694,"openalex_id":1696,"authors":1697,"venue":1660,"cited_by_count":424,"oa_url":1657,"card":1707,"direction":686,"ingested_from":687},"W7221832990",[1698,1701,1704],{"name":1699,"orcid":1700},"Prateek Gupta","https:\u002F\u002Forcid.org\u002F0000-0003-0521-8124",{"name":1702,"orcid":1703},"Sohit Agarwal","https:\u002F\u002Forcid.org\u002F0000-0002-1280-7907",{"name":1705,"orcid":1706},"Jitesh Rajendra Neve","https:\u002F\u002Forcid.org\u002F0009-0009-2358-1147",{"tldr":1708,"method":1709,"finding":1710,"direction":884,"opportunity":1711},"综述自适应强化学习在动态不确定环境下自主资源优化的框架、方法与应用。","综述深度强化学习、多智能体强化学习及边缘计算、数字孪生、物联网融合方法。","自适应强化学习能高效分配计算、能源与物理资源，降低成本并保持系统稳定自主决策。","可将ARL用于智慧农业中水肥、农机与能源的实时协同调度，弥补农业场景多智能体与数字孪生结合的空白。","2026-10-15T23:30:12.824701Z",{"id":1714,"title":1715,"url":1716,"summary":1717,"summary_zh":1718,"content":11,"source_name":578,"source_url":1716,"published_at":1719,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1086,"score_detail":1720,"sources":1722,"tags":1724,"primary_topic":1727,"secondary_topics":1728,"classification":1731,"search_phrases":1772,"slug":1775,"view_count":424,"doi":1776,"paper":1777,"created_at":1799},4989,"Advances in micro-irrigation and supplementary irrigation in millets of dryland ecosystems","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.16312","Dryland ecosystems cover approximately 41 % of the Earth's terrestrial surface and support nearly two billion people. This review synthesises recent advances in micro-irrigation and supplementary irrigation strategies for enhancing millet productivity and resilience in dryland ecosystems. Drylands are characterised by low and erratic rainfall, high evapotranspiration, poor soil fertility and frequent drought stress, making agricultural production vulnerable to climate variability and water scarcity. Millets are drought-tolerant, climate-resilient cereals cultivated in dryland agroecosystems. Despite their resilience, millet productivity remains constrained by increasing rainfall variability, prolonged intra-seasonal dry spells and rising atmospheric aridity associated with climate change. Evidence from semi-arid and arid regions indicates that micro-irrigation systems, including drip, subsurface drip and micro-sprinkler irrigation, significantly improve yield and biomass accumulation compared to conventional surface irrigation methods. These irrigation systems also enhance water use efficiency (WUE) and nutrient use efficiency (NUE), making them highly suitable for millet production in dryland ecosystems. The integration of fertigation further improves nutrient synchronisation with crop demand, reduces leaching losses and supports soil fertility. The review also highlights optimised irrigation scheduling based on evapotranspiration, soil moisture thresholds, deficit irrigation principles and sensor-based automation. Smart irrigation technologies incorporating soil moisture sensors and Internet of Things (IoT)-based control systems show strong potential to enhance precision water management and reduce water consumption by up to 30–35 %. Their adoption, supported by technological innovation, policy frameworks and location-specific research, will promote sustainable water management and resource-use efficiency in millet-based dryland ecosystems. Future research should focus on optimising irrigation scheduling, smart technologies and cost-effective solutions for water-limited environments.","旱地生态系统约占地球陆地表面的41%，支撑着近20亿人口。本文综述了微灌与补充灌溉策略在提升旱地生态系统中小米生产力与恢复力方面的最新进展。旱地降水少且不稳定、蒸散量大、土壤肥力低、干旱胁迫频繁，使农业生产易受气候变率和缺水的影响。小米是种植于旱地农业生态系统中的耐旱、气候适应型谷物。