[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2410":3,"related-2410":97},{"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":32,"secondary_topics":38,"classification":39,"search_phrases":69,"slug":72,"view_count":73,"doi":74,"paper":75,"created_at":96},2410,"Assessing output adjustment in South Africa's avocado industry","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1931791","Export-oriented perennial horticultural industries are increasingly exposed to macroeconomic shocks, yet there is limited empirical evidence on how exchange rates, financing conditions, production costs, and climatic variability jointly influence long-run production adjustment in emerging economies. This knowledge gap constrains evidence-based policies aimed at strengthening export competitiveness and investment in high-value horticulture. This study examines the macroeconomic determinants of avocado production in South Africa, a capital-intensive export industry closely integrated into global markets. Annual data for 1991–2024 are analyzed using an autoregressive distributed lag (ARDL) error-correction framework to estimate long-run relationships and short-run adjustment dynamics between avocado production and key macroeconomic variables. The results indicate a stable long-run equilibrium in which exchange-rate movements are the dominant driver of production, highlighting the importance of external competitiveness in export horticulture. In the short run, production adjusts primarily through changes in fertilizer price pressures and financing conditions, while rainfall variability and consumer inflation exert limited influence, reflecting widespread irrigation and weak dependence on domestic demand. The estimated error-correction mechanism indicates relatively rapid adjustment following macroeconomic shocks. These findings suggest that production in export-oriented perennial horticulture responds more strongly to trade competitiveness and production costs than to climatic variability or domestic price conditions. The findings underscore the importance of exchange-rate stability, efficient input markets, and access to specialized agricultural finance in sustaining the competitiveness of high-value horticultural exports in emerging economies. The results should be interpreted in light of the use of aggregate annual industry-level data, which do not capture regional or farm-level heterogeneity. Future research should employ farm-level or regional panel data to examine heterogeneous production responses and the role of technological and institutional factors in shaping long-run industry performance.","出口导向型多年生园艺产业日益暴露于宏观经济冲击之下，然而关于汇率、融资条件、生产成本和气候变异性如何共同影响新兴经济体长期生产调整的实证证据仍然有限。这一知识缺口制约了旨在增强出口竞争力和高价值园艺投资的政策制定。本研究考察南非牛油果生产的宏观经济决定因素，该产业属于资本密集型出口产业，与全球市场紧密融合。研究采用自回归分布滞后（ARDL）误差修正框架分析1991—2024年的年度数据，以估计牛油果生产与关键宏观经济变量之间的长期关系和短期调整动态。结果表明，存在稳定的长期均衡，其中汇率变动是生产的主导驱动因素，凸显了外部竞争力在出口园艺中的重要性。短期内，生产主要通过化肥价格压力和融资条件的变化进行调整，而降雨变异性和消费者通胀的影响有限，这反映了灌溉的广泛使用以及对国内需求的依赖较弱。估计的误差修正机制表明，宏观经济冲击后调整相对迅速。这些发现表明，出口导向型多年生园艺的生产对贸易竞争力和生产成本的响应强于对气候变异性或国内价格条件的响应。研究结果凸显了汇率稳定、高效投入品市场以及获得专业农业融资对于维持新兴经济体高价值园艺出口竞争力的重要性。由于使用的是行业层面的年度总量数据，无法捕捉区域或农场层面的异质性，因此结果应在此局限下加以解读。