[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1241":3,"related-1241":97},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":67,"slug":70,"view_count":71,"doi":72,"paper":73,"created_at":96},1241,"Cocoa climate-smart agriculture adoption in Ghana: why the synergies and trade-offs matter for policy","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1857724","The role of climate-smart agriculture (CSA) in sustainable cocoa production cannot be ignored. However, studies on perceived effectiveness and the socio-economic drivers of CSA among cocoa farmers are underexplored. This study fills the gap by using Spearman’s rank correlation and a multivariate probit model with 455 cocoa farmers in Ghana. Results show high adoption of agroforestry (93.48%) and hybrid planting materials (86.30%), moderate uptake of lining and pegging (52%) and cover cropping (49%), and relatively low adoption of irrigation (23%), drainage construction (30%) and off-farm diversification (33%). Most farmers perceive agroforestry, hybrid varieties, lining and pegging, and cover cropping as effective or highly effective in reducing the impacts of climate variability. In contrast, a large proportion are uncertain about the benefits of off-farm diversification, irrigation, and drainage, revealing important information and perception gaps. There are positive and significant correlations among perceived effectiveness scores (on-farm and off-farm diversification; SWC and drainage). The study found that farming experience, farm size, household size, climate information, distance to the farm, ICT ownership, membership of farmer-based organizations, and regional location significantly shape the adoption of CSA practices. Hence, to promote sustainable cocoa production, a climate-smart cocoa policy should recognize both the complementarities and trade-offs among CSA practices. Therefore, cocoa policymakers should strengthen extension and climate information systems, promote integrated, context-specific CSA packages, improve access to irrigation, and support farmer organizations.",null,"Frontiers in Sustainable Food Systems","2026-08-26T00:00:00Z","论文",10,false,75,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,20,13,2,1,"基于455名农户数据的实证研究，揭示气候智慧农业在可可种植中的采纳差异与协同权衡，对政策制定有参考价值。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","农业政策","气候智慧农业","可可","加纳",{"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":13,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":47},[44,52,57,62],{"code":45,"score":46,"supported":47,"title":48,"abstract":50,"tags":51},"I1",12,true,[49],"smart agriculture",[49],[26],{"code":32,"score":53,"supported":47,"title":54,"abstract":55,"tags":56},3,[],[33],[],{"code":58,"score":21,"supported":13,"title":59,"abstract":60,"tags":61},"B3",[],[],[29],{"code":63,"score":21,"supported":13,"title":64,"abstract":65,"tags":66},"H7",[],[],[28],[68,69],"气候智慧农业 农业政策 智慧农业 加纳","气候智慧农业 农业政策","气候智慧农业农业政策智慧农业加纳-1241",4,"10.3389\u002Ffsufs.2026.1857724",{"doi":72,"openalex_id":74,"authors":75,"venue":9,"cited_by_count":87,"oa_url":88,"card":89,"direction":93,"ingested_from":95},"W7204273129",[76,78,81,83,85],{"name":77,"orcid":8},"Philip Neri Zuobog",{"name":79,"orcid":80},"Gazali Issahaku","https:\u002F\u002Forcid.org\u002F0000-0002-3471-7347",{"name":82,"orcid":8},"Franklin N. Mabe",{"name":84,"orcid":8},"Abraham Zakaria",{"name":86,"orcid":8},"Abdul Razak Baba",0,"https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1857724\u002Fpdf",{"tldr":90,"method":91,"finding":92,"direction":93,"opportunity":94},"研究加纳可可农采纳气候智能农业的驱动因素及协同与权衡，为政策提供建议。","使用Spearman秩相关和多元probit模型分析455名农户数据。","采纳率差异大，感知有效性影响采纳，社会经济因素显著驱动。","农业绿色发展与碳","可探索不同区域CSA实践间的协同与权衡机制，以及如何通过政策组合优化采纳效果。","openalex","2026-09-01T04:03:09.298237Z",{"total":98,"page":21,"page_size":98,"items":99},6,[100,173,288,381,452,525],{"id":101,"title":102,"url":103,"summary":104,"summary_zh":105,"content":8,"source_name":106,"source_url":103,"published_at":107,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