[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2775":3,"related-2775":135},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":33,"secondary_topics":39,"classification":40,"search_phrases":100,"slug":103,"view_count":104,"doi":105,"paper":106,"created_at":134},2775,"Digital-enabled life cycle thinking for sustainable food systems: aligning with sustainable development goals","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-63273-w","The increasing complexity of global food systems, coupled with climate change, resource scarcity, and socio-economic pressures, necessitates advanced sustainability assessment approaches. While life cycle thinking (LCT) provides a comprehensive framework, its practical application is constrained by data limitations, methodological uncertainty, and the limited ability of conventional models to capture dynamic operational conditions. This study proposes a digitally integrated LCT framework that combines artificial intelligence (AI), blockchain, internet of things (IoT), and remote sensing to enhance data accuracy, transparency, and real-time decision-making in food supply chains. A key contribution of this research is the introduction of two novel constructs, the digital trust index (DTI) and proof-of-climate impact (PCI) which quantify stakeholder trust and provide verifiable evidence linking digital interventions to measurable environmental outcomes. The DTI is conceptualized as a composite measure of stakeholder trust, integrating both digital infrastructure capabilities and governance-based trust mechanisms. Scenario analysis demonstrates that integrated digital and governance systems can achieve up to 34% climate impact reduction (CIR), while Monte Carlo simulation results indicate an average CIR of approximately 20%, confirming the robustness of the framework under uncertainty. The study reveals that SDG 12 (23%) and SDG 9 (20%) dominate digital LCT research in food systems, followed by SDG 2 and SDG 13, while biodiversity-focused goals (SDG 14 & SDG 15) receive less attention. The proposed SDG oriented framework integrates digital technologies across LCT phases, enabling real-time and transparent sustainability assessment while enhancing the monitoring of biodiversity impacts. The findings highlight that digitalization, when coupled with governance and sustainable development goal-oriented metrics, enables more transparent, adaptive, and impact-driven sustainability assessment in food systems.","全球粮食系统日益复杂，加之气候变化、资源稀缺和社会经济压力，迫切需要先进的可持续性评估方法。尽管生命周期思维（LCT）提供了全面的框架，但其实际应用受到数据限制、方法学不确定性以及传统模型捕捉动态运行条件能力有限的制约。本研究提出了一种数字化集成的LCT框架，将人工智能（AI）、区块链、物联网（IoT）和遥感相结合，以提升食品供应链中的数据准确性、透明度和实时决策能力。本研究的一项关键贡献是引入了两个新颖构念——数字信任指数（DTI）和气候影响证明（PCI），用以量化利益相关者信任，并提供可验证的证据，将数字干预与可衡量的环境成果相联系。DTI被概念化为利益相关者信任的综合度量，整合了数字基础设施能力和基于治理的信任机制。情景分析表明，集成的数字与治理系统可实现高达34%的气候影响减排（CIR），而蒙特卡洛模拟结果显示平均CIR约为20%，证实了该框架在不确定性下的稳健性。研究揭示，SDG 12（23%）和SDG 9（20%）在食品系统中的数字LCT研究中占主导地位，其次是SDG 2和SDG 13，而聚焦生物多样性的目标（SDG 14和SDG 15）受到的关注较少。所提出的面向SDG的框架将数字技术整合到LCT各阶段，实现实时、透明的可持续性评估，同时增强对生物多样性影响的监测。研究结果表明，数字化在与治理和面向可持续发展目标的指标相结合时，能够在食品系统中实现更透明、更具适应性和以影响为导向的可持续性评估。",null,"Scientific Reports","2026-09-15T00:00:00Z","论文",10,false,86,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},22,23,19,14,8,1,"提出数字信任指数与气候影响证明等新构念，量化数字技术对食物系统可持续性的减排贡献，方法新颖、结论可靠，对农业信息化与绿色转型具有较强参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31,32],"智慧农业","农业人工智能","农业遥感","可持续发展","区块链溯源","食物系统",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[],{"status":41,"locked":14,"version":42,"method":43,"evidence":44},"classified","2026-10-22.2","rules",{"candidates":45,"agricultural_evidence":48},[46,55,63,71,78,85,90,95],{"code":34,"score":47,"supported":48,"title":49,"abstract":50,"tags":54},9,true,[],[51,52,53],"物联网","internet of