[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1193":3,"related-1193":68},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":63,"slug":66,"view_count":53,"doi":8,"paper":8,"created_at":67},1193,"Digital Tech Becomes Grain Supply Chains Secret Weapon (数字技术成为粮食供应链的秘密武器:基于中国285个城市十年数据的实证研究)","https:\u002F\u002Fagritechinsights.com\u002Findex.php\u002F2026\u002F08\u002F26\u002Fdigital-tech-becomes-grain-supply-chains-secret-weapon","Frontiers in Sustainable Food Systems发表的新研究,第一作者Hao Li来自信阳师范大学商学院。研究使用中国285个城市十年数据,深入探讨数字工具——从精准农业应用到基于区块链的供应链追踪——如何加强粮食工业链。研究发现,数字化不仅提高生产力;它从根本上重塑粮食从农场到餐桌的流动方式。研究将韧性分解为三个核心组成部分:抗性(系统承受冲击的能力)、恢复(系统反弹速度)和转型(系统进化能力)。数字工具通过优化资源配置(如AI驱动的灌溉系统减少水浪费)和促进产业协同(农民、加工商和分销商共享实时数据)改善所有这三个方面。",null,"In the face of climate shocks, trade disruptions and pandemic aftershocks, the global grain supply chain is under pressure like never before. But what if the key to making it more resilient lies in the same technology that powers our smartphones and streaming services? A new study published in *Frontiers in Sustainable Food Systems* suggests that digital technology could be a game-changer for the grain industry, offering a lifeline to farmers, traders and policymakers alike.\n\nThe research, led by Hao Li from the School of Business at Xinyang Normal University in China, delves into how digital tools—from precision farming apps to blockchain-based supply chain tracking—can strengthen the grain industrial chain. Using a decade of data from 285 Chinese cities, the study finds that digitalization doesn’t just boost productivity; it fundamentally reshapes how grain moves from farm to table. “Digital technology isn’t just about making things faster,” Li explains. “It’s about making the entire system more adaptable when things go wrong.”\n\nThe study breaks down resilience into three core components: resistance (how well the system withstands shocks), recovery (how quickly it bounces back), and transformation (its ability to evolve). Digital tools, the research shows, improve all three by optimizing resource allocation—think AI-driven irrigation systems that cut water waste—and fostering industrial synergy, where farmers, processors and distributors share real-time data to smooth out bottlenecks.\n\nThe commercial implications are significant. For agribusinesses, this means lower costs, fewer supply chain breakdowns and a competitive edge in an increasingly volatile market. Smallholder farmers, often the most vulnerable to disruptions, could benefit from digital platforms that connect them directly to buyers or provide weather-based crop advice. Even grain traders stand to gain from transparent, traceable supply chains that reduce fraud and spoilage.\n\nBut the study also uncovers nuances. Digitalization’s impact isn’t uniform—it’s stronger in regions with robust digital infrastructure and higher farmer literacy in tech. In China’s eastern plains, for example, the resilience boost from digital tools was markedly higher than in less-developed areas. This suggests that investments in rural broadband and digital training could amplify the benefits.\n\nThe research also identifies thresholds: digital infrastructure and farmers’ digital literacy act as gatekeepers. Below a certain level of either, the resilience gains from digital tools plateau. This underscores a critical point for policymakers and investors: digitalization isn’t a silver bullet. It requires targeted infrastructure and education to unlock its full potential.