[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3605":3,"related-3605":113},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":85,"slug":88,"view_count":54,"doi":89,"paper":90,"created_at":112},3605,"AquaCrop-derived surrogate model for crop yield response to monthly irrigation deficit","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104995","Context: The Upper Yellow River Basin faces acute water competition between cascade hydropower operations and large irrigation districts. Traditional multi-objective reservoir operation leads to distorted energy-food benefit trade-offs from oversimplified linear representation of agricultural objectives, while process-based crop models are infeasible for direct embedding into reservoir operation frameworks requiring long-sequence iterative optimization due to excessive computational cost. Objective: This study aims to develop an AquaCrop-derived and computationally efficient agricultural surrogate model that accurately maps monthly water deficits to nonlinear crop yield responses, thereby providing a refined agricultural loss function for reservoir operation frameworks. Methods: Focusing on spring wheat, spring maize, and sunflower in the Hetao Irrigation District, a locally calibrated AquaCrop model was applied to simulate yield responses under full-gradient deficit irrigation scenarios. On this basis, we developed high-order polynomial ridge regression surrogate models driven by growing-season Standardized Precipitation Evapotranspiration Index and monthly irrigation deficit ratios. Finally, the model performance was validated using an offline dual validation framework covering generalization performance and decision consistency, and critical irrigation months and yield-loss thresholds were further quantified. Results and conclusions: The surrogate model achieved a global coefficient of determination above 0.93 across dual held-out test sets, with median yield-loss deviations from the sample-best AquaCrop scheme of less than 2%, and outperformed benchmark irrigation allocation schemes. However, prediction accuracy declined substantially under severe water stress, especially for spring maize. Furthermore, computational efficiency was improved by over five orders of magnitude compared with the process-based model. Under the locally parameterized AquaCrop framework, this study identified April as the most irrigation-sensitive month for spring wheat, May for spring maize, and June for sunflower. Compared with interval median scheme, the surrogate model-recommended scheme delayed physically feasible yield-loss thresholds by 6.7 to 14.7 percentage points, with bootstrap 95% confidence intervals entirely above zero. Significance: This study highlights that the AquaCrop-derived surrogate model bridges the gap between crop modeling and reservoir operation by mapping monthly water-allocation decisions to nonlinear crop yield responses. Aligned with the monthly decision cycle of reservoir operation and with ultra-high computational efficiency, this model is expected to serve as a crucial methodological component for future multi-objective reservoir operation frameworks.","背景：黄河上游流域面临梯级水电运行与大型灌区之间尖锐的水资源竞争。传统多目标水库调度因农业目标的过度简化线性表征，导致能量-粮食效益权衡关系失真；而基于过程的作物模型由于计算成本过高，无法直接嵌入需要长序列迭代优化的水库调度框架。目标：本研究旨在开发一种源自AquaCrop且计算高效的农业代理模型，能够准确刻画月度水分亏缺与作物产量非线性响应之间的映射关系，从而为水库调度框架提供精细化的农业损失函数。方法：以河套灌区的春小麦、春玉米和向日葵为研究对象，采用经本地校准的AquaCrop模型模拟全梯度亏缺灌溉情景下的产量响应。在此基础上，构建了以生长季标准化降水蒸散指数和月度灌溉亏缺比为驱动因子的高阶多项式岭回归代理模型。