":108,"score_detail":109,"sources":113,"tags":115,"primary_topic":120,"secondary_topics":121,"classification":128,"search_phrases":154,"slug":157,"view_count":87,"doi":158,"paper":159,"created_at":172},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":110,"substance":17,"depth":16,"authority":46,"freshness":111,"relevant":21,"comment":112},8,9,"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。",[114],{"name":106,"url":103},[26,116,117,118,119],"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},[122],{"code":123,"slug":124,"name":125,"path":126,"parent_code":127},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":39,"locked":13,"version":40,"method":41,"evidence":129},{"candidates":130,"agricultural_evidence":47},[131,137,143,150],{"code":32,"score":132,"supported":47,"title":133,"abstract":134,"tags":136},15,[33],[135,33],"灌溉",[117],{"code":123,"score":46,"supported":47,"title":138,"abstract":140,"tags":142},[139],"artificial intelligence",[141],"decision support",[116],{"code":144,"score":98,"supported":47,"title":145,"abstract":146,"tags":149},"H2",[],[147,148],"土壤","soil",[],{"code":45,"score":21,"supported":13,"title":151,"abstract":152,"tags":153},[],[],[26],[155,156],"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":158,"openalex_id":160,"authors":161,"venue":106,"cited_by_count":87,"oa_url":103,"card":166,"direction":170,"ingested_from":95},"W7223131205",[162,164],{"name":163,"orcid":8},"Nassima Benkhelifa",{"name":165,"orcid":8},"Yacine Aït Ahmed",{"tldr":167,"method":168,"finding":169,"direction":170,"opportunity":171},"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":174,"title":175,"url":176,"summary":177,"summary_zh":178,"content":8,"source_name":179,"source_url":176,"published_at":107,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":180,"score_detail":181,"sources":183,"tags":185,"primary_topic":189,"secondary_topics":190,"classification":197,"search_phrases":257,"slug":260,"view_count":87,"doi":261,"paper":262,"created_at":287},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":16,"substance":17,"depth":16,"authority":46,"freshness":111,"relevant":21,"comment":182},"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。",[184],{"name":179,"url":176},[26,116,186,187,188],"遥感","作物水分胁迫","精准灌溉",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[191,192],{"code":123,"slug":124,"name":125,"path":126,"parent_code":127},{"code":193,"slug":194,"name":195,"path":196,"parent_code":127},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":39,"locked":13,"version":40,"method":41,"evidence":198},{"candidates":199,"agricultural_evidence":47},[200,204,210,217,224,230,237,244,251],{"code":32,"score":132,"supported":47,"title":201,"abstract":202,"tags":203},[33],[135,33],[188],{"code":123,"score":12,"supported":47,"title":205,"abstract":206,"tags":209},[],[207,208,139],"机器学习","machine learning",[116],{"code":193,"score":111,"supported":47,"title":211,"abstract":212,"tags":216},[],[213,214,215],"物联网","internet of things","IoT",[],{"code":45,"score":218,"supported":47,"title":219,"abstract":220,"tags":223},7,[],[221,222],"数字孪生","digital twin",[26],{"code":225,"score":218,"supported":47,"title":226,"abstract":227,"tags":229},"I3",[],[186,228],"remote sensing",[186],{"code":231,"score":98,"supported":47,"title":232,"abstract":233,"tags":236},"A2",[],[234,235],"小麦","wheat",[],{"code":238,"score":98,"supported":47,"title":239,"abstract":240,"tags":243},"A3",[],[241,242],"玉米","maize",[],{"code":245,"score":98,"supported":47,"title":246,"abstract":247,"tags":250},"A5",[],[248,249],"马铃薯","potato",[],{"code":252,"score":53,"supported":47,"title":253,"abstract":254,"tags":256},"B2",[],[255],"葡萄",[],[258,259],"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":261,"openalex_id":263,"authors":264,"venue":179,"cited_by_count":87,"oa_url":176,"card":280,"direction":286,"ingested_from":95},"W7223088921",[265,268,271,274,277],{"name":266,"orcid":267},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":269,"orcid":270},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":272,"orcid":273},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":275,"orcid":276},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":278,"orcid":279},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":281,"method":282,"finding":283,"direction":284,"opportunity":285},"综述物候期对热成像CWSI阈值的影响，提出物候感知灌溉调度框架。","综述葡萄、小麦、玉米等作物的热遥感CWSI研究，并审计56项AI\u002F热成像研究。","78.6%研究未显式纳入物候，仅5.4%编码物候期；CWSI与产量在生殖期关联最强。","农业遥感与作物表型","可开展多站点多年物候期特异性CWSI阈值标定与标准化物候定义研究。