\n\nFor the agriculture sector, this study offers a roadmap. Companies investing in digital solutions—whether IoT sensors for grain storage or AI-driven logistics—could see dividends in stability and profitability. Meanwhile, governments may need to prioritize rural digital inclusion to ensure no farmer is left behind.\n\nAs Li puts it: “The goal isn’t just to digitize agriculture for the sake of it. It’s to build a system that can feed the world, even when the world is on fire.”","Frontiers in Sustainable Food Systems \u002F Agritech Insights","2026-08-26T08:00:00Z","论文",10,false,74,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},22,20,18,12,2,1,"基于285城十年数据的实证研究，揭示数字技术对粮食供应链韧性的多维提升，对政策与产业有参考价值，但时效性稍弱。",[25],{"name":10,"url":6},[27,28,29,30],"智慧农业","粮食安全","数字技术","供应链韧性",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":47},[44,52,58],{"code":45,"score":46,"supported":47,"title":48,"abstract":49,"tags":51},"I1",5,true,[],[50],"精准农业",[29,27],{"code":32,"score":53,"supported":47,"title":54,"abstract":55,"tags":57},3,[],[56],"灌溉",[],{"code":59,"score":22,"supported":14,"title":60,"abstract":61,"tags":62},"A8",[],[],[28],[64,65],"供应链韧性 数字技术 智慧农业 粮食安全","供应链韧性 数字技术","供应链韧性数字技术智慧农业粮食安全-1193","2026-09-01T00:03:40.306438Z",{"total":69,"page":22,"page_size":69,"items":70},6,[71,174,251,318,420,508],{"id":72,"title":73,"url":74,"summary":75,"summary_zh":76,"content":8,"source_name":77,"source_url":74,"published_at":78,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":79,"score_detail":80,"sources":83,"tags":85,"primary_topic":89,"secondary_topics":94,"classification":96,"search_phrases":151,"slug":154,"view_count":155,"doi":8,"paper":156,"created_at":173},5312,"Agricultural Production under Climate Change in Afghanistan: Impacts, Challenges and Adaptation Options (A Review)","https:\u002F\u002Fesrj.edu.af\u002Fesrj\u002Farticle\u002Fview\u002F123","Climate change poses serious challenges to agriculture in Afghanistan, a country largely dependent on rainfed farming and highly vulnerable to water scarcity, drought, and temperature extremes. Understanding the impacts of climate variability and identifying effective adaptation strategies are essential to sustain agricultural productivity and ensure food security. This systematic review synthesizes empirical, modeling, and review studies on climate change effects on Afghan agriculture, assesses adaptation measures, and integrates regional experiences from Pakistan, Iran, Tajikistan, and Uzbekistan. A structured literature search was conducted using peer-reviewed articles, institutional reports, and regional studies published between 2000 and 2025. Studies were selected based on relevance to Afghan agro-ecological systems, climate stressors, and adaptation strategies. We extracted data using a structured table format following PRISMA guidelines, combining national and regional evidence. Findings indicate that water scarcity, prolonged drought, rising temperatures, and erratic precipitation are the main stressors affecting major crops, including wheat, barley, chickpea, maize, and cotton. Yield reductions ranged from 12% to 25% across provinces. Adaptation strategies such as Climate-Smart Agriculture (CSA), efficient irrigation (drip and sprinkler), rainwater harvesting, mulching, and drought- or heat-tolerant crop varieties were most effective. Regional evidence confirms the transferability of these measures under similar agro-climatic conditions. In conclusion, climate change significantly threatens agricultural productivity in Afghanistan. Evidence-based adaptation strategies, supported by coordinated policy and capacity-building initiatives, can enhance resilience, protect rural livelihoods, and ensure sustainable food security.","气候变化对阿富汗农业构成严峻挑战，该国农业在很大程度上依赖雨养耕作，极易受到水资源短缺、干旱和极端温度的影响。