things","IoT",[],{"code":56,"score":57,"supported":48,"title":58,"abstract":59,"tags":62},"I3",7,[],[60,61],"遥感","remote sensing",[29],{"code":64,"score":65,"supported":48,"title":66,"abstract":67,"tags":70},"H6",6,[],[68,69],"生物多样性","biodiversity",[],{"code":72,"score":65,"supported":48,"title":73,"abstract":74,"tags":77},"H7",[],[75,76],"气候变化","climate change",[],{"code":79,"score":80,"supported":48,"title":81,"abstract":82,"tags":84},"I2",4,[],[83],"artificial intelligence",[28],{"code":86,"score":22,"supported":14,"title":87,"abstract":88,"tags":89},"E2",[],[],[32],{"code":91,"score":22,"supported":14,"title":92,"abstract":93,"tags":94},"E4",[],[],[31],{"code":96,"score":22,"supported":14,"title":97,"abstract":98,"tags":99},"I1",[],[],[27],[101,102],"农业人工智能 区块链溯源 可持续发展 农业遥感","农业人工智能 区块链溯源","农业人工智能区块链溯源可持续发展农业遥感-2775",3,"10.1038\u002Fs41598-026-63273-w",{"doi":105,"openalex_id":107,"authors":108,"venue":10,"cited_by_count":124,"oa_url":125,"card":126,"direction":132,"ingested_from":133},"W7213239181",[109,111,114,116,118,121],{"name":110,"orcid":9},"S. U. Parvathy",{"name":112,"orcid":113},"Vysakh Kani Kolil","https:\u002F\u002Forcid.org\u002F0000-0003-2035-3439",{"name":115,"orcid":9},"Raghu Raman",{"name":117,"orcid":9},"Nripendra P. Rana",{"name":119,"orcid":120},"Sasangan Ramanathan","https:\u002F\u002Forcid.org\u002F0000-0002-7459-0934",{"name":122,"orcid":123},"Krishnashree Achuthan","https:\u002F\u002Forcid.org\u002F0000-0003-2618-0882",0,"https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-63273-w_reference.pdf",{"tldr":127,"method":128,"finding":129,"direction":130,"opportunity":131},"提出融合AI、区块链、物联网与遥感的数字生命周期思维框架，用于食品系统可持续性评估。","整合AI、区块链、IoT与遥感，构建数字信任指数和气候影响证明，用情景分析与蒙特","数字与治理结合可使气候影响降低最高34%，平均约20%；SDG12和SDG9主导相关研究。","农业绿色发展与碳","可探索数字信任指数与气候影响证明在农业食品供应链中的实证验证及生物多样性目标监测。","智慧农业 \u002F 农业物联网","openalex","2026-09-17T23:30:15.308110Z",{"total":65,"page":22,"page_size":65,"items":136},[137,196,280,350,452,539],{"id":138,"title":139,"url":140,"summary":141,"summary_zh":9,"content":9,"source_name":142,"source_url":140,"published_at":143,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":144,"score_detail":145,"sources":149,"tags":151,"primary_topic":153,"secondary_topics":154,"classification":155,"search_phrases":176,"slug":179,"view_count":180,"doi":181,"paper":182,"created_at":195},1270,"Tendencias en agricultura y silvicultura de precisión: un análisis bibliométrico de la integración de datos, el apoyo a la toma de decisiones y la sostenibilidad","https:\u002F\u002Fdoi.org\u002F10.69639\u002Farandu.v13i3.2452","Este estudio analiza las tendencias y la estructura conceptual de la investigación sobre agricultura y silvicultura de precisión, con énfasis en la integración de datos, los sistemas de apoyo a la toma de decisiones y la sostenibilidad. Se desarrolló un análisis bibliométrico de documentos indexados en Web of Science y Scopus, recuperados mediante una estrategia de búsqueda orientada a identificar estudios relacionados con tecnologías digitales, inteligencia artificial, teledetección, IoT, modelización predictiva y gestión sostenible. El proceso de selección documental siguió criterios de depuración basados en PRISMA, clasificación por cuartiles y revisión de metadatos, obteniéndose un corpus final de 411 documentos publicados entre 2003 y 2026. Los resultados evidencian un crecimiento acelerado de la producción científica, con una alta concentración de literatura reciente y una estructura temática organizada en torno a temas motores, básicos, de nicho y emergentes. Los clústeres de precision agriculture y smart agriculture se identificaron como núcleos articuladores del campo, mientras que inteligencia artificial, IoT y seguridad alimentaria funcionaron como bases transversales. Asimismo, la agricultura digital, la gobernanza tecnológica, el aprendizaje por refuerzo y