尽管具有恢复力，小米生产力仍受降雨变率增大、季节性持续干旱期延长以及气候变化相关的大气干燥度上升的制约。来自半干旱和干旱地区的证据表明，与传统地面灌溉方式相比，微灌系统（包括滴灌、地下滴灌和微喷灌）显著提高了产量和生物量积累。这些灌溉系统还提高了水分利用效率（WUE）和养分利用效率（NUE），使其非常适合旱地生态系统中的小米生产。水肥一体化（fertigation）的整合进一步改善了养分与作物需求的同步性，减少了淋失损失，并有助于维持土壤肥力。本文还重点介绍了基于蒸散量、土壤水分阈值、亏缺灌溉原则和基于传感器的自动化的优化灌溉制度。结合土壤水分传感器和基于物联网（IoT）控制系统的智能灌溉技术，在提升精准水分管理和减少高达30%–35%的用水量方面展现出巨大潜力。在技术创新、政策框架和因地制宜研究的支持下，这些技术的采用将促进以小米为基础的旱地生态系统中的可持续水分管理和资源利用效率。未来研究应聚焦于优化灌溉制度、智能技术以及面向水资源受限环境的成本效益解决方案。","2026-10-14T00:00:00Z",{"impact":361,"substance":508,"depth":712,"authority":804,"freshness":173,"relevant":455,"comment":1721},"系统综述微灌与补充灌溉提升旱地谷子产量与水肥效率，含物联网智能灌溉节水30-35%等实质结论，对旱作智慧农业有参考价值。",[1723],{"name":578,"url":1716},[589,753,1725,1031,1726],"旱作农业","谷子高粱",{"code":152,"slug":153,"name":154,"path":155,"parent_code":128},[1729,1730],{"code":48,"slug":49,"name":50,"path":51,"parent_code":7},{"code":184,"slug":185,"name":186,"path":187,"parent_code":128},{"status":599,"locked":581,"version":4,"method":600,"evidence":1732},{"candidates":1733,"agricultural_evidence":605},[1734,1739,1743,1749,1754,1758,1763,1768],{"code":152,"score":909,"supported":605,"title":1735,"abstract":1736,"tags":1738},[153],[608,777,153,1737],"evapotranspiration",[1725,1031],{"code":48,"score":385,"supported":605,"title":1740,"abstract":1741,"tags":1742},[],[620,621,622],[753],{"code":184,"score":584,"supported":605,"title":1744,"abstract":1745,"tags":1748},[],[845,1746,1310,846,1747],"气候适应","drought",[],{"code":96,"score":637,"supported":605,"title":1750,"abstract":1751,"tags":1753},[],[1752,1615],"水肥一体化",[],{"code":144,"score":637,"supported":605,"title":1755,"abstract":1756,"tags":1757},[],[772,145],[],{"code":240,"score":652,"supported":605,"title":1759,"abstract":1760,"tags":1762},[],[1761],"millet",[],{"code":176,"score":652,"supported":605,"title":1764,"abstract":1765,"tags":1767},[],[1766],"农业生态",[],{"code":16,"score":455,"supported":581,"title":1769,"abstract":1770,"tags":1771},[],[],[589],[1773,1774],"微灌 补充灌溉 旱地 谷子","智慧灌溉 水分利用效率 谷子","微灌补充灌溉旱地谷子-4989","10.14719\u002Fpst.16312",{"doi":1776,"openalex_id":1778,"authors":1779,"venue":578,"cited_by_count":424,"oa_url":1793,"card":1794,"direction":686,"ingested_from":687},"W7221796668",[1780,1783,1786,1787,1790],{"name":1781,"orcid":1782},"M Megha","https:\u002F\u002Forcid.org\u002F0009-0006-7303-6946",{"name":1784,"orcid":1785},"N Vadivel","https:\u002F\u002Forcid.org\u002F0000-0002-5301-5324",{"name":675,"orcid":676},{"name":1788,"orcid":1789},"P. Ayyadurai","https:\u002F\u002Forcid.org\u002F0000-0003-1889-6047",{"name":1791,"orcid":1792},"PD Rathinasamy","https:\u002F\u002Forcid.org\u002F0000-0001-8793-3513","https:\u002F\u002Fhorizonepublishing.com\u002Fjournals\u002Findex.php\u002FPST\u002Farticle\u002Fdownload\u002F16312\u002F16748",{"tldr":1795,"method":1796,"finding":1797,"direction":686,"opportunity":1798},"综述旱地微灌与补充灌溉提升小米产量与水分利用效率的研究进展。","综述滴灌、地下滴灌、微喷及水肥一体化、传感器与IoT智能灌溉技术。","微灌显著提高小米产量、WUE和NUE，智能灌溉可节水30–35%。","可研究低成本IoT灌溉决策模型在小米旱地的本地化适配与长期效应。","2026-10-15T23:30:08.971713Z",{"id":1801,"title":1802,"url":1803,"summary":1804,"summary_zh":1805,"content":11,"source_name":1806,"source_url":1803,"published_at":1807,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1228,"score_detail":1808,"sources":1810,"tags":1812,"primary_topic":1815,"secondary_topics":1816,"classification":1817,"search_phrases":1847,"slug":1850,"view_count":424,"doi":11,"paper":1851,"created_at":1868},4890,"ระบบพยากรณและเฝาระวงคณภาพนำอจฉรยะตามดชนคณภาพนำ ดวยเทคนคการเรยนรของเครองและอนเทอรเนตของสรรพสง","https:\u002F\u002Fso04.tci-thaijo.org\u002Findex.php\u002FJSSE\u002Farticle\u002Fview\u002F289948","This