最后，采用涵盖泛化性能和决策一致性的离线双重验证框架对模型性能进行验证，并进一步量化了关键灌溉月份和产量损失阈值。结果与结论：代理模型在两个独立测试集上的全局决定系数均超过0.93，与样本最优AquaCrop方案相比，产量损失偏差中位数低于2%，且优于基准灌溉配水方案。然而，在重度水分胁迫条件下，预测精度显著下降，春玉米尤为明显。此外，与基于过程的模型相比，计算效率提升了五个数量级以上。在本地参数化的AquaCrop框架下，本研究确定春小麦对灌溉最敏感的月份为4月，春玉米为5月，向日葵为6月。与区间中值方案相比，代理模型推荐方案将物理可行的产量损失阈值推迟了6.7至14.7个百分点，Bootstrap 95%置信区间完全位于零以上。意义：本研究突出表明，源自AquaCrop的代理模型通过映射……弥合了作物建模与水库调度之间的差距。",null,"Agricultural Systems","2026-09-26T00:00:00Z","论文",10,true,82,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,23,14,9,1,"将AquaCrop过程模型转化为高效代理模型，为水库调度提供非线性农业损失函数，方法新颖、验证扎实，对灌区水资源协同管理有实用价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","作物模型","节水灌溉","河套灌区",{"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":40,"version":41,"method":42,"evidence":43},"classified",false,"2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":14},[45,52,60,67,74,80],{"code":32,"score":17,"supported":14,"title":46,"abstract":47,"tags":51},[33],[48,49,33,50],"灌溉","水资源","evapotranspiration",[29],{"code":53,"score":54,"supported":14,"title":55,"abstract":56,"tags":59},"A2",6,[],[57,58],"小麦","wheat",[],{"code":61,"score":54,"supported":14,"title":62,"abstract":63,"tags":66},"A3",[],[64,65],"玉米","maize",[],{"code":68,"score":69,"supported":14,"title":70,"abstract":71,"tags":73},"A4",3,[],[72],"向日葵",[],{"code":75,"score":76,"supported":40,"title":77,"abstract":78,"tags":79},"I2",2,[],[],[28,27],{"code":81,"score":21,"supported":40,"title":82,"abstract":83,"tags":84},"I1",[],[],[26],[86,87],"河套灌区 春小麦 灌溉亏缺","AquaCrop 代理模型 产量响应","河套灌区春小麦灌溉亏缺-3605","10.1016\u002Fj.agsy.2026.104995",{"doi":89,"openalex_id":91,"authors":92,"venue":10,"cited_by_count":104,"oa_url":6,"card":105,"direction":109,"ingested_from":111},"W7214449839",[93,95,98,100,102],{"name":94,"orcid":9},"任泽岭",{"name":96,"orcid":97},"Binquan Li","https:\u002F\u002Forcid.org\u002F0000-0002-9958-0396",{"name":99,"orcid":9},"Jing Liu",{"name":101,"orcid":9},"Kuang Li",{"name":103,"orcid":9},"Zhongmin Liang",0,{"tldr":106,"method":107,"finding":108,"direction":109,"opportunity":110},"构建AquaCrop衍生的代理模型，快速映射月度灌溉亏缺与作物产量的非线性响应。","局部校准AquaCrop模拟亏缺灌溉，用SPEI与亏缺比驱动高阶多项式岭回归代理","代理模型R²>0.93、计算效率提升五个数量级，但重旱下春玉米精度明显下降。","农业人工智能与决策模型","可探索极端干旱下代理模型精度衰减的校正方法，并耦合水库调度实现能量-粮食协同优化。","openalex","2026-09-27T23:30:06.912579Z",{"total":54,"page":21,"page_size":54,"items":114},[115,210,275,338,444,534],{"id":116,"title":117,"url":118,"summary":119,"summary_zh":120,"content":9,"source_name":121,"source_url":118,"published_at":122,"category":12,"cover_url":9,"hotness":13,"is_selected":40,"score":123,"score_detail":124,"sources":129,"tags":131,"primary_topic":134,"secondary_topics":135,"classification":136,"search_phrases":189,"slug":192,"view_count":104,"doi":193,"paper":194,"created_at":209},4740,"EcoGrow Smart Irrigation System (EGSIS) A solar-powered, AI-enabled irrigation and water management system with live monitoring, alarms and ESG reporting for climate-smart food production, water resilience, land-based aquaculture and the circular economy","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23294278","The EcoGrow Smart Irrigation System (EGSIS) is a low-cost, solar-powered irrigation controller and water management system designed for small-scale vegetable production. It replaces calendar-based or manual watering with irrigation that responds to the actual moisture condition of the soil. Water is applied only when the crop needs it, and the entire system runs on solar energy stored