las redes neuronales convolucionales aparecen como líneas especializadas o en desarrollo. Se concluye que el mapa temático permite reconocer patrones de centralidad y densidad que diferencian tendencias consolidadas, especializadas y emergentes, aportando una visión ordenada para orientar futuras investigaciones sobre sistemas agroforestales inteligentes, adaptativos y sostenibles.","Arandu-UTIC.","2026-08-29T00:00:00Z",69,{"impact":146,"substance":147,"depth":146,"authority":13,"freshness":104,"relevant":22,"comment":148},18,20,"基于411篇文献的计量分析，揭示精准农业与林业研究趋势，对智慧农业领域有参考价值。",[150],{"name":142,"url":140},[27,28,29,30,152],"精准农业",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":156},{"candidates":157,"agricultural_evidence":48},[158,164,168,172],{"code":96,"score":21,"supported":48,"title":159,"abstract":160,"tags":163},[],[161,162],"smart agriculture","precision agriculture",[27,152],{"code":34,"score":104,"supported":48,"title":165,"abstract":166,"tags":167},[],[53],[],{"code":79,"score":22,"supported":14,"title":169,"abstract":170,"tags":171},[],[],[28],{"code":56,"score":22,"supported":14,"title":173,"abstract":174,"tags":175},[],[],[29],[177,178],"农业人工智能 可持续发展 农业遥感 智慧农业","农业人工智能 可持续发展","农业人工智能可持续发展农业遥感智慧农业-1270",2,"10.69639\u002Farandu.v13i3.2452",{"doi":181,"openalex_id":183,"authors":184,"venue":142,"cited_by_count":124,"oa_url":188,"card":189,"direction":132,"ingested_from":133},"W7204668132",[185],{"name":186,"orcid":187},"Carlos Arturo Carvajal Chávez","https:\u002F\u002Forcid.org\u002F0000-0002-2781-6953","https:\u002F\u002Frevista.utic.edu.py\u002Frevista.ojs\u002Findex.php\u002Frevistas\u002Farticle\u002Fdownload\u002F2452\u002F3991",{"tldr":190,"method":191,"finding":192,"direction":193,"opportunity":194},"通过文献计量分析，梳理精准农业与林业的研究趋势、主题结构和热点方向。","基于Web of Science和Scopus，采用PRISMA筛选，对411篇","精准农业和智慧农业是核心主题，AI、IoT和食品安全为基础，数字农业、强化学习等为新兴方向。","农业人工智能与决策模型","可深入探索强化学习与卷积神经网络在智能农林业系统中的应用，结合数据集成与可持续性，填补新兴技术整合研究的空白。","2026-09-01T04:03:13.088508Z",{"id":197,"title":198,"url":199,"summary":200,"summary_zh":201,"content":9,"source_name":202,"source_url":199,"published_at":203,"category":12,"cover_url":9,"hotness":204,"is_selected":14,"score":205,"score_detail":206,"sources":211,"tags":215,"primary_topic":218,"secondary_topics":224,"classification":226,"search_phrases":257,"slug":260,"view_count":124,"doi":261,"paper":262,"created_at":279},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。","Zenodo (CERN European Organization for Nuclear Research)","2026-11-25T00:00:00Z",25,73,{"impact":17,"substance":207,"depth":208,"authority":209,"freshness":124,"relevant":22,"comment":210},21,17,13,"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。",[212,213],{"name":202,"url":199},{"name":202,"url":214},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527988",[27,28,216,31,217],"农业物联网","精准灌溉",{"code":219,"slug":220,"name":221,"path":222,"parent_code":223},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[225],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"status":41,"locked":14,"version":42,"method":43,"evidence":227},{"candidates":228,"agricultural_evidence":48},[229,235,239,243,249,253],{"code":219,"score":230,"supported":48,"title":231,"abstract":232,"tags":234},15,[220],[233,220],"灌溉",[217],{"code":34,"score":13,"supported":48,"title":236,"abstract":237,"tags":238},[],[51,52,53],[216],{"code":79,"score":80,"supported":48,"title":240,"abstract":241,"tags":242},[],[83],[28],{"code":244,"score":104,"supported":48,"title":245,"abstract":246,"tags":248},"G2",[],[247],"小农户",[],{"code":91,"score":22,"supported":14,"title":250,"abstract":251,"tags":252},[],[],[31],{"code":96,"score":22,"supported":14,"title":254,"abstract":255,"tags":256},[],[],[27],[258,259],"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":261,"openalex_id":263,"authors":264,"venue":202,"cited_by_count":124,"oa_url":199,"card":273,"direction":132,"ingested_from":133},"W7223034914",[265,268,270],{"name":266,"orcid":267},"A. Shaji