research utilizes Internet of Things (IoT) technology and Machine Learning (ML) to develop a prototype for a smart water quality forecasting and monitoring system. The study focuses on analyzing the Reduced Water Quality Index (rWQI) based on water temperature, pH, and Total Dissolved Solids (TDS). To forecast water quality 1 to 6 hours in advance, the performance of four predictive models was evaluated and compared: Extremely Randomized Trees (ET), Random Forest (RF), Simple Linear Regression (LR), and Multiple Linear Regression (MLR). The efficiency of these algorithms was validated using four key evaluation metrics: Coefficient of Determination (R2), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Symmetric Mean Absolute Percentage Error (sMAPE). The experimental results indicated that the predictive performance of the models varied significantly depending on the forecasting horizon. In the short-term horizon (1–3 hours ahead), the RF model achieved the highest performance, with an R² of 0.566 and an MAE of 5.215 in the first hour. Conversely, for long-term forecasting (4–6 hours ahead), all models exhibited a significant drop in performance (R² \u003C 0), yielding higher prediction errors than the Mean Baseline. This long-term limitation stems from high water quality volatility in real-world environments combined with error accumulation in multi-step ahead forecasting. In conclusion, these findings emphasize that aligning model selection with the specific forecasting horizon is critical for enhancing system efficiency and stability. The developed model demonstrates high accuracy and suitability for short-term early warnings within 3 hours. This knowledge enables operators to plan water quality management with precision and speed, thereby reducing operational costs and the risk of aquatic livestock loss. Furthermore, this system serves as a technological prototype in digital agriculture, elevating conventional water quality monitoring into proactive management through intelligent technology.","本研究利用物联网（IoT）技术与机器学习（ML），开发了一套智能水质预测与监测系统原型。研究聚焦于基于水温、pH值和总溶解固体（TDS）的简化水质指数（rWQI）分析。为提前1至6小时预测水质，本研究评估并比较了四种预测模型的性能：极端随机树（ET）、随机森林（RF）、简单线性回归（LR）和多元线性回归（MLR）。采用四项关键评价指标验证了这些算法的有效性：决定系数（R²）、平均绝对误差（MAE）、平均绝对百分比误差（MAPE）和对称平均绝对百分比误差（sMAPE）。实验结果表明，各模型的预测性能因预测时间跨度不同而存在显著差异。在短期预测（提前1至3小时）中，RF模型表现最佳，第一小时的R²为0.566，MAE为5.215。相反，在长期预测（提前4至6小时）中，所有模型性能均显著下降（R² \u003C 0），预测误差高于均值基线。这一长期局限性源于真实环境中水质的高波动性以及多步超前预测中的误差累积。综上所述，这些发现强调，将模型选择与特定预测时间跨度相匹配对于提升系统效率与稳定性至关重要。所开发的模型具有较高精度，适用于3小时内的短期预警。这一认识使操作人员能够精准、快速地规划水质管理，从而降低运营成本和水产养殖损失风险。此外，该系统可作为数字农业中的技术原型，通过智能技术将传统水质监测提升为主动管理。","Journal of Science and Science Education (JSSE)","2026-10-12T00:00:00Z",{"impact":416,"substance":508,"depth":712,"authority":804,"freshness":173,"relevant":455,"comment":1809},"论文将物联网与机器学习结合用于水产养殖水质短期预警，方法对比扎实、结论明确，但属细分技术原型，影响范围有限。",[1811],{"name":1806,"url":1803},[589,613,620,1813,1814],"水产养殖","水质监测",{"code":48,"slug":49,"name":50,"path":51,"parent_code":7},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":1818},{"candidates":1819,"agricultural_evidence":605},[1820,1824,1830,1834,1839,1843],{"code":48,"score":385,"supported":605,"title":1821,"abstract":1822,"tags":1823},[],[620,621,622],[620],{"code":16,"score":625,"supported":605,"title":1825,"abstract":1826,"tags":1829},[],[1827,1828],"数字农业","digital agriculture",[589],{"code":24,"score":625,"supported":605,"title":1831,"abstract":1832,"tags":1833},[],[613,614],[613],{"code":388,"score":400,"supported":605,"title":1835,"abstract":1836,"tags":1838},[],[1837],"水产",[1813],{"code":332,"score":652,"supported":605,"title":1840,"abstract":1841,"tags":1842},[],[333],[],{"code":419,"score":455,"supported":581,"title":1844,"abstract":1845,"tags":1846},[],[],[1814],[1848,1849],"物联网 