in a long-life LiFePO4 battery. Agriculture is both a major user of freshwater and a significant source of greenhouse gas emissions, while small-scale growers in South Africa face unreliable electricity, rising energy costs, drought risk and limited access to technology. EGSIS addresses these pressures with a compact, affordable and measurable solution that links renewable energy, automation and intelligent water use. EGSIS also includes an AI and software layer. Machine-learning models adapt irrigation to different crops, growth stages and weather, while a web and mobile app gives growers live data on tank levels, solar generation and soil moisture, a dashboard of key performance indicators, and triggers and alarms for faults. The same data feeds automated ESG (environmental, social and governance) reports that growers can use to attract funding and investment.","EcoGrow智能灌溉系统（EGSIS）是一款低成本、太阳能驱动的灌溉控制器与水资源管理系统，专为小规模蔬菜生产而设计。它取代了基于日历或人工浇水的模式，转而根据土壤实际湿度状况进行灌溉。只有在作物需要时才供水，整个系统依靠储存在长寿命磷酸铁锂（LiFePO4）电池中的太阳能运行。农业既是淡水的主要使用者，也是温室气体排放的重要来源，而南非的小规模种植者面临着电力不可靠、能源成本上涨、干旱风险以及技术获取渠道有限等问题。EGSIS通过一种紧凑、经济且可量化的解决方案来应对这些压力，将可再生能源、自动化和智能用水连接起来。EGSIS还包含人工智能与软件层。机器学习模型可根据不同作物、生长阶段和天气调整灌溉，而网页和移动应用则为种植者提供水箱水位、太阳能发电量和土壤湿度的实时数据、关键绩效指标仪表板，以及故障触发器和警报。同一套数据还可用于生成自动化ESG（环境、社会和治理）报告，种植者可借此吸引资金和投资。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-11T00:00:00Z",68,{"impact":125,"substance":17,"depth":126,"authority":127,"freshness":20,"relevant":21,"comment":128},12,16,13,"面向小农户的低成本太阳能AI灌溉系统，方法具体、数据可测，但属细分技术方案，公共影响有限。",[130],{"name":121,"url":118},[26,27,29,132,133],"光伏农业","ESG报告",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":40,"version":41,"method":42,"evidence":137},{"candidates":138,"agricultural_evidence":14},[139,143,150,158,165,172,179,185],{"code":32,"score":17,"supported":14,"title":140,"abstract":141,"tags":142},[33],[48,49,33],[29],{"code":144,"score":145,"supported":14,"title":146,"abstract":148,"tags":149},"D",8,[147],"aquaculture",[],[],{"code":151,"score":152,"supported":14,"title":153,"abstract":154,"tags":157},"B7",7,[],[155,156],"温室","greenhouse",[132],{"code":159,"score":54,"supported":14,"title":160,"abstract":161,"tags":164},"B1",[],[162,163],"蔬菜","vegetable",[],{"code":166,"score":54,"supported":14,"title":167,"abstract":168,"tags":171},"H2",[],[169,170],"土壤","soil",[],{"code":173,"score":54,"supported":14,"title":174,"abstract":175,"tags":178},"H7",[],[176,177],"干旱","drought",[],{"code":75,"score":180,"supported":14,"title":181,"abstract":182,"tags":184},4,[],[183],"机器学习",[27],{"code":81,"score":21,"supported":40,"title":186,"abstract":187,"tags":188},[],[],[26],[190,191],"EGSIS 太阳能 灌溉","农业人工智能 光伏农业 智慧农业 节水灌溉","EGSIS太阳能灌溉-4740","10.5281\u002Fzenodo.23294278",{"doi":193,"openalex_id":195,"authors":196,"venue":121,"cited_by_count":104,"oa_url":118,"card":203,"direction":207,"ingested_from":111},"W7221439427",[197,199,201],{"name":198,"orcid":9},"Hoko Energy and Projects",{"name":200,"orcid":9},"Siyabulela Hoko",{"name":202,"orcid":9},"Asithandile Mgweba",{"tldr":204,"method":205,"finding":206,"direction":207,"opportunity":208},"开发太阳能AI灌溉系统EGSIS，按土壤湿度精准灌溉并生成ESG报告。","太阳能供电、LiFePO4电池、机器学习模型、Web\u002F移动应用实时监控。","低成本系统可减少用水、适应小农需求，并自动生成ESG报告助力融资。","智慧农业 \u002F 农业物联网","可研究AI灌溉模型在不同作物和气候下的泛化能力，以及ESG报告对小农融资的实际影响。","2026-10-12T23:30:07.706557Z",{"id":211,"title":212,"url":213,"summary":214,"summary_zh":9,"content":9,"source_name":215,"source_url":9,"published_at":216,"category":12,"cover_url":9,"hotness":13,"is_selected":40,"score":217,"score_detail":218,"sources":221,"tags":223,"primary_topic":226,"secondary_topics":227,"classification":228,"search_phrases":261,"slug":264,"view_count":76,"doi":9,"paper":265,"created_at":274},2395,"[预印本]Simulating the Marginal Green Contribution of AI Modules in a Smart-Agriculture Platform: Evidence from Two Monte Carlo Experiments","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.06740","arXiv 2609.06740（2026-09-10）。