George","https:\u002F\u002Forcid.org\u002F0000-0002-8677-3682",{"name":269,"orcid":9},"Dr.T.Baskar",{"name":271,"orcid":272},"P. Balaji Srikaanth","https:\u002F\u002Forcid.org\u002F0000-0003-4717-7367",{"tldr":274,"method":275,"finding":276,"direction":277,"opportunity":278},"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":281,"title":282,"url":283,"summary":284,"summary_zh":285,"content":9,"source_name":286,"source_url":283,"published_at":287,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":288,"score_detail":289,"sources":293,"tags":295,"primary_topic":298,"secondary_topics":302,"classification":308,"search_phrases":330,"slug":333,"view_count":104,"doi":334,"paper":335,"created_at":349},3839,"A remote sensing image processing method based on edge computing and deep learning","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43926-026-00508-2","The rapid advancement of Artificial Intelligence (AI) and edge computing has increased the demand for accurate and low-latency remote sensing image classification; however, conventional cloud-centric approaches face challenges related to processing latency, communication overhead, and efficient analysis of diverse land-cover scenes. This research proposes a remote sensing image processing method based on edge computing and deep learning (DL), integrating an Adaptive Chameleon Swarm-tuned Efficient Three-Dimensional Convolutional Neural Network (ACS-E3DCNN). The Remote Sensing Image Dataset obtained from Kaggle contains 5,631 RGB images categorized into five land-cover classes, namely agriculture, barren land, urban, vegetation, and water. During preprocessing, image normalization, adaptive histogram equalization, and non-local means filtering are applied to reduce noise, enhance contrast, and improve image quality. Subsequently, Principal Component Analysis (PCA) is employed for dimensionality refinement. The Efficient Three-Dimensional Convolutional Neural Network captures discriminative image representations with reduced parameter complexity, while the Adaptive Chameleon Swarm algorithm optimizes critical hyperparameters, including learning rate, kernel depth, and filter dimensions, to improve convergence and classification performance. Experimental results implemented using Python 3.10.1 demonstrate that the proposed model outperforms existing approaches, achieving an accuracy of 98.10%, F1-score of 96.85%, PSNR of 38.05, SSIM of 0.97, and latency of 85 ms under the 80:20 data split, with a statistically significant paired t-test result ( p \u003C 0.05). The proposed model provides an intelligent, reliable, and efficient approach for remote sensing image classification, supporting accurate land-cover mapping, environmental monitoring, and low-latency edge-enabled image processing.","人工智能（AI）与边缘计算的快速发展，提升了对高精度、低延迟遥感图像分类的需求；然而，传统的以云为中心的方法在处理延迟、通信开销以及高效分析多样化土地覆盖场景方面面临挑战。本研究提出了一种基于边缘计算与深度学习（DL）的遥感图像处理方法，集成了自适应变色龙群优化高效三维卷积神经网络（ACS-E3DCNN）。从Kaggle获取的遥感图像数据集包含5，631幅RGB图像，分为农业、裸地、城市、植被和水体五个土地覆盖类别。在预处理阶段，采用图像归一化、自适应直方图均衡化和非局部均值滤波来降低噪声、增强对比度并提升图像质量。随后，采用主成分分析（PCA）进行降维优化。高效三维卷积神经网络以较低的参数复杂度捕获具有判别性的图像表征，而自适应变色龙群算法则优化关键超参数，包括学习率、卷积核深度和滤波器尺寸，以改善收敛性和分类性能。使用Python 3.10.1实现的实验结果表明，所提模型优于现有方法，在80：20数据划分下达到了98.10%的准确率、96.85%的F1分数、38.05的PSNR、0.97的SSIM以及85 ms的延迟，配对t检验结果具有统计学显著性（p \u003C 0.05）。