未来研究应采用农场层面或区域面板数据，以考察异质性生产响应以及技术和制度因素在塑造产业长期表现中的作用。",null,"Frontiers in Sustainable Food Systems","2026-09-13T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,21,17,13,8,1,"以南非牛油果产业1991—2024年数据实证汇率、融资与投入成本对出口型园艺生产的长期调整机制，方法规范、结论对新兴经济体高值农产品出口竞争力政策有参考价值，但属境外细分行业研究，国内落地相关性有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"农产品出口","农业金融","牛油果产业","气候风险","南非农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,59,64],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":51},6,true,[],[50,34],"灌溉",[],{"code":53,"score":54,"supported":47,"title":55,"abstract":56,"tags":58},"F3",3,[],[57],"fertilizer",[],{"code":60,"score":22,"supported":14,"title":61,"abstract":62,"tags":63},"E5",[],[],[27],{"code":65,"score":22,"supported":14,"title":66,"abstract":67,"tags":68},"H7",[],[],[30],[70,71],"农产品出口 牛油果产业 农业金融 南非农业","农产品出口 牛油果产业","农产品出口牛油果产业农业金融南非农业-2410",2,"10.3389\u002Ffsufs.2026.1931791",{"doi":74,"openalex_id":76,"authors":77,"venue":10,"cited_by_count":88,"oa_url":6,"card":89,"direction":93,"ingested_from":95},"W7212447128",[78,81,84,86],{"name":79,"orcid":80},"Biru Gelgo Dube","https:\u002F\u002Forcid.org\u002F0000-0002-2321-4517",{"name":82,"orcid":83},"Mamakie Lungwana","https:\u002F\u002Forcid.org\u002F0000-0002-6239-2236",{"name":85,"orcid":9},"Solly Molepo",{"name":87,"orcid":9},"Joseph Kau",0,{"tldr":90,"method":91,"finding":92,"direction":93,"opportunity":94},"用ARDL模型分析1991–2024年南非牛油果产量与汇率、融资、成本、气候的长期与短期关系。","1991–2024年年度数据，ARDL误差修正模型估计长期均衡与短期调整。","汇率是产量长期主导驱动，短期靠化肥价格与融资条件调整，降雨和通胀影响有限。","农业绿色发展与碳","可延伸至农场或区域面板数据，考察技术与制度因素对出口型园艺产业长期绩效的异质性影响。","openalex","2026-09-14T23:30:06.646726Z",{"total":46,"page":22,"page_size":46,"items":98},[99,175,243,356,446,510],{"id":100,"title":101,"url":102,"summary":103,"summary_zh":104,"content":9,"source_name":105,"source_url":102,"published_at":106,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":107,"score_detail":108,"sources":114,"tags":116,"primary_topic":122,"secondary_topics":123,"classification":130,"search_phrases":156,"slug":159,"view_count":88,"doi":160,"paper":161,"created_at":174},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。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",69,{"impact":21,"substance":109,"depth":110,"authority":111,"freshness":112,"relevant":22,"comment":113},22,18,12,9,"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。",[115],{"name":105,"url":102},[117,118,119,120,121],"智慧农业","农业人工智能","灌溉决策","安全保证","试验设计",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[124],{"code":125,"slug":126,"name":127,"path":128,"parent_code":129},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":40,"locked":14,"version":41,"method":42,"evidence":131},{"candidates":132,"agricultural_evidence":47},[133,138,144,151],{"code":33,"score":134,"supported":47,"title":135,"abstract":136,"tags":137},15,[34],[50,34],[119],{"code":125,"score":111,"supported":47,"title":139,"abstract":141,"tags":143},[140],"artificial intelligence",[142],"decision support",[118],{"code":145,"score":46,"supported":47,"title":146,"abstract":147,"tags":150},"H2",[],[148,149],"土壤","soil",[],{"code":152,"score":22,"supported":14,"title":153,"abstract":154,"tags":155},"I1",[],[],[117],[157,158],"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":160,"openalex_id":162,"authors":163,"venue":105,"cited_by_count":88,"oa_url":102,"card":168,"direction":172,"ingested_from":95},"W7223131205",[164,166],{"name":165,"orcid":9},"Nassima Benkhelifa",{"name":167,"orcid":9},"Yacine Aït Ahmed",{"tldr":169,"method":170,"finding":171,"direction":172,"opportunity":173},"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":176,"title":177,"url":178,"summary":179,"summary_zh":180,"content":9,"source_name":181,"source_url":178,"published_at":182,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":183,"score_detail":184,"sources":187,"tags":189,"primary_topic":195,"secondary_topics":196,"classification":197,"search_phrases":221,"slug":224,"view_count":88,"doi":225,"paper":226,"created_at":242},5575,"Wetland vegetation dynamics and its linkage with climatic, hydrological, and land-use\u002F land cover changes in the Foket floodplain of the Fetam River Watershed, Northwestern Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44274-026-01136-x","Timely monitoring of wetland