基于海南智慧农业平台两项蒙特卡洛实验：实验1显示AI模式下20%农药减量概率从近零升至20.7%（基线）至49%（诊断精度0.95、采纳率0.85），15%化肥减量概率从近零升至52.0%；实验2显示AI灌溉调度增加5.0个百分点节水至16.0%，稻田CH4减排30.5%。研究指出农户采纳是主要瓶颈。","arXiv | 2026-09-10","2026-09-10T00:00:00Z",78,{"impact":17,"substance":219,"depth":17,"authority":125,"freshness":145,"relevant":21,"comment":220},22,"预印本以蒙特卡洛模拟量化AI模块在农药化肥减量与稻田减排上的边际绿色贡献，数据与结论有新意，但尚待同行评议，属细分领域前沿进展。",[222],{"name":215,"url":213},[26,27,224,29,225],"精准农业","农业减排",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":40,"version":41,"method":42,"evidence":229},{"candidates":230,"agricultural_evidence":14},[231,236,240,246,252,257],{"code":81,"score":232,"supported":14,"title":233,"abstract":234,"tags":235},5,[],[26],[26,224],{"code":32,"score":180,"supported":14,"title":237,"abstract":238,"tags":239},[],[48],[29],{"code":241,"score":69,"supported":14,"title":242,"abstract":243,"tags":245},"A1",[],[244],"稻田",[],{"code":247,"score":69,"supported":14,"title":248,"abstract":249,"tags":251},"F4",[],[250],"农药",[],{"code":253,"score":21,"supported":40,"title":254,"abstract":255,"tags":256},"H4",[],[],[225],{"code":75,"score":21,"supported":40,"title":258,"abstract":259,"tags":260},[],[],[27],[262,263],"农业人工智能 农业减排 智慧农业 精准农业","农业人工智能 农业减排","农业人工智能农业减排智慧农业精准农业-2395",{"doi":9,"openalex_id":9,"authors":266,"venue":9,"cited_by_count":104,"oa_url":9,"card":267,"direction":271,"ingested_from":273},[],{"tldr":268,"method":269,"finding":270,"direction":271,"opportunity":272},"用蒙特卡洛实验模拟智慧农业平台中AI模块对农药化肥减量与节水减排的边际绿色贡献。","海南智慧农业平台数据，两项蒙特卡洛实验，含诊断精度与采纳率情景。","AI使农药减量20%概率升至20.7%-49%，化肥减量15%概率达52%，稻田CH4减排30.5%","农业绿色发展与碳","可将农户采纳行为内生化，研究采纳率提升机制与AI模块绿色效益的经济激励设计。","agent","2026-09-14T00:06:32.920282Z",{"id":276,"title":277,"url":278,"summary":279,"summary_zh":280,"content":9,"source_name":121,"source_url":278,"published_at":281,"category":12,"cover_url":9,"hotness":13,"is_selected":40,"score":282,"score_detail":283,"sources":285,"tags":287,"primary_topic":291,"secondary_topics":292,"classification":298,"search_phrases":320,"slug":323,"view_count":104,"doi":324,"paper":325,"created_at":337},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":145,"substance":219,"depth":17,"authority":125,"freshness":20,"relevant":21,"comment":284},"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。",[286],{"name":121,"url":278},[26,27,288,289,290],"灌溉决策","安全保证","试验设计",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[293],{"code":75,"slug":294,"name":295,"path":296,"parent_code":297},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":39,"locked":40,"version":41,"method":42,"evidence":299},{"candidates":300,"agricultural_evidence":14},[301,306,312,316],{"code":32,"score":302,"supported":14,"title":303,"abstract":304,"tags":305},15,[33],[48,33],[288],{"code":75,"score":125,"supported":14,"title":307,"abstract":309,"tags":311},[308],"artificial intelligence",[310],"decision