所提模型为遥感图像分类提供了一种智能、可靠且高效的方法，支持精确的土地覆盖制图、环境监测以及低延迟的边缘图像处理。","Discover Internet of Things","2026-09-29T00:00:00Z",67,{"impact":290,"substance":146,"depth":291,"authority":290,"freshness":47,"relevant":22,"comment":292},12,16,"该论文提出边缘计算与深度学习结合的遥感图像分类方法，在农业土地覆盖识别上精度高、延迟低，对智慧农业遥感应用有参考价值，但属方法类研究，产业影响有限。",[294],{"name":286,"url":283},[27,28,29,296,297],"边缘计算","土地覆盖分类",{"code":79,"slug":299,"name":300,"path":301,"parent_code":38},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[303,307],{"code":56,"slug":304,"name":305,"path":306,"parent_code":38},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"status":41,"locked":14,"version":42,"method":43,"evidence":309},{"candidates":310,"agricultural_evidence":48},[311,317,321,326],{"code":79,"score":146,"supported":48,"title":312,"abstract":314,"tags":316},[313],"deep learning",[315,313,83],"深度学习",[28],{"code":56,"score":230,"supported":48,"title":318,"abstract":319,"tags":320},[61],[60,61],[29],{"code":34,"score":230,"supported":48,"title":322,"abstract":324,"tags":325},[323],"edge computing",[296,323],[296],{"code":96,"score":22,"supported":14,"title":327,"abstract":328,"tags":329},[],[],[27],[331,332],"遥感图像 土地覆盖 分类","边缘计算 深度学习 遥感","遥感图像土地覆盖分类-3839","10.1007\u002Fs43926-026-00508-2",{"doi":334,"openalex_id":336,"authors":337,"venue":286,"cited_by_count":124,"oa_url":283,"card":343,"direction":347,"ingested_from":133},"W7214769118",[338,340],{"name":339,"orcid":9},"Yuli Song",{"name":341,"orcid":342},"Hong Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-3176-3994",{"tldr":344,"method":345,"finding":346,"direction":347,"opportunity":348},"提出边缘计算与深度学习结合的遥感图像分类方法，用改进3D CNN实现低延迟高精度土地覆盖识别。","ACS-E3DCNN模型，结合自适应变色龙群优化超参数，用Kaggle 5631","准确率98.10%、F1为96.85%、延迟85ms，优于现有方法，支持低延迟边缘处理。","农业遥感与作物表型","可探索边缘端轻量化模型在农田实时监测中的部署，结合多光谱与时序数据提升作物分类泛化性。","2026-09-30T23:30:27.868009Z",{"id":351,"title":352,"url":353,"summary":354,"summary_zh":355,"content":9,"source_name":356,"source_url":353,"published_at":357,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":358,"score_detail":359,"sources":361,"tags":363,"primary_topic":365,"secondary_topics":366,"classification":369,"search_phrases":423,"slug":426,"view_count":124,"doi":427,"paper":428,"created_at":451},5564,"Dynamic thermal imaging for crop water stress detection across phenological stages: Enhancing irrigation scheduling precision","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.17728","Thermal remote sensing and derived indices such as the crop water stress index (CWSI) can assess crop water status and support precision irrigation. However, most operational and artificial intelligence (AI)-based approaches rely on fixed, season-long thresholds to interpret canopy–air temperature differences and often overlook the influence of crop phenology. This review synthesises evidence from grapevine, wheat, maize, potato and several other crops on how developmental stage affects the non-water-stressed baseline (NWSB), CWSI thresholds and irrigation-scheduling algorithms. It covers CWSI theory and estimation practices; crop-specific, phenology-dependent behaviour of baselines and thresholds; a comparison of threshold-based, machine learning, Internet of Things (IoT), digital twin and Food and Agriculture Organization (FAO)-56 approaches with respect to crop phenology; and a conceptual framework for phenology-aware scheduling. Across the reviewed crops, baseline behaviour generally varied systematically across the growing season, the relationship between CWSI and crop water status generally strengthened after canopy closure and CWSI measured during reproductive stages was generally most closely related to yield. A quantitative audit of 56 AI-, thermal-imaging-, or CWSI-based studies found that 78.6 % either did not explicitly incorporate crop phenology or relied on day of year as a temporal proxy, whereas only 5.4 % explicitly encoded phenological stage. The proposed framework considers phenology explicitly in baseline estimation, threshold setting and data fusion or machine learning for irrigation scheduling; the framework will require multi-year, multi-site field validation before operational use. Future research should prioritise standardised phenology definitions and long-term, multi-location studies to establish and validate stage-specific parameters.","热红外遥感及其衍生的作物水分胁迫指数（crop water stress index, CWSI）等指标可评估作物水分状况并支持精准灌溉。