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:24.526683Z",{"id":289,"title":290,"url":291,"summary":292,"summary_zh":293,"content":8,"source_name":294,"source_url":291,"published_at":107,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":295,"score_detail":296,"sources":298,"tags":300,"primary_topic":303,"secondary_topics":304,"classification":307,"search_phrases":357,"slug":360,"view_count":87,"doi":361,"paper":362,"created_at":380},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":16,"substance":17,"depth":16,"authority":19,"freshness":111,"relevant":21,"comment":297},"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。",[299],{"name":294,"url":291},[26,116,213,301,302],"精准农业","气候韧性",{"code":123,"slug":124,"name":125,"path":126,"parent_code":127},[305,306],{"code":193,"slug":194,"name":195,"path":196,"parent_code":127},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":13,"version":40,"method":41,"evidence":308},{"candidates":309,"agricultural_evidence":47},[310,318,324,330,335,341,345,351],{"code":123,"score":16,"supported":47,"title":311,"abstract":312,"tags":317},[139],[313,207,314,208,315,316],"深度学习","计算机视觉","deep learning","computer vision",[116],{"code":45,"score":319,"supported":47,"title":320,"abstract":322,"tags":323},16,[321],"precision agriculture",[301,321],[26,301],{"code":193,"score":12,"supported":47,"title":325,"abstract":326,"tags":329},[],[213,327,215,328],"边缘计算","edge computing",[213],{"code":32,"score":111,"supported":47,"title":331,"abstract":332,"tags":334},[],[135,333,33],"水资源",[],{"code":63,"score":218,"supported":47,"title":336,"abstract":337,"tags":340},[],[338,339],"气候变化","climate change",[302],{"code":144,"score":98,"supported":47,"title":342,"abstract":343,"tags":344},[],[147,148],[],{"code":346,"score":53,"supported":47,"title":347,"abstract":348,"tags":350},"A7",[],[349],"间作",[],{"code":352,"score":53,"supported":47,"title":353,"abstract":354,"tags":356},"F3",[],[355],"养分管理",[],[358,359],"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":361,"openalex_id":363,"authors":364,"venue":294,"cited_by_count":87,"oa_url":291,"card":375,"direction":286,"ingested_from":95},"W7223121456",[365,368,370,372],{"name":366,"orcid":367},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":369,"orcid":8},"Oluwadare OYEBODE",{"name":371,"orcid":8},"Iretiayo Adelaiye",{"name":373,"orcid":374},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":376,"method":377,"finding":378,"direction":170,"opportunity":379},"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":382,"title":383,"url":384,"summary":385,"summary_zh":386,"content":8,"source_name":387,"source_url":384,"published_at":388,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":108,"score_detail":389,"sources":392,"tags":394,"primary_topic":396,"secondary_topics":397,"classification":399,"search_phrases":431,"slug":434,"view_count":87,"doi":435,"paper":436,"created_at":451},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":46,"substance":18,"depth":390,"authority":46,"freshness":110,"relevant":21,"comment":391},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。",[393],{"name":387,"url":384},[26,116,395,241,117],"水稻",{"code":123,"slug":124,"name":125,"path":126,"parent_code":127},[398],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":13,"version":40,"method":41,"evidence":400},{"candidates":401,"agricultural_evidence":47},[402,406,410,416,420,427],{"code":123,"score":16,"supported":47,"title