support",[27],{"code":166,"score":54,"supported":14,"title":313,"abstract":314,"tags":315},[],[169,170],[],{"code":81,"score":21,"supported":40,"title":317,"abstract":318,"tags":319},[],[],[26],[321,322],"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":324,"openalex_id":326,"authors":327,"venue":121,"cited_by_count":104,"oa_url":278,"card":332,"direction":109,"ingested_from":111},"W7223131205",[328,330],{"name":329,"orcid":9},"Nassima Benkhelifa",{"name":331,"orcid":9},"Yacine Aït Ahmed",{"tldr":333,"method":334,"finding":335,"direction":109,"opportunity":336},"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。","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":339,"title":340,"url":341,"summary":342,"summary_zh":343,"content":9,"source_name":344,"source_url":341,"published_at":281,"category":12,"cover_url":9,"hotness":13,"is_selected":40,"score":345,"score_detail":346,"sources":348,"tags":350,"primary_topic":354,"secondary_topics":355,"classification":362,"search_phrases":414,"slug":417,"view_count":104,"doi":418,"paper":419,"created_at":443},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":17,"substance":219,"depth":17,"authority":125,"freshness":20,"relevant":21,"comment":347},"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。",[349],{"name":344,"url":341},[26,27,351,352,353],"遥感","作物水分胁迫","精准灌溉",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[356,357],{"code":75,"slug":294,"name":295,"path":296,"parent_code":297},{"code":358,"slug":359,"name":360,"path":361,"parent_code":297},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":39,"locked":40,"version":41,"method":42,"evidence":363},{"candidates":364,"agricultural_evidence":14},[365,369,374,381,387,393,397,401,408],{"code":32,"score":302,"supported":14,"title":366,"abstract":367,"tags":368},[33],[48,33],[353],{"code":75,"score":13,"supported":14,"title":370,"abstract":371,"tags":373},[],[183,372,308],"machine learning",[27],{"code":358,"score":20,"supported":14,"title":375,"abstract":376,"tags":380},[],[377,378,379],"物联网","internet of things","IoT",[],{"code":81,"score":152,"supported":14,"title":382,"abstract":383,"tags":386},[],[384,385],"数字孪生","digital twin",[26],{"code":388,"score":152,"supported":14,"title":389,"abstract":390,"tags":392},"I3",[],[351,391],"remote sensing",[351],{"code":53,"score":54,"supported":14,"title":394,"abstract":395,"tags":396},[],[57,58],[],{"code":61,"score":54,"supported":14,"title":398,"abstract":399,"tags":400},[],[64,65],[],{"code":402,"score":54,"supported":14,"title":403,"abstract":404,"tags":407},"A5",[],[405,406],"马铃薯","potato",[],{"code":409,"score":69,"supported":14,"title":410,"abstract":411,"tags":413},"B2",[],[412],"葡萄",[],[415,416],"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":418,"openalex_id":420,"authors":421,"venue":344,"cited_by_count":104,"oa_url":341,"card":437,"direction":207,"ingested_from":111},"W7223088921",[422,425,428,431,434],{"name":423,"orcid":424},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":426,"orcid":427},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":429,"orcid":430},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":432,"orcid":433},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":435,"orcid":436},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":438,"method":439,"finding":440,"direction":441,"opportunity":442},"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":445,"title":446,"url":447,"summary":448,"summary_zh":449,"content":9,"source_name":450,"source_url":447,"published_at":281,"category":12,"cover_url":9,"hotness":13,"is_selected":40,"score":451,"score_detail":452,"sources":454,"tags":456,"primary_topic":458,"secondary_topics":459,"classification":462,"search_phrases":510,"slug":513,"view_count":104,"doi":514,"paper":515,"created_at":533},5553,"Artificial