然而，大多数业务化方法和基于人工智能（artificial intelligence, AI）的方法依赖固定的全生育期阈值来解释冠层—空气温差，且往往忽视作物物候的影响。本文综述了葡萄、小麦、玉米、马铃薯及其他若干作物在发育阶段如何影响非水分胁迫基线（non-water-stressed baseline, NWSB）、CWSI阈值和灌溉调度算法的相关证据。综述内容涵盖CWSI理论与估算实践；基线和阈值的作物特异性、物候依赖性行为；基于阈值、机器学习、物联网（Internet of Things, IoT）、数字孪生及联合国粮农组织（Food and Agriculture Organization, FAO）-56等方法在作物物候方面的比较；以及物候感知调度的概念框架。在所综述的作物中，基线行为在整个生长季通常呈系统性变化，CWSI与作物水分状况之间的关系在冠层封垄后总体增强，且在生殖阶段测得的CWSI与产量的关系总体最为密切。对56项基于AI、热成像或CWSI的研究进行的定量审查发现，78.6%的研究要么未明确纳入作物物候，要么依赖年积日作为时间代理变量，而仅5.4%的研究明确编码了物候阶段。所提出的框架在基线估算、阈值设定以及用于灌溉调度的数据融合或机器学习中明确考虑物候；该框架在投入业务应用前尚需多年、多站点的田间验证。未来研究应优先建立标准化的物候定义，并开展长期、多地点研究以建立和验证阶段特异性参数。","Plant Science Today","2026-10-21T00:00:00Z",79,{"impact":146,"substance":17,"depth":146,"authority":290,"freshness":47,"relevant":22,"comment":360},"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。",[362],{"name":356,"url":353},[27,28,60,364,217],"作物水分胁迫",{"code":219,"slug":220,"name":221,"path":222,"parent_code":223},[367,368],{"code":79,"slug":299,"name":300,"path":301,"parent_code":38},{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"status":41,"locked":14,"version":42,"method":43,"evidence":370},{"candidates":371,"agricultural_evidence":48},[372,376,382,386,392,396,403,410,417],{"code":219,"score":230,"supported":48,"title":373,"abstract":374,"tags":375},[220],[233,220],[217],{"code":79,"score":13,"supported":48,"title":377,"abstract":378,"tags":381},[],[379,380,83],"机器学习","machine learning",[28],{"code":34,"score":47,"supported":48,"title":383,"abstract":384,"tags":385},[],[51,52,53],[],{"code":96,"score":57,"supported":48,"title":387,"abstract":388,"tags":391},[],[389,390],"数字孪生","digital twin",[27],{"code":56,"score":57,"supported":48,"title":393,"abstract":394,"tags":395},[],[60,61],[60],{"code":397,"score":65,"supported":48,"title":398,"abstract":399,"tags":402},"A2",[],[400,401],"小麦","wheat",[],{"code":404,"score":65,"supported":48,"title":405,"abstract":406,"tags":409},"A3",[],[407,408],"玉米","maize",[],{"code":411,"score":65,"supported":48,"title":412,"abstract":413,"tags":416},"A5",[],[414,415],"马铃薯","potato",[],{"code":418,"score":104,"supported":48,"title":419,"abstract":420,"tags":422},"B2",[],[421],"葡萄",[],[424,425],"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":427,"openalex_id":429,"authors":430,"venue":356,"cited_by_count":124,"oa_url":353,"card":446,"direction":132,"ingested_from":133},"W7223088921",[431,434,437,440,443],{"name":432,"orcid":433},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":435,"orcid":436},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":438,"orcid":439},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":441,"orcid":442},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":444,"orcid":445},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":447,"method":448,"finding":449,"direction":347,"opportunity":450},"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。","可开展多站点多年物候期特异性CWSI阈值标定与标准化物候定义研究。","2026-10-22T11:20:24.526683Z",{"id":453,"title":454,"url":455,"summary":456,"summary_zh":457,"content":9,"source_name":458,"source_url":455,"published_at":357,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":459,"score_detail":460,"sources":462,"tags":464,"primary_topic":466,"secondary_topics":467,"classification":470,"search_phrases":515,"slug":518,"view_count":124,"doi":519,"paper":520,"created_at":538},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":146,"substance":17,"depth":146,"authority":209,"freshness":47,"relevant":22,"comment":461},"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。",[463],{"name":458,"url":455},[27,28,51,152,465],"气候韧性",{"code":79,"slug":299,"name":300,"path":301,"parent_code":38},[468,469],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"code":219,"slug":220,"name":221,"path":222,"parent_code":223},{"status":41,"locked":14,"version":42,"method":43,"evidence":471},{"candidates":472,"agricultural_evidence":48},[473,479,483,487,492,496,503,509],{"code":79,"score":146,"supported":48,"title":474,"abstract":475,"tags":478},[83],[315,379,476,380,313,477],"计算机视觉","computer