理解气候变异的影响并确定有效的适应策略，对于维持农业生产力和保障粮食安全至关重要。本系统综述综合了关于气候变化对阿富汗农业影响的实证研究、模拟研究和综述研究，评估了适应措施，并整合了巴基斯坦、伊朗、塔吉克斯坦和乌兹别克斯坦的区域经验。采用结构化文献检索方法，检索了2000年至2025年间发表的同行评审论文、机构报告和区域研究。研究依据其与阿富汗农业生态系统、气候胁迫因子和适应策略的相关性进行筛选。我们遵循PRISMA指南，采用结构化表格格式提取数据，综合了国家层面和区域层面的证据。研究结果表明，水资源短缺、持续干旱、气温上升和降水不稳定是影响主要作物（包括小麦、大麦、鹰嘴豆、玉米和棉花）的主要胁迫因子。各省产量降幅在12%至25%之间。气候智慧型农业（CSA）、高效灌溉（滴灌和喷灌）、雨水收集、覆盖栽培以及耐旱或耐热作物品种等适应策略最为有效。区域证据证实了这些措施在相似农业气候条件下的可推广性。总之，气候变化显著威胁阿富汗的农业生产力。以协调政策和能力建设举措为支撑的循证适应策略能够增强韧性、保护农村生计并确保可持续的粮食安全。","ESRJ","2026-10-16T00:00:00Z",73,{"impact":19,"substance":18,"depth":81,"authority":20,"freshness":69,"relevant":22,"comment":82},17,"系统综述整合2000-2025年证据，量化减产12%-25%并提出可迁移的适应策略，对区域农业气候韧性研究有参考价值，但非国内三农政策或产业事件，时效性一般。",[84],{"name":77,"url":74},[27,28,86,87,88],"气候变化","适应性策略","水资源短缺",{"code":90,"slug":91,"name":92,"path":93,"parent_code":36},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",[95],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":97},{"candidates":98,"agricultural_evidence":47},[99,106,112,118,125,132,139,145],{"code":90,"score":19,"supported":47,"title":100,"abstract":102,"tags":105},[101],"climate change",[86,103,101,104],"干旱","drought",[86],{"code":32,"score":107,"supported":47,"title":108,"abstract":109,"tags":111},9,[],[56,110,33],"水资源",[],{"code":59,"score":113,"supported":47,"title":114,"abstract":115,"tags":117},7,[],[28,116],"food security",[28],{"code":119,"score":69,"supported":47,"title":120,"abstract":121,"tags":124},"A2",[],[122,123],"小麦","wheat",[],{"code":126,"score":69,"supported":47,"title":127,"abstract":128,"tags":131},"A3",[],[129,130],"玉米","maize",[],{"code":133,"score":69,"supported":47,"title":134,"abstract":135,"tags":138},"A6",[],[136,137],"棉花","cotton",[],{"code":45,"score":140,"supported":47,"title":141,"abstract":142,"tags":144},4,[],[143],"smart agriculture",[27],{"code":146,"score":53,"supported":47,"title":147,"abstract":148,"tags":150},"H6",[],[149],"农业生态",[],[152,153],"阿富汗 气候变化","气候智慧型农业 灌溉","阿富汗气候变化-5312",0,{"doi":8,"openalex_id":157,"authors":158,"venue":77,"cited_by_count":155,"oa_url":74,"card":165,"direction":171,"ingested_from":172},"W7222907396",[159,161,163],{"name":160,"orcid":8},"Mohammad Shafiq Faieq",{"name":162,"orcid":8},"Mustafa Abid",{"name":164,"orcid":8},"Abdul khalil Afghani",{"tldr":166,"method":167,"finding":168,"direction":169,"opportunity":170},"系统综述气候变化对阿富汗农业的影响、挑战与适应策略。","按PRISMA检索2000-2025年文献，整合阿富汗及周边四国区域证据。","水资源短缺与干旱致主要作物减产12%-25%，气候智慧型农业与节水灌溉最有效。","农业绿色发展与碳","可研究气候智慧型农业措施在阿富汗及周边干旱区的本地化适配与推广机制。","智慧农业 \u002F 农业物联网","openalex","2026-10-19T23:30:11.285828Z",{"id":175,"title":176,"url":177,"summary":178,"summary_zh":8,"content":8,"source_name":179,"source_url":177,"published_at":180,"category":12,"cover_url":8,"hotness":181,"is_selected":14,"score":182,"score_detail":183,"sources":186,"tags":190,"primary_topic":193,"secondary_topics":198,"classification":206,"search_phrases":234,"slug":237,"view_count":22,"doi":238,"paper":239,"created_at":250},1255,"Optimizing Water Management in Agriculture: The Role of IoT-Powered Smart Irrigation Systems in