机器学习 水质预测","rWQI 水质监测 水产养殖","物联网机器学习水质预测-4890",{"doi":11,"openalex_id":1852,"authors":1853,"venue":1806,"cited_by_count":424,"oa_url":1803,"card":1863,"direction":686,"ingested_from":687},"W7221732805",[1854,1856,1858,1860],{"name":1855,"orcid":11},"Wanarase Sinhashthita",{"name":1857,"orcid":11},"Tossaporn Alherbe",{"name":1859,"orcid":11},"Kamol Chareonsri",{"name":1861,"orcid":1862},"Chayaporn Kaensar","https:\u002F\u002Forcid.org\u002F0009-0005-3330-8532",{"tldr":1864,"method":1865,"finding":1866,"direction":686,"opportunity":1867},"利用物联网与机器学习构建水质预测监测系统，提前1-6小时预测rWQI。","基于水温、pH、TDS数据，比较ET、RF、LR、MLR四种模型预测性能。","短期（1-3小时）RF最优，长期（4-6小时）所有模型性能显著下降。","可探索融合时序深度学习与误差校正，提升长期水质预测精度与鲁棒性。","2026-10-14T23:30:13.314647Z",{"id":1870,"title":1871,"url":1872,"summary":1873,"summary_zh":1874,"content":11,"source_name":1875,"source_url":1872,"published_at":1807,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1876,"score_detail":1877,"sources":1879,"tags":1881,"primary_topic":1883,"secondary_topics":1884,"classification":1886,"search_phrases":1905,"slug":1908,"view_count":424,"doi":1909,"paper":1910,"created_at":1928},4888,"A MATLAB-based AI-IoT simulation framework for LSTM-driven smart solar irrigation and renewable energy management","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-74935-0","Smart irrigation offers a great avenue for better water use efficiency, and farming sustainability. However, most of the studies conducted so far tend to concentrate on the monitoring systems and platforms of the Internet of Things (IoT) or on the prediction models based on Artificial Intelligence (AI) (but not on both). To address this gap, this research study proposes an Artificial Intelligence (AI)-Internet of Things (IoT) system to predict the water consumption requirements and control the energy produced by a PV system along with a battery State of Charge (SOC) management system, Maximum Power Point Tracking (MPPT) algorithm, adaptive pump-speed control, and conceptual IoT communication architecture. A dataset of real environmental data from the NASA POWER database was used to test the framework for Egypt’s New Delta region. The LSTM model was able to classify the irrigation pump operating states with an accuracy of 96.11%, a recall of 0.9820, a precision of 0.9100, and an F1-score of 0.9446. It had a better performance than the two models, Artificial Neural Network (ANN) and Support Vector Machine (SVM) when compared and robustness testing with various independent training runs showed a stable performance in predicting. An estimated annual water-saving rate of 28.57% was obtained relative to the assumed conventional fixed-schedule baseline, while motor energy use was reduced by 74.74% through adaptive pump-speed control. A sensitivity analysis was also performed, showing that the model was stable to moderate variations in environmental and operating conditions. Combined, these findings suggest that the use of AI-based modelling for renewable energy management in the context of an irrigation simulation model built in MATLAB could be a valuable asset in improving irrigation efficiency and lowering water and energy use, which is a promising start for future implementation of hardware versions of intelligent, solar-powered irrigation devices.","智能灌溉为提高用水效率和农业可持续性提供了重要途径。然而，迄今为止大多数研究倾向于集中在物联网（IoT）的监测系统和平台，或基于人工智能（AI）的预测模型（但很少两者兼顾）。