Intelligence–Driven Precision Agriculture Systems: Engineering Frameworks for Climate-Resilient Food Production","https:\u002F\u002Fdoi.org\u002F10.53982\u002Fajerd.2026.01.14-j","Climate change, soil issues, water scarcity, and the increasing need for food worldwide mean we need smart farming systems that can manage resources flexibly. Old farming methods used the same inputs and simple rules for decisions. AI can help us manage resources better by adapting and optimizing them as we deal with complex environmental factors. This paper looks closely at AI-powered precision agriculture systems designed to make food production more resilient to changing weather. The authors reviewed research from 2021 to 2026 in agricultural engineering, computer engineering, and environmental systems. The authors examined how machine learning, deep learning, reinforcement learning, computer vision, robotics, and IoT sensors are used for predicting crop yields, spotting diseases, improving irrigation, managing nutrients, and automating field tasks. Our analysis shows that hybrid deep learning models (CNN-LSTM) are better than single models for predicting crop yields over space and time. Reinforcement learning algorithms are also better at adjusting irrigation cycles when weather is unpredictable. Using edge computing for system design reduces delays and improves real-time decision-making compared to traditional cloud-based systems. However, there are still big challenges with understanding how AI models work, having enough diverse data, ensuring cybersecurity, setting common standards, and making these systems affordable, especially in developing countries. To tackle these problems, we suggest a framework that combines AI analysis, distributed sensor networks, adaptive controls, and climate risk assessments. The authors provided critical insights into essential research gaps and outline a technological roadmap towards achieving sustainable, resilient and intelligent agricultural systems.","气候变化、土壤问题、水资源短缺以及全球日益增长的粮食需求，意味着我们需要能够灵活管理资源的智能农业系统。传统农业方法对相同的投入采用固定模式，并依赖简单规则进行决策。人工智能可以帮助我们更好地管理资源，通过适应和优化资源利用来应对复杂的环境因素。本文深入探讨了人工智能驱动的精准农业系统，旨在使粮食生产对不断变化的天气更具韧性。作者综述了2021年至2026年间农业工程、计算机工程和环境系统领域的研究。作者考察了机器学习、深度学习、强化学习、计算机视觉、机器人技术及物联网传感器如何被用于作物产量预测、病害识别、灌溉优化、养分管理和田间作业自动化。我们的分析表明，混合深度学习模型（CNN-LSTM）在时空作物产量预测方面优于单一模型。在天气不可预测的情况下，强化学习算法在调整灌溉周期方面也表现更优。与传统基于云的系统相比，采用边缘计算进行系统设计可降低延迟并改善实时决策能力。然而，在理解人工智能模型的工作原理、获取足够多样化的数据、确保网络安全、制定通用标准以及使这些系统具备可负担性方面，仍存在重大挑战，尤其是在发展中国家。为应对这些问题，我们提出了一个融合人工智能分析、分布式传感器网络、自适应控制和气候风险评估的框架。作者对关键研究空白提供了重要见解，并勾勒了实现可持续、韧性和智能农业系统的技术路线图。","ABUAD Journal of Engineering Research and Development (AJERD)",80,{"impact":17,"substance":219,"depth":17,"authority":127,"freshness":20,"relevant":21,"comment":453},"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。",[455],{"name":450,"url":447},[26,27,377,224,457],"气候韧性",{"code":75,"slug":294,"name":295,"path":296,"parent_code":297},[460,461],{"code":358,"slug":359,"name":360,"path":361,"parent_code":297},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":40,"version":41,"method":42,"evidence":463},{"candidates":464,"agricultural_evidence":14},[465,473,478,484,488,494,498,504],{"code":75,"score":17,"supported":14,"title":466,"abstract":467,"tags":472},[308],[468,183,469,372,470,471],"深度学习","计算机视觉","deep