Addressing Global Food Security and Water Scarcity","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.21413238","Objective: This study aims to explore the impact of IoT-powered smart irrigation systems on optimizing water management in agriculture, with a specific focus on addressing global food security and mitigating water scarcity challenges. Materials and Methods: The research employs a mixed-methods approach, combining quantitative data analysis from agricultural case studies and qualitative interviews with farmers and agricultural experts. The study assesses the effectiveness of smart irrigation technologies, including soil moisture sensors, weather forecasting tools, and automated irrigation systems, in improving water use efficiency and crop yields. Results and Discussion: The findings indicate that IoT-powered smart irrigation systems significantly enhance water management practices in agriculture. These systems result in a reduction of up to 30% in water usage, while simultaneously increasing crop productivity by 15-20%. Additionally, the integration of real-time data analytics allows for more precise irrigation scheduling, which contributes to sustainable agricultural practices and improved food security. Conclusions: This research contributes to the existing literature by providing empirical evidence of the benefits of IoT technologies in agriculture. It highlights the potential of smart irrigation systems as a viable solution to the dual challenges of food security and water scarcity. The findings emphasize the need for policy interventions and investment in technology to facilitate the adoption of these systems in farming practices worldwide.","Zenodo (CERN European Organization for Nuclear Research)","2026-08-30T00:00:00Z",25,70,{"impact":19,"substance":17,"depth":184,"authority":20,"freshness":21,"relevant":22,"comment":185},16,"论文实证智能灌溉节水30%增产20%，对农业水资源管理有参考价值，但时效性较低。",[187,188],{"name":179,"url":177},{"name":179,"url":189},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.21413237",[27,28,191,192],"智能灌溉","水资源管理",{"code":59,"slug":194,"name":195,"path":196,"parent_code":197},"yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security","A",[199,200],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":201,"slug":202,"name":203,"path":204,"parent_code":205},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":207},{"candidates":208,"agricultural_evidence":47},[209,214,218,224,230],{"code":32,"score":210,"supported":47,"title":211,"abstract":212,"tags":213},13,[33],[33],[191,192],{"code":59,"score":20,"supported":47,"title":215,"abstract":216,"tags":217},[116],[116],[28],{"code":201,"score":219,"supported":47,"title":220,"abstract":222,"tags":223},11,[221],"IoT",[221],[],{"code":225,"score":53,"supported":47,"title":226,"abstract":227,"tags":229},"H2",[],[228],"soil",[],{"code":45,"score":22,"supported":14,"title":231,"abstract":232,"tags":233},[],[],[27],[235,236],"水资源管理 智慧农业 智能灌溉 粮食安全","水资源管理 智慧农业","水资源管理智慧农业智能灌溉粮食安全-1255","10.5281\u002Fzenodo.21413238",{"doi":238,"openalex_id":240,"authors":241,"venue":179,"cited_by_count":155,"oa_url":177,"card":245,"direction":171,"ingested_from":172},"W7169531097",[242],{"name":243,"orcid":244},"Mahdi Sabati