为弥补这一空白，本研究提出了一种人工智能（AI）-物联网（IoT）系统，用于预测用水需求并控制光伏系统产生的能量，同时包含电池荷电状态（SOC）管理系统、最大功率点跟踪（MPPT）算法、自适应泵速控制以及概念性物联网通信架构。研究采用NASA POWER数据库中的真实环境数据集，在埃及新三角洲地区对该框架进行了测试。LSTM模型能够对灌溉泵运行状态进行分类，准确率达到96.11%，召回率为0.9820，精确率为0.9100，F1分数为0.9446。与人工神经网络（ANN）和支持向量机（SVM）两个模型相比，该模型表现更优，且通过多次独立训练运行进行的鲁棒性测试表明其在预测方面具有稳定性能。相对于假设的传统固定调度基线，估计年节水率为28.57%，而通过自适应泵速控制，电机能耗降低了74.74%。研究还进行了敏感性分析，表明该模型对环境和运行条件的中等程度变化具有稳定性。综合来看，这些发现表明，在MATLAB中构建的灌溉仿真模型背景下，将基于AI的建模用于可再生能源管理，可能是提高灌溉效率、降低水和能源消耗的有价值手段，这为未来实现智能太阳能灌溉设备的硬件版本提供了一个有前景的开端。","Scientific Reports",78,{"impact":827,"substance":321,"depth":361,"authority":345,"freshness":173,"relevant":455,"comment":1878},"该研究提出AI-IoT融合的太阳能灌溉仿真框架，LSTM预测精度达96.11%，节水28.57%、节能74.74%，方法新颖且数据可靠，对智慧农业与农业能源管理有参考价值。",[1880],{"name":1875,"url":1872},[589,590,754,900,1882],"光伏农业",{"code":48,"slug":49,"name":50,"path":51,"parent_code":7},[1885],{"code":152,"slug":153,"name":154,"path":155,"parent_code":128},{"status":599,"locked":581,"version":4,"method":600,"evidence":1887},{"candidates":1888,"agricultural_evidence":605},[1889,1893,1897,1901],{"code":48,"score":712,"supported":605,"title":1890,"abstract":1891,"tags":1892},[622],[620,621,622],[],{"code":152,"score":827,"supported":605,"title":1894,"abstract":1895,"tags":1896},[153],[608,153],[754,900],{"code":24,"score":400,"supported":605,"title":1898,"abstract":1899,"tags":1900},[],[615],[590],{"code":16,"score":455,"supported":581,"title":1902,"abstract":1903,"tags":1904},[],[],[589],[1906,1907],"MATLAB AI-IoT 太阳能灌溉","LSTM 智能灌溉 埃及新三角洲","MATLABAI-IoT太阳能灌溉-4888","10.1038\u002Fs41598-026-74935-0",{"doi":1909,"openalex_id":1911,"authors":1912,"venue":1875,"cited_by_count":424,"oa_url":1922,"card":1923,"direction":686,"ingested_from":687},"W7221571268",[1913,1916,1919],{"name":1914,"orcid":1915},"Ahmed Mashaly","https:\u002F\u002Forcid.org\u002F0009-0007-7009-4531",{"name":1917,"orcid":1918},"Ragab Abdelaziz El-Sehiemy","https:\u002F\u002Forcid.org\u002F0000-0002-3340-4031",{"name":1920,"orcid":1921},"Mohamed M. Elgohary","https:\u002F\u002Forcid.org\u002F0000-0003-4441-7812","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-74935-0.pdf",{"tldr":1924,"method":1925,"finding":1926,"direction":686,"opportunity":1927},"提出MATLAB中AI-IoT仿真框架，用LSTM预测灌溉需水并管理光伏与电池。","NASA POWER环境数据，LSTM对比ANN\u002FSVM，含MPPT与自适应泵速","LSTM分类准确率96.11%，节水28.57%，电机能耗降74.74%。","可延伸至真实硬件部署与多区域验证，并融合实时气象与土壤墒情数据。","2026-10-14T23:30:13.060384Z",{"technologies":1930,"keywords":1966},[1931,1933,1935,1937,1939,1940,1941,1943,1944,1945,1947,1949,1950,1952,1954,1956,1958,1959,1960,1961,1963,1965],{"name":590,"count":1932},91,{"name":753,"count":1934},59,{"name":620,"count":1936},52,{"name":809,"count":1938},48,{"name":835,"count":524},{"name":628,"count":173},{"name":1942,"count":637},"区块链",{"name":613,"count":637},{"name":591,"count":637},{"name":1946,"count":408},"农业传感器",{"name":1948,"count":408},"联邦学习",{"name":1467,"count":408},{"name":1951,"count":400},"传感器",{"name":1953,"count":400},"农业机器人",{"name":1955,"count":652},"农业遥感",{"name":1957,"count":69},"基因编辑",{"name":1092,"count":69},{"name":1667,"count":69},{"name":822,"count":69},{"name":1962,"count":455},"人工智能",{"name":1964,"count":455},"农业大模型",{"name":821,"count":455},[1967,1969,1970,1971,1972,1973,1975,1976,1977,1978,1980,1981],{"name":589,"count":1968},145,{"name":754,"count":604},{"name":755,"count":173},{"name":593,"count":625},{"name":1827,"count":637},{"name":1974,"count":637},"无线传感器网络",{"name":900,"count":637},{"name":1589,"count":408},{"name":703,"count":408},{"name":1979,"count":400},"数据治理",{"name":963,"count":652},{"name":985,"count":652}]