learning","computer vision",[27],{"code":81,"score":126,"supported":14,"title":474,"abstract":476,"tags":477},[475],"precision agriculture",[224,475],[26,224],{"code":358,"score":13,"supported":14,"title":479,"abstract":480,"tags":483},[],[377,481,379,482],"边缘计算","edge computing",[377],{"code":32,"score":20,"supported":14,"title":485,"abstract":486,"tags":487},[],[48,49,33],[],{"code":173,"score":152,"supported":14,"title":489,"abstract":490,"tags":493},[],[491,492],"气候变化","climate change",[457],{"code":166,"score":54,"supported":14,"title":495,"abstract":496,"tags":497},[],[169,170],[],{"code":499,"score":69,"supported":14,"title":500,"abstract":501,"tags":503},"A7",[],[502],"间作",[],{"code":505,"score":69,"supported":14,"title":506,"abstract":507,"tags":509},"F3",[],[508],"养分管理",[],[511,512],"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":514,"openalex_id":516,"authors":517,"venue":450,"cited_by_count":104,"oa_url":447,"card":528,"direction":207,"ingested_from":111},"W7223121456",[518,521,523,525],{"name":519,"orcid":520},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":522,"orcid":9},"Oluwadare OYEBODE",{"name":524,"orcid":9},"Iretiayo Adelaiye",{"name":526,"orcid":527},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":529,"method":530,"finding":531,"direction":109,"opportunity":532},"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":535,"title":536,"url":537,"summary":538,"summary_zh":539,"content":9,"source_name":540,"source_url":537,"published_at":541,"category":12,"cover_url":9,"hotness":13,"is_selected":40,"score":282,"score_detail":542,"sources":546,"tags":548,"primary_topic":550,"secondary_topics":551,"classification":553,"search_phrases":584,"slug":587,"view_count":104,"doi":588,"paper":589,"created_at":604},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":125,"substance":543,"depth":544,"authority":125,"freshness":145,"relevant":21,"comment":545},20,17,"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。",[547],{"name":540,"url":537},[26,27,549,64,288],"水稻",{"code":75,"slug":294,"name":295,"path":296,"parent_code":297},[552],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":40,"version":41,"method":42,"evidence":554},{"candidates":555,"agricultural_evidence":14},[556,560,564,569,573,580],{"code":75,"score":17,"supported":14,"title":557,"abstract":558,"tags":559},[372],[183,372,310],[27],{"code":32,"score":302,"supported":14,"title":561,"abstract":562,"tags":563},[33],[48,33],[288],{"code":241,"score":152,"supported":14,"title":565,"abstract":566,"tags":568},[],[549,567],"paddy",[549],{"code":61,"score":152,"supported":14,"title":570,"abstract":571,"tags":572},[],[64,65],[64],{"code":574,"score":54,"supported":14,"title":575,"abstract":576,"tags":579},"G2",[],[577,578],"小农户","smallholder",[],{"code":81,"score":21,"supported":40,"title":581,"abstract":582,"tags":583},[],[],[26],[585,586],"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":588,"openalex_id":590,"authors":591,"venue":540,"cited_by_count":104,"oa_url":537,"card":599,"direction":109,"ingested_from":111},"W7223016583",[592,595,597],{"name":593,"orcid":594},"Muhammad Rusdi","https:\u002F\u002Forcid.org\u002F0000-0002-1048-5000",{"name":596,"orcid":9},"Zaenuddin Zaenuddin",{"name":598,"orcid":9},"Hendra Sanjaya",{"tldr":600,"method":601,"finding":602,"direction":207,"opportunity":603},"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"]