Gavgani","https:\u002F\u002Forcid.org\u002F0000-0002-4585-9199",{"tldr":246,"method":247,"finding":248,"direction":171,"opportunity":249},"研究物联网智能灌溉系统优化农业用水，提升粮食安全与缓解水资源短缺。","混合方法：案例定量分析与农户专家定性访谈，评估传感器、天气预报和自动灌溉技术。","智能灌溉节水30%，增产15-20%，实时数据分析优化灌溉调度。","可探索智能灌溉系统在不同作物、气候和农户采纳障碍下的适应性，以及政策激励对推广的影响。","2026-09-01T04:03:11.304099Z",{"id":252,"title":253,"url":254,"summary":255,"summary_zh":256,"content":8,"source_name":179,"source_url":254,"published_at":257,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":258,"score_detail":259,"sources":262,"tags":264,"primary_topic":269,"secondary_topics":270,"classification":276,"search_phrases":299,"slug":302,"view_count":155,"doi":303,"paper":304,"created_at":317},5591,"Hierarchical Conformal Risk Control for Artificial Intelligence Agriculture Irrigation Scheduling Under Unmodeled Infiltration: A Computer Safety-Guarantee Evaluation","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23543665","Irrigation scheduling algorithms that rely on soil-moisture forecasts are vulnerable to unmodeled infiltration, the process by which water bypasses the root zone through preferential pathways that resistance-based soil models cannot represent. The framework is evaluated through a replication-based design study that characterises the operating characteristics of the before-after-control-impact estimator used to audit scheduler performance, under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64. Power was estimated across 2000 design cells spanning cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The analysis indicates that 54 clusters are required to detect a 5-percentage-point effect at 80% power, and that the minimum detectable effect declines steeply as cluster count rises from 8 to 32. The contribution is a formal safety guarantee for irrigation decision support that remains valid when the infiltration model is wrong, together with an audit design that makes the guarantee empirically checkable.","依赖土壤湿度预报的灌溉调度算法容易受到未建模入渗的影响，即水分通过优先路径绕过根区的过程，而基于阻力的土壤模型无法表征这一过程。该框架通过一项基于重复的设计研究进行评估，该研究刻画了用于审计调度器性能的前后对照影响估计量的运行特性，其所依据的声明方差结构为：组间标准差6.0、残差标准差4.5、组内相关系数0.64。功效估计覆盖2000个设计单元，涵盖聚类数8、16、24和32以及效应量3、5和8个百分点。分析表明，在80%功效下检测5个百分点效应需要54个聚类，且最小可检测效应随聚类数从8增至32而急剧下降。本文的贡献在于为灌溉决策支持提供了一种正式的安全保证，该保证在入渗模型错误时仍然有效，同时提出了一种使该保证可经实证检验的审计设计。","2026-10-21T00:00:00Z",69,{"impact":260,"substance":17,"depth":19,"authority":20,"freshness":107,"relevant":22,"comment":261},8,"该研究提出在入渗模型失配条件下仍有效的灌溉决策安全保证框架，并给出可审计的试验设计，方法新颖、结论可靠，但属细分方法学研究，产业影响有限，适合作为专业精选而非头条。",[263],{"name":179,"url":254},[27,265,266,267,268],"农业人工智能","灌溉决策","安全保证","试验设计",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[271],{"code":272,"slug":273,"name":274,"path":275,"parent_code":205},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":39,"locked":14,"version":40,"method":41,"evidence":277},{"candidates":278,"agricultural_evidence":47},[279,284,290,295],{"code":32,"score":280,"supported":47,"title":281,"abstract":282,"tags":283},15,[33],[56,33],[266],{"code":272,"score":20,"supported":47,"title":285,"abstract":287,"tags":289},[286],"artificial intelligence",[288],"decision support",[265],{"code":225,"score":69,"supported":47,"title":291,"abstract":292,"tags":294},[],[293,228],"土壤",[],{"code":45,"score":22,"supported":14,"title":296,"abstract":297,"tags":298},[],[],[27],[300,301],"灌溉调度 安全保证","农业人工智能 安全保证 智慧农业 灌溉决策","灌溉调度安全保证-5591","10.5281\u002Fzenodo.23543665",{"doi":303,"openalex_id":305,"authors":306,"venue":179,"cited_by_count":155,"oa_url":254,"card":311,"direction":315,"ingested_from":172},"W7223131205",[307,309],{"name":308,"orcid":8},"Nassima Benkhelifa",{"name":310,"orcid":8},"Yacine Aït