changes is vital for effective conservation and management of the landscape. This study, therefore, examines vegetation dynamics and their climatic, hydrological, and land-use drivers in the Foket floodplain wetland, Upper Blue Nile basin, Ethiopia. The LULC analysis was employed based on 1985, 2002, and 2020 cloud-free Landsat images. The maximum likelihood classification algorithm was applied to classify the images into eight LULC classes. The spatial and temporal dynamics of the floodplain’s surface water were further analyzed using the Global Surface Water Explorer (1984–2020), focusing on water occurrence, seasonality, transitions, and changes in surface-water dynamics. The Normalized Difference Vegetation Index (NDVI) was used to assess the temporal dynamics of wetland vegetation, while annual rainfall, temperature, and evapotranspiration data were used to examine the climatic context of vegetation change. The results reveal that the area coverage of water body and wetland decreased by 63.2% and 29%, respectively, over the study period. In contrast, cultivated land, settlement, and eucalyptus plantation had expanded by 39.8%, 280.6%, and 380%, respectively, at the expense of wetland, woodland, and shrubland. Surface-water dynamics showed a substantial decline in seasonally inundated areas and changes in the spatial distribution and persistence of surface water. This indicates the reduction of the floodplain’s hydrological connectivity. The NDVI analysis also revealed a significant increasing of wetland vegetation over 1985–2020\u002F21. Vegetation growth was very fast during 1985–2000, slowed down between 2001 and 2012, and dropped slightly from 2013 to 2020\u002F21. Analysis of climatic variables showed no major change in annual rainfall or evapotranspiration, whereas temperature exhibited a significant upward trend. The overall findings revealed the association of the floodplain vegetation dynamics with land-use land cover, hydrological, and climatic changes. This indicates that land-use land cover, hydrological, and climatic changes have contributed to the degradation of the floodplain vegetation, highlighting the urgent need for integrated management interventions that regulate agricultural expansion, grazing, and water diversion while maintaining hydrological connectivity and wetland vegetation. Clinical trial number Not applicable (NA).","及时监测湿地变化对于景观的有效保护和管理至关重要。因此，本研究考察了埃塞俄比亚上青尼罗河流域福凯特洪泛平原湿地的植被动态及其气候、水文和土地利用驱动因素。基于1985年、2002年和2020年无云Landsat影像进行土地利用\u002F土地覆盖（LULC）分析，采用最大似然分类算法将影像分为八个LULC类别。进一步利用全球地表水探索器（1984—2020年）分析洪泛平原地表水的时空动态，重点关注水的出现频率、季节性、转换及地表水动态变化。采用归一化差异植被指数（NDVI）评估湿地植被的时间动态，同时利用年降水量、气温和蒸散发数据考察植被变化的气候背景。结果表明，研究期间水体和湿地的面积覆盖率分别下降了63.2%和29%。相比之下，耕地、居民点和桉树种植园分别扩张了39.8%、280.6%和380%，侵占了湿地、林地和灌丛。地表水动态显示季节性淹没区域大幅减少，地表水的空间分布和持续性发生变化，表明洪泛平原水文连通性降低。NDVI分析还揭示1985—2020\u002F21年间湿地植被显著增加。植被生长在1985—2000年间非常迅速，2001—2012年间放缓，2013—2020\u002F21年间略有下降。气候变量分析显示年降水量或蒸散发无重大变化，而气温呈显著上升趋势。总体研究结果揭示了洪泛平原植被动态与土地利用\u002F土地覆盖、水文和气候变化的关联。这表明土地利用\u002F土地覆盖、水文和气候变化共同导致了洪泛平原植被退化，凸显了迫切需要综合管理干预措施，以调控农业扩张、放牧和引水，同时维持水文连通性和湿地植被。临床试验编号：不适用（NA）。","Discover Environment","2026-10-20T00:00:00Z",65,{"impact":21,"substance":185,"depth":17,"authority":111,"freshness":112,"relevant":22,"comment":186},20,"基于Landsat与NDVI的湿地植被动态实证研究，数据扎实但属区域案例，国际参考价值有限，不宜进入每日精选。",[188],{"name":181,"url":178},[190,191,192,193,194],"NDVI","埃塞俄比亚","土地利用变化","湿地遥感","水文连通性",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":198},{"candidates":199,"agricultural_evidence":47},[200,206,211,216],{"code":33,"score":46,"supported":47,"title":201,"abstract":202,"tags":205},[],[203,204],"蒸散发","evapotranspiration",[],{"code":65,"score":54,"supported":47,"title":207,"abstract":208,"tags":210},[],[209],"气候变化",[],{"code":212,"score":22,"supported":14,"title":213,"abstract":214,"tags":215},"H6",[],[],[192],{"code":217,"score":22,"supported":14,"title":218,"abstract":219,"tags":220},"I3",[],[],[190],[222,223],"土地利用变化 