vision",[28],{"code":96,"score":291,"supported":48,"title":480,"abstract":481,"tags":482},[162],[152,162],[27,152],{"code":34,"score":13,"supported":48,"title":484,"abstract":485,"tags":486},[],[51,296,53,323],[51],{"code":219,"score":47,"supported":48,"title":488,"abstract":489,"tags":491},[],[233,490,220],"水资源",[],{"code":72,"score":57,"supported":48,"title":493,"abstract":494,"tags":495},[],[75,76],[465],{"code":497,"score":65,"supported":48,"title":498,"abstract":499,"tags":502},"H2",[],[500,501],"土壤","soil",[],{"code":504,"score":104,"supported":48,"title":505,"abstract":506,"tags":508},"A7",[],[507],"间作",[],{"code":510,"score":104,"supported":48,"title":511,"abstract":512,"tags":514},"F3",[],[513],"养分管理",[],[516,517],"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":519,"openalex_id":521,"authors":522,"venue":458,"cited_by_count":124,"oa_url":455,"card":533,"direction":132,"ingested_from":133},"W7223121456",[523,526,528,530],{"name":524,"orcid":525},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":527,"orcid":9},"Oluwadare OYEBODE",{"name":529,"orcid":9},"Iretiayo Adelaiye",{"name":531,"orcid":532},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":534,"method":535,"finding":536,"direction":193,"opportunity":537},"综述2021-2026年AI驱动精准农业系统，提出气候韧性粮食生产工程框架。","综述机器学习、深度学习、强化学习、计算机视觉、机器人与物联网在精准农业中的应用。","CNN-LSTM混合模型和强化学习在产量预测与灌溉调度上优于单一方法，边缘计算提升实时性。","可研究边缘计算与强化学习结合的轻量级灌溉决策系统，并探索发展中国家低成本部署与标准化路径。","2026-10-22T11:20:18.151376Z",{"id":540,"title":541,"url":542,"summary":543,"summary_zh":544,"content":9,"source_name":545,"source_url":542,"published_at":546,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":547,"score_detail":548,"sources":550,"tags":552,"primary_topic":555,"secondary_topics":556,"classification":558,"search_phrases":575,"slug":578,"view_count":124,"doi":579,"paper":580,"created_at":600},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","2026-10-19T00:00:00Z",76,{"impact":291,"substance":207,"depth":146,"authority":290,"freshness":47,"relevant":22,"comment":549},"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。",[551],{"name":545,"url":542},[27,28,216,553,554],"大语言模型","传感器标准化",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[557],{"code":79,"slug":299,"name":300,"path":301,"parent_code":38},{"status":41,"locked":14,"version":42,"method":43,"evidence":559},{"candidates":560,"agricultural_evidence":48},[561,565,571],{"code":34,"score":146,"supported":48,"title":562,"abstract":563,"tags":564},[53],[51,52,53],[216],{"code":79,"score":291,"supported":48,"title":566,"abstract":568,"tags":570},[567],"decision support",[569,567],"large language model",[28,553],{"code":96,"score":57,"supported":48,"title":572,"abstract":573,"tags":574},[],[27,161],[27],[576,577],"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":579,"openalex_id":581,"authors":582,"venue":545,"cited_by_count":124,"oa_url":542,"card":595,"direction":132,"ingested_from":133},"W7222920424",[583,585,587,589,591,593],{"name":584,"orcid":9},"Leqi Lai",{"name":586,"orcid":9},"Langtao Duan",{"name":588,"orcid":9},"Yuhao Chen",{"name":590,"orcid":9},"Yuhang Zheng",{"name":592,"orcid":9},"Jinyu Yang",{"name":594,"orcid":9},"Mingyao Wen",{"tldr":596,"method":597,"finding":598,"direction":132,"opportunity":599},"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"]