Ahmed",{"tldr":312,"method":313,"finding":314,"direction":315,"opportunity":316},"提出分层保形风险控制，为未建模入渗下的灌溉调度提供安全保证。","分层保形风险控制与基于重复设计的BACI审计，2000个设计单元功效分析。","检测5个百分点效应需54个集群，最小可检测效应随集群数增加陡降。","农业人工智能与决策模型","可将保形风险控制扩展到其他未建模扰动下的农业决策安全保证与审计设计。","2026-10-22T11:21:10.283561Z",{"id":319,"title":320,"url":321,"summary":322,"summary_zh":323,"content":8,"source_name":324,"source_url":321,"published_at":257,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":325,"score_detail":326,"sources":328,"tags":330,"primary_topic":334,"secondary_topics":335,"classification":338,"search_phrases":390,"slug":393,"view_count":155,"doi":394,"paper":395,"created_at":419},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",79,{"impact":19,"substance":17,"depth":19,"authority":20,"freshness":107,"relevant":22,"comment":327},"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。",[329],{"name":324,"url":321},[27,265,331,332,333],"遥感","作物水分胁迫","精准灌溉",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[336,337],{"code":272,"slug":273,"name":274,"path":275,"parent_code":205},{"code":201,"slug":202,"name":203,"path":204,"parent_code":205},{"status":39,"locked":14,"version":40,"method":41,"evidence":339},{"candidates":340,"agricultural_evidence":47},[341,345,351,357,363,369,373,377,384],{"code":32,"score":280,"supported":47,"title":342,"abstract":343,"tags":344},[33],[56,33],[333],{"code":272,"score":13,"supported":47,"title":346,"abstract":347,"tags":350},[],[348,349,286],"机器学习","machine learning",[265],{"code":201,"score":107,"supported":47,"title":352,"abstract":353,"tags":356},[],[354,355,221],"物联网","internet of things",[],{"code":45,"score":113,"supported":47,"title":358,"abstract":359,"tags":362},[],[360,361],"数字孪生","digital twin",[27],{"code":364,"score":113,"supported":47,"title":365,"abstract":366,"tags":368},"I3",[],[331,367],"remote sensing",[331],{"code":119,"score":69,"supported":47,"title":370,"abstract":371,"tags":372},[],[122,123],[],{"code":126,"score":69,"supported":47,"title":374,"abstract":375,"tags":376},[],[129,130],[],{"code":378,"score":69,"supported":47,"title":379,"abstract":380,"tags":383},"A5",[],[381,382],"马铃薯","potato",[],{"code":385,"score":53,"supported":47,"title":386,"abstract":387,"tags":389},"B2",[],[388],"葡萄",[],[391,392],"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":394,"openalex_id":396,"authors":397,"venue":324,"cited_by_count":155,"oa_url":321,"card":413,"direction":171,"ingested_from":172},"W7223088921",[398,401,404,407,410],{"name":399,"orcid":400},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":402,"orcid":403},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":405,"orcid":406},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":408,"orcid":409},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":411,"orcid":412},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":414,"method":415,"finding":416,"direction":417,"opportunity":418},"综述物候期对热成像CWSI阈值的影响，提出物候感知灌溉调度框架。","综述葡萄、小麦、玉米等作物的热遥感CWSI研究，并审计56项AI\u002F热成像研究。","78.6%研究未显式纳入物候，仅5.4%编码物候期；CWSI与产量在生殖期关联最强。","农业遥感与作物表型","可开展多站点多年物候期特异性CWSI阈值标定与标准化物候定义研究。","2026-10-22T11:20:24.526683Z",{"id":421,"title":422,"url":423,"summary":424,"summary_zh":425,"content":8,"source_name":426,"source_url":423,"published_at":257,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":427,"score_detail":428,"sources":430,"tags":432,"primary_topic":434,"secondary_topics":435,"classification":438,"search_phrases":484,"slug":487,"