":403,"abstract":404,"tags":405},[208],[207,208,141],[116],{"code":32,"score":132,"supported":47,"title":407,"abstract":408,"tags":409},[33],[135,33],[117],{"code":411,"score":218,"supported":47,"title":412,"abstract":413,"tags":415},"A1",[],[395,414],"paddy",[395],{"code":238,"score":218,"supported":47,"title":417,"abstract":418,"tags":419},[],[241,242],[241],{"code":421,"score":98,"supported":47,"title":422,"abstract":423,"tags":426},"G2",[],[424,425],"小农户","smallholder",[],{"code":45,"score":21,"supported":13,"title":428,"abstract":429,"tags":430},[],[],[26],[432,433],"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":435,"openalex_id":437,"authors":438,"venue":387,"cited_by_count":87,"oa_url":384,"card":446,"direction":170,"ingested_from":95},"W7223016583",[439,442,444],{"name":440,"orcid":441},"Muhammad Rusdi","https:\u002F\u002Forcid.org\u002F0000-0002-1048-5000",{"name":443,"orcid":8},"Zaenuddin Zaenuddin",{"name":445,"orcid":8},"Hendra Sanjaya",{"tldr":447,"method":448,"finding":449,"direction":286,"opportunity":450},"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",{"id":453,"title":454,"url":455,"summary":456,"summary_zh":457,"content":8,"source_name":106,"source_url":455,"published_at":458,"category":11,"cover_url":8,"hotness":459,"is_selected":13,"score":460,"score_detail":461,"sources":464,"tags":468,"primary_topic":471,"secondary_topics":472,"classification":474,"search_phrases":502,"slug":505,"view_count":87,"doi":506,"paper":507,"created_at":524},5474,"Real-World Impact: Transforming Agriculture with Technology A Multi-Stakeholder Review of Smart Monitoring, Precision Irrigation, and Digital Value-Chain Integration","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23510500","There's a deliberate and significant transition from input to information. This article combines peer-reviewed evidence and public-sector data on six interrelated technology clusters that are fundamentally changing on-farm and off-farm decision making artificial intelligence (AI) based Farm Management Platforms, Internet of Things (IoT) smart irrigation, aerial and satellite crop monitoring, blockchain-enabled supply-chain digitisation, sustainability and carbon-tracking instruments, and farm data analytics dashboards. In the literature examined, precision tools have been correlated with 15% to 30% gains in crop yields, 30% to 55% of water saved during irrigation, and 10% to 20% of operational costs reduced, all with a range of variability based on the crop, the geographical region, and the size of the farm. It examines the mechanisms of these effects on the one hand and the differential rates of benefit, on the other, between large commercial farms and smallholders, and the policy and infrastructural conditions that drive actual adoption. It also highlights areas of current evidence which are lacking, such as limited long horizon field trials, poor interoperability standards, and lack of social outcome data. The article ends with a discussion on how to turn pilots into lasting, system-level change in food production, both from the government and enterprise side, as well as from the perspective of small and medium producers.","从投入要素到信息的转变是有意为之且意义重大的。本文结合经同行评审的证据与公共部门数据，聚焦六个相互关联、正在从根本上改变农场内外决策方式的技术集群：基于人工智能（AI）的农场管理平台、物联网（IoT）智能灌溉、航空与卫星作物监测、区块链赋能的供应链数字化、可持续性与碳追踪工具，以及农场数据分析仪表盘。在所考察的文献中，精准工具与作物产量提升15%至30%、灌溉用水节约30%至55%、运营成本降低10%至20%相关联，且这些效果因作物、地理区域和农场规模而存在不同程度的差异。本文一方面考察这些效应的作用机制，另一方面分析大型商业农场与小农户之间受益程度的差异，以及驱动实际采用的政策与基础设施条件。文章还指出了当前证据的薄弱之处，如缺乏长期田间试验、互操作性标准不完善以及社会成效数据缺失。