埃塞俄比亚 水文连通性 湿地遥感","土地利用变化 埃塞俄比亚","土地利用变化埃塞俄比亚水文连通性湿地遥感-5575","10.1007\u002Fs44274-026-01136-x",{"doi":225,"openalex_id":227,"authors":228,"venue":181,"cited_by_count":88,"oa_url":235,"card":236,"direction":240,"ingested_from":95},"W7223064056",[229,232],{"name":230,"orcid":231},"Workiyie Worie Assefa","https:\u002F\u002Forcid.org\u002F0000-0002-4029-9022",{"name":233,"orcid":234},"Belachew Getnet Eneyew","https:\u002F\u002Forcid.org\u002F0000-0003-1196-9004","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44274-026-01136-x.pdf",{"tldr":237,"method":238,"finding":239,"direction":240,"opportunity":241},"分析埃塞俄比亚Foket洪泛湿地植被动态及其气候、水文与土地利用驱动因素。","用Landsat影像做最大似然分类，结合全球地表水数据与NDVI及气候数据分析。","水体与湿地面积大幅减少，耕地、居民点和桉树种植扩张，温度显著上升。","农业遥感与作物表型","可结合多源遥感与水文连通性指标，量化湿地退化对农业灌溉与乡村生态服务的影响。","2026-10-22T11:20:42.476484Z",{"id":244,"title":245,"url":246,"summary":247,"summary_zh":248,"content":9,"source_name":249,"source_url":246,"published_at":106,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":250,"score_detail":251,"sources":253,"tags":255,"primary_topic":259,"secondary_topics":260,"classification":267,"search_phrases":326,"slug":329,"view_count":88,"doi":330,"paper":331,"created_at":355},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":110,"substance":109,"depth":110,"authority":111,"freshness":112,"relevant":22,"comment":252},"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。",[254],{"name":249,"url":246},[117,118,256,257,258],"遥感","作物水分胁迫","精准灌溉",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[261,262],{"code":125,"slug":126,"name":127,"path":128,"parent_code":129},{"code":263,"slug":264,"name":265,"path":266,"parent_code":129},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":40,"locked":14,"version":41,"method":42,"evidence":268},{"candidates":269,"agricultural_evidence":47},[270,274,280,287,294,299,306,313,320],{"code":33,"score":134,"supported":47,"title":271,"abstract":272,"tags":273},[34],[50,34],[258],{"code":125,"score":13,"supported":47,"title":275,"abstract":276,"tags":279},[],[277,278,140],"机器学习","machine learning",[118],{"code":263,"score":112,"supported":47,"title":281,"abstract":282,"tags":286},[],[283,284,285],"物联网","internet of things","IoT",[],{"code":152,"score":288,"supported":47,"title":289,"abstract":290,"tags":293},7,[],[291,292],"数字孪生","digital twin",[117],{"code":217,"score":288,"supported":47,"title":295,"abstract":296,"tags":298},[],[256,297],"remote sensing",[256],{"code":300,"score":46,"supported":47,"title":301,"abstract":302,"tags":305},"A2",[],[303,304],"小麦","wheat",[],{"code":307,"score":46,"supported":47,"title":308,"abstract":309,"tags":312},"A3",[],[310,311],"玉米","maize",[],{"code":314,"score":46,"supported":47,"title":315,"abstract":316,"tags":319},"A5",[],[317,318],"马铃薯","potato",[],{"code":321,"score":54,"supported":47,"title":322,"abstract":323,"tags":325},"B2",[],[324],"葡萄",[],[327,328],"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":330,"openalex_id":332,"authors":333,"venue":249,"cited_by_count":88,"oa_url":246,"card":349,"direction":354,"ingested_from":95},"W7223088921",[334,337,340,343,346],{"name":335,"orcid":336},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":338,"orcid":339},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":341,"orcid":342},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":344,"orcid":345},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":347,"orcid":348},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":350,"method":351,"finding":352,"direction":240,"opportunity":353},"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。