view_count":155,"doi":488,"paper":489,"created_at":507},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":19,"substance":17,"depth":19,"authority":210,"freshness":107,"relevant":22,"comment":429},"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。",[431],{"name":426,"url":423},[27,265,354,50,433],"气候韧性",{"code":272,"slug":273,"name":274,"path":275,"parent_code":205},[436,437],{"code":201,"slug":202,"name":203,"path":204,"parent_code":205},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":439},{"candidates":440,"agricultural_evidence":47},[441,449,454,460,464,468,472,478],{"code":272,"score":19,"supported":47,"title":442,"abstract":443,"tags":448},[286],[444,348,445,349,446,447],"深度学习","计算机视觉","deep learning","computer vision",[265],{"code":45,"score":184,"supported":47,"title":450,"abstract":452,"tags":453},[451],"precision agriculture",[50,451],[27,50],{"code":201,"score":13,"supported":47,"title":455,"abstract":456,"tags":459},[],[354,457,221,458],"边缘计算","edge computing",[354],{"code":32,"score":107,"supported":47,"title":461,"abstract":462,"tags":463},[],[56,110,33],[],{"code":90,"score":113,"supported":47,"title":465,"abstract":466,"tags":467},[],[86,101],[433],{"code":225,"score":69,"supported":47,"title":469,"abstract":470,"tags":471},[],[293,228],[],{"code":473,"score":53,"supported":47,"title":474,"abstract":475,"tags":477},"A7",[],[476],"间作",[],{"code":479,"score":53,"supported":47,"title":480,"abstract":481,"tags":483},"F3",[],[482],"养分管理",[],[485,486],"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":488,"openalex_id":490,"authors":491,"venue":426,"cited_by_count":155,"oa_url":423,"card":502,"direction":171,"ingested_from":172},"W7223121456",[492,495,497,499],{"name":493,"orcid":494},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":496,"orcid":8},"Oluwadare OYEBODE",{"name":498,"orcid":8},"Iretiayo Adelaiye",{"name":500,"orcid":501},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":503,"method":504,"finding":505,"direction":315,"opportunity":506},"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":509,"title":510,"url":511,"summary":512,"summary_zh":513,"content":8,"source_name":514,"source_url":511,"published_at":515,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":258,"score_detail":516,"sources":518,"tags":520,"primary_topic":522,"secondary_topics":523,"classification":525,"search_phrases":557,"slug":560,"view_count":155,"doi":561,"paper":562,"created_at":577},5496,"Multi-Crop Irrigation Need Classification Based on Machine Learning with an Indonesian-Language GenAI-UX Recommendation Layer","https:\u002F\u002Fdoi.org\u002F10.30871\u002Fjaic.v10i5.13774","Inefficient irrigation water use remains a critical challenge in Indonesian agriculture, and the complexity of raw sensor data hinders adoption by smallholder farmers who lack data-analysis skills. This study develops an intelligent irrigation Decision Support System that integrates interpretable Machine Learning with an Indonesian-language GenAI-UX recommendation layer, using a publicly available multi-crop irrigation scheduling dataset (501 records, nine crops) with an analytical focus on paddy and maize. Four components are combined into one pipeline: (i) irrigation-need classification compared across six algorithms; (ii) regression of a continuous water-requirement score; (iii) sensor-anomaly detection using Isolation Forest cross-validated with the Interquartile Range method; and (iv) a rule-based natural-language recommendation layer whose operating point is optimized by a Genetic Algorithm. Validity was safeguarded through stratified five-fold cross-validation and leakage-controlled preprocessing. Gradient Boosting achieved the best classification performance (F1-score 0.980; cross-validated F1 0.963), Random Forest Regressor reached R square of 0.807, Isolation Forest agreed with IQR outliers at 95.2%, and the Genetic Algorithm attained a confidence of 0.993 within realistic agronomic bounds. In an illustrative usability evaluation, the GenAI-UX interface obtained the highest System Usability Scale score (Good category). The results indicate that pairing accurate, interpretable models with a language-based delivery layer can transform complex sensor data into actionable irrigation recommendations, lowering the adoption barrier for non-technical users. Field validation with real respondents and edge deployment are identified as the next steps.","灌溉用水效率低下仍是印度尼西亚农业面临的一项关键挑战，而原始传感器数据的复杂性阻碍了缺乏数据分析技能的小农户采用相关技术。本研究开发了一种智能灌溉决策支持系统（Decision Support System），将可解释的机器学习与印度尼西亚语生成式人工智能用户体验（GenAI-UX）推荐层相结合，使用公开可用的多作物灌溉调度数据集（501条记录，九种作物），并以水稻和玉米为分析重点。四个组件被整合为一条流水线：（i）在六种算法之间比较灌溉需求分类；（ii）对连续需水量得分进行回归；（iii）使用孤立森林（Isolation Forest）进行传感器异常检测，并与四分位距（IQR）方法交叉验证；（iv）基于规则的自然语言推荐层，其操作点由遗传算法优化。通过分层五折交叉验证和防泄漏预处理保障了有效性。梯度提升（Gradient Boosting）取得了最佳分类性能（F1分数0.980；交叉验证F1为0.963），随机森林回归器（Random Forest Regressor）达到R平方0.807，孤立森林与IQR异常值的一致率为95.2%，遗传算法在现实农艺范围内达到0.993的置信度。在一项示例性可用性评估中，GenAI-UX界面获得了最高的系统可用性量表（System Usability Scale）得分（良好类别）。结果表明，将准确、可解释的模型与基于语言的交付层相结合，能够将复杂的传感器数据转化为可操作的灌溉建议，从而降低非技术用户的采用门槛。与真实受访者进行田间验证以及边缘部署被确定为下一步工作。","Journal of Applied Informatics and Computing","2026-10-19T00:00:00Z",{"impact":20,"substance":18,"depth":81,"authority":20,"freshness":260,"relevant":22,"comment":517},"方法组合完整、指标扎实的农业AI决策支持论文，但属印尼小农户场景的实验室级研究，公共影响有限，适合作为技术参考而非每日精选头条。",[519],{"name":514,"url":511},[27,265,521,129,266],"水稻",{"code":272,"slug":273,"name":274,"path":275,"parent_code":205},[524],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":526},{"candidates":527,"agricultural_evidence":47},[528,532,536,542,546,553],{"code":272,"score":19,"supported":47,"title":529,"abstract":530,"tags":531},[349],[348,349,288],[265],{"code":32,"score":280,"supported":47,"title":533,"abstract":534,"tags":535},[33],[56,33],[266],{"code":537,"score":113,"supported":47,"title":538,"abstract":539,"tags":541},"A1",[],[521,540],"paddy",[521],{"code":126,"score":113,"supported":47,"title":543,"abstract":544,"tags":545},[],[129,130],[129],{"code":547,"score":69,"supported":47,"title":548,"abstract":549,"tags":552},"G2",[],[550,551],"小农户","smallholder",[],{"code":45,"score":22,"supported":14,"title":554,"abstract":555,"tags":556},[],[],[27],[558,559],"印尼 多作物 灌溉决策","GenAI 灌溉推荐","印尼多作物灌溉决策-5496","10.30871\u002Fjaic.v10i5.13774",{"doi":561,"openalex_id":563,"authors":564,"venue":514,"cited_by_count":155,"oa_url":511,"card":572,"direction":315,"ingested_from":172},"W7223016583",[565,568,570],{"name":566,"orcid":567},"Muhammad Rusdi","https:\u002F\u002Forcid.org\u002F0000-0002-1048-5000",{"name":569,"orcid":8},"Zaenuddin Zaenuddin",{"name":571,"orcid":8},"Hendra Sanjaya",{"tldr":573,"method":574,"finding":575,"direction":171,"opportunity":576},"构建融合可解释机器学习与印尼语GenAI推荐层的智能灌溉决策支持系统。","六种分类算法、回归、孤立森林异常检测与遗传算法优化规则推荐层。","梯度提升分类F1达0.980，GenAI界面可用性最佳，降低非技术用户使用门槛。","可探索多语言本地化GenAI推荐层与边缘部署，并开展真实农户田间验证。","2026-10-21T23:30:46.441864Z"]