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:24.526683Z",{"id":357,"title":358,"url":359,"summary":360,"summary_zh":361,"content":9,"source_name":362,"source_url":359,"published_at":106,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":363,"score_detail":364,"sources":366,"tags":368,"primary_topic":371,"secondary_topics":372,"classification":375,"search_phrases":422,"slug":425,"view_count":88,"doi":426,"paper":427,"created_at":445},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":110,"substance":109,"depth":110,"authority":20,"freshness":112,"relevant":22,"comment":365},"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。",[367],{"name":362,"url":359},[117,118,283,369,370],"精准农业","气候韧性",{"code":125,"slug":126,"name":127,"path":128,"parent_code":129},[373,374],{"code":263,"slug":264,"name":265,"path":266,"parent_code":129},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":376},{"candidates":377,"agricultural_evidence":47},[378,386,391,397,402,407,411,417],{"code":125,"score":110,"supported":47,"title":379,"abstract":380,"tags":385},[140],[381,277,382,278,383,384],"深度学习","计算机视觉","deep learning","computer vision",[118],{"code":152,"score":17,"supported":47,"title":387,"abstract":389,"tags":390},[388],"precision agriculture",[369,388],[117,369],{"code":263,"score":13,"supported":47,"title":392,"abstract":393,"tags":396},[],[283,394,285,395],"边缘计算","edge computing",[283],{"code":33,"score":112,"supported":47,"title":398,"abstract":399,"tags":401},[],[50,400,34],"水资源",[],{"code":65,"score":288,"supported":47,"title":403,"abstract":404,"tags":406},[],[209,405],"climate change",[370],{"code":145,"score":46,"supported":47,"title":408,"abstract":409,"tags":410},[],[148,149],[],{"code":412,"score":54,"supported":47,"title":413,"abstract":414,"tags":416},"A7",[],[415],"间作",[],{"code":53,"score":54,"supported":47,"title":418,"abstract":419,"tags":421},[],[420],"养分管理",[],[423,424],"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":426,"openalex_id":428,"authors":429,"venue":362,"cited_by_count":88,"oa_url":359,"card":440,"direction":354,"ingested_from":95},"W7223121456",[430,433,435,437],{"name":431,"orcid":432},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":434,"orcid":9},"Oluwadare OYEBODE",{"name":436,"orcid":9},"Iretiayo Adelaiye",{"name":438,"orcid":439},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":441,"method":442,"finding":443,"direction":172,"opportunity":444},"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":447,"title":448,"url":449,"summary":450,"summary_zh":451,"content":9,"source_name":105,"source_url":449,"published_at":106,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":452,"sources":454,"tags":456,"primary_topic":460,"secondary_topics":461,"classification":467,"search_phrases":494,"slug":497,"view_count":88,"doi":498,"paper":499,"created_at":509},5552,"INTEGRATING IoT, REMOTE SENSING, AND ARTIFICIAL INTELLIGENCE FOR SMART IRRIGATION: A REVIEW OF ARCHITECTURES, METHODS, AND CHALLENGES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531020","This research paper reviews the integration of Internet of Things (IoT), remote sensing, and Artificial Intelligence (AI) in smart irrigation systems. It discusses system architectures, intelligent irrigation techniques, automated water management, key challenges, and future research opportunities to improve water-use efficiency and promote sustainable agriculture.","本文综述了物联网（Internet of Things, IoT）、遥感与人工智能（Artificial Intelligence, AI）在智能灌溉系统中的融合应用，探讨了系统架构、智能灌溉技术、自动化水资源管理、关键挑战及未来研究方向，以提升用水效率并促进可持续农业发展。",{"impact":110,"substance":185,"depth":17,"authority":111,"freshness":112,"relevant":22,"comment":453},"系统综述物联网、遥感与AI融合的智能灌溉架构与方法，属智慧农业细分领域有实质增量的国际综述，值得入选。",[455],{"name":105,"url":449},[118,457,256,458,459],"农业物联网","智慧灌溉","节水农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[462,463],{"code":263,"slug":264,"name":265,"path":266,"parent_code":129},{"code":217,"slug":464,"name":465,"path":466,"parent_code":129},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":40,"locked":14,"version":41,"method":42,"evidence":468},{"candidates":469,"agricultural_evidence":47},[470,475,479,483,487],{"code":33,"score":471,"supported":47,"title":472,"abstract":473,"tags":474},19,[34],[50,400,34],[458,459],{"code":263,"score":110,"supported":47,"title":476,"abstract":477,"tags":478},[285],[283,284,285],[457],{"code":217,"score":134,"supported":47,"title":480,"abstract":481,"tags":482},[297],[256,297],[256],{"code":125,"score":111,"supported":47,"title":484,"abstract":485,"tags":486},[140],[140],[118],{"code":488,"score":46,"supported":47,"title":489,"abstract":490,"tags":493},"H8",[],[491,492],"可持续农业","sustainable agriculture",[],[495,496],"IoT 遥感 AI 智能灌溉","农业人工智能 农业物联网 智慧灌溉 节水农业","IoT遥感AI智能灌溉-5552","10.5281\u002Fzenodo.23531020",{"doi":498,"openalex_id":500,"authors":501,"venue":105,"cited_by_count":88,"oa_url":449,"card":504,"direction":354,"ingested_from":95},"W7223090389",[502],{"name":503,"orcid":9},"Patil",{"tldr":505,"method":506,"finding":507,"direction":354,"opportunity":508},"综述物联网、遥感与人工智能融合的智能灌溉系统架构、方法与挑战。","文献综述，梳理IoT、遥感与AI集成的系统架构与智能灌溉技术。","融合三者可提升用水效率并推动可持续农业，但仍面临关键挑战。","可针对多源数据融合的实时灌溉决策模型与边缘部署开展实证研究。","2026-10-22T11:20:18.028252Z",{"id":511,"title":512,"url":513,"summary":514,"summary_zh":515,"content":9,"source_name":516,"source_url":513,"published_at":106,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":250,"score_detail":517,"sources":520,"tags":522,"primary_topic":528,"secondary_topics":529,"classification":533,"search_phrases":555,"slug":558,"view_count":88,"doi":559,"paper":560,"created_at":580},5541,"Year-round modeling of soil water-heat-salt transport and crop growth under film-mulched drip irrigation with freeze-thaw cycles","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.105024","Context Soil water-heat-salt migration and crop growth in film-mulched drip irrigation fields in seasonally frozen regions are influenced by partial mulching and drip irrigation during the growth period, as well as soil freeze-thaw processes during the fallow period. Understanding year-round soil water-heat-salt dynamics and crop growth is essential for sustainable agricultural water use. Objective However, existing year-round models often use one-dimensional (1D) approaches during the growth period, which neglect horizontal salt migration under partial mulching and drip irrigation, leading to an overestimation of salt leaching efficiency. This limitation can be addressed by developing a year-round model that incorporates the distinct dynamics of both growth and fallow periods. Methods In this study, we developed a year-round coupled model, CAWM-S, by integrating the previously developed Crop and Water Model based on HYDRUS-2D (CAWM-H2D) with the SHAW model, which considers soil freeze-thaw processes. The key features of CAWM-S include detailed depiction of 2D soil water-heat-salt migration, crop growth and yield formation, and energy balance at the film-mulched soil surface during the growth period, as well as the redistribution of soil water-salt due to freeze-thaw processes in the fallow period. The model was calibrated and validated using monitored data from two consecutive year-round periods in a film-mulched drip irrigation cotton field in southern Xinjiang, a region with seasonal frozen soils, under different irrigation water salinity levels. This validation is treatment-wise and limited to the same site and two-year period; independent validation across other sites, years, crops, and irrigation systems remains necessary. Results and conclusions The simulated results for soil water, temperature, salinity, crop growth indicators, and yield generally matched the measured data across the two years with varying salinity levels, with treatment-averaged R 2 values ranging from 0.48 to 0.98 and NSE values 0.42 to 0.97. Furthermore, we analyzed the effects of different irrigation volume and salinity combinations on soil desalination rates, evapotranspiration and its components, crop yield, and water productivity, revealing a quadratic relationship between optimal irrigation volume and water salinity under film-mulched drip irrigation. Validation results show that CAWM-S effectively quantifies coupled soil water-heat-salt dynamics and crop growth, and predicts salt redistribution across year-round cycles in seasonally frozen regions. Significance It provides an effective tool for supporting sustainable agriculture by aiding water saving, control of soil salinization and improvement of crop water productivity in the film-mulched drip irrigation fields within seasonally frozen regions.","背景 季节性冻土区覆膜滴灌农田的土壤水热盐迁移与作物生长，受生育期内部分覆膜和滴灌以及休止期土壤冻融过程的影响。理解全年土壤水热盐动态及作物生长对于农业可持续用水至关重要。目的 然而，现有全年模型在生育期多采用一维（1D）方法，忽略了部分覆膜滴灌条件下的水平盐分迁移，导致对盐分淋洗效率的高估。这一局限可通过开发兼顾生育期与休止期不同动态特征的全年模型加以解决。方法 本研究将前期开发的基于HYDRUS-2D的作物与水分模型（CAWM-H2D）与考虑土壤冻融过程的SHAW模型相耦合，构建了全年耦合模型CAWM-S。CAWM-S的主要特点包括：详细刻画生育期二维土壤水热盐迁移、作物生长与产量形成以及覆膜土壤表面能量平衡，同时描述休止期冻融过程引起的土壤水盐再分布。模型采用南疆季节性冻土区覆膜滴灌棉田连续两个全年周期的监测数据，在不同灌溉水矿化度水平下进行率定与验证。该验证按处理进行，且仅限于同一站点和两年时段；仍需在其他站点、年份、作物及灌溉系统下开展独立验证。结果与结论 在两个不同矿化度水平的年份中，土壤水分、温度、盐分、作物生长指标及产量的模拟结果与实测数据总体吻合，处理平均R²为0.48~0.98，NSE为0.42~0.97。此外，分析了不同灌水量与矿化度组合对土壤脱盐率、蒸散及其组分、作物产量和水分生产率的影响，揭示了覆膜滴灌条件下最优灌水量与灌溉水矿化度之间的二次关系。验证结果表明，CAWM-S能够有效量化土壤水热盐耦合动态与作物生长，并预测","Agricultural Systems",{"impact":17,"substance":109,"depth":110,"authority":518,"freshness":112,"relevant":22,"comment":519},14,"构建了覆盖冻融期的全年水热盐与作物生长耦合模型，方法有新意、验证数据扎实，对盐碱地滴灌节水有实用价值，但属单点两年验证的学术成果，产业影响有限。",[521],{"name":516,"url":513},[523,524,525,526,527],"棉花","盐碱地治理","节水灌溉","覆膜滴灌","土壤水盐运移",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[530],{"code":145,"slug":149,"name":531,"path":532,"parent_code":37},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"status":40,"locked":14,"version":41,"method":42,"evidence":534},{"candidates":535,"agricultural_evidence":47},[536,540,544,551],{"code":33,"score":110,"supported":47,"title":537,"abstract":538,"tags":539},[34],[50,34,204],[525],{"code":145,"score":134,"supported":47,"title":541,"abstract":542,"tags":543},[149],[148,149],[524],{"code":545,"score":546,"supported":47,"title":547,"abstract":548,"tags":550},"A6",4,[],[549],"cotton",[523],{"code":488,"score":54,"supported":47,"title":552,"abstract":553,"tags":554},[],[492],[],[556,557],"南疆 覆膜滴灌 棉花","CAWM-S 模型 冻融","南疆覆膜滴灌棉花-5541","10.1016\u002Fj.agsy.2026.105024",{"doi":559,"openalex_id":561,"authors":562,"venue":516,"cited_by_count":88,"oa_url":513,"card":575,"direction":240,"ingested_from":95},"W7223120642",[563,566,568,571,573],{"name":564,"orcid":565},"Dong Lin","https:\u002F\u002Forcid.org\u002F0000-0001-8433-9658",{"name":567,"orcid":9},"Tongkai Guo",{"name":569,"orcid":570},"Zunqiu Xu","https:\u002F\u002Forcid.org\u002F0000-0002-9393-3605",{"name":572,"orcid":9},"Wenping Bi",{"name":574,"orcid":9},"Xiaomin Mao",{"tldr":576,"method":577,"finding":578,"direction":240,"opportunity":579},"构建全年耦合模型CAWM-S，模拟覆膜滴灌棉田水热盐运移与作物生长。","将HYDRUS-2D的CAWM-H2D与冻融模型SHAW耦合，用南疆两年监测数据","模型精度较高（R²0.48-0.98），揭示最优灌溉量与灌水盐度呈二次关系。","可扩展至多站点、多作物与不同灌溉制度的独立验证，并耦合遥感数据实现区域尺度模拟。","2026-10-22T11:20:12.238655Z"]