文章最后讨论了如何从政府与企业两侧，以及中小生产者视角出发，将试点项目转化为粮食生产中持久、系统层面的变革。","2026-11-25T00:00:00Z",25,73,{"impact":17,"substance":462,"depth":390,"authority":19,"freshness":87,"relevant":21,"comment":463},21,"多利益相关方综述整合六类技术集群的量化效益与采纳差异，信息增量足、产业参考价值高，但发布于一个月后，时效性不足，暂不宜进入每日精选。",[465,466],{"name":106,"url":455},{"name":106,"url":467},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527988",[26,116,469,470,188],"农业物联网","区块链溯源",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[473],{"code":193,"slug":194,"name":195,"path":196,"parent_code":127},{"status":39,"locked":13,"version":40,"method":41,"evidence":475},{"candidates":476,"agricultural_evidence":47},[477,481,485,489,493,498],{"code":32,"score":132,"supported":47,"title":478,"abstract":479,"tags":480},[33],[135,33],[188],{"code":193,"score":12,"supported":47,"title":482,"abstract":483,"tags":484},[],[213,214,215],[469],{"code":123,"score":71,"supported":47,"title":486,"abstract":487,"tags":488},[],[139],[116],{"code":421,"score":53,"supported":47,"title":490,"abstract":491,"tags":492},[],[424],[],{"code":494,"score":21,"supported":13,"title":495,"abstract":496,"tags":497},"E4",[],[],[470],{"code":45,"score":21,"supported":13,"title":499,"abstract":500,"tags":501},[],[],[26],[503,504],"Zenodo 智慧农业 多利益相关方综述","精准灌溉 物联网 区块链 供应链","Zenodo智慧农业多利益相关方综述-5474","10.5281\u002Fzenodo.23510500",{"doi":506,"openalex_id":508,"authors":509,"venue":106,"cited_by_count":87,"oa_url":455,"card":518,"direction":286,"ingested_from":95},"W7223034914",[510,513,515],{"name":511,"orcid":512},"A. Shaji George","https:\u002F\u002Forcid.org\u002F0000-0002-8677-3682",{"name":514,"orcid":8},"Dr.T.Baskar",{"name":516,"orcid":517},"P. Balaji Srikaanth","https:\u002F\u002Forcid.org\u002F0000-0003-4717-7367",{"tldr":519,"method":520,"finding":521,"direction":522,"opportunity":523},"综述六大农业数字技术集群对农场决策的变革及实际效益与采纳差异。","综合同行评议证据与公共部门数据，多利益相关方综述六类技术。","精准技术可增产15-30%、节水30-55%、降本10-20%，但小农获益不均。","数字乡村与农业信息化","缺乏长期田间试验、互操作标准与社会效益数据，小农采纳机制是研究空白。","2026-10-21T23:30:08.684798Z",{"id":526,"title":527,"url":528,"summary":529,"summary_zh":530,"content":8,"source_name":106,"source_url":528,"published_at":388,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":531,"score_detail":532,"sources":534,"tags":536,"primary_topic":540,"secondary_topics":546,"classification":548,"search_phrases":585,"slug":588,"view_count":87,"doi":589,"paper":590,"created_at":600},5381,"Climate-Smart Floriculture: Strategies for Sustainable Flower Production Under a Changing Climate","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23467834","Floriculture is a high-value horticultural sector comprising cut flowers, potted flowering plants, foliage plants, ornamental bulbs and nursery plants. The sector is increasingly exposed to climate variability through rising temperature, drought, irregular rainfall, extreme weather events, elevated atmospheric CO₂ and changing pest and disease pressure. These stresses can influence vegetative growth, flowering, flower quality, production scheduling and postharvest performance, thereby affecting the economic value of ornamental crops. Climate-smart floriculture provides an integrated framework for improving resilience while maintaining productivity and resource-use efficiency. Key strategies include climate-resilient cultivar selection, efficient irrigation, soil-moisture conservation, protected cultivation, microclimate regulation, integrated pest management and precision technologies. Genetic improvement offers a long-term approach for developing cultivars capable of maintaining desirable ornamental traits under environmental stress, whereas protected cultivation can buffer crops against external climatic fluctuations. Efficient irrigation, rainwater harvesting and improved soil management can reduce vulnerability to water scarcity, while integrated pest management can address climate-associated changes in pest and disease pressure. Precision agriculture, controlled-environment agriculture and machine-learning-based decision systems offer additional opportunities for real-time monitoring and resource optimisation. However, technological adoption is influenced by investment costs, knowledge availability, extension services, market access and supportive policies. This article discusses the major climate-related risks to floriculture and evaluates integrated strategies for developing productive, resource-efficient and climate-resilient flower production systems.","花卉产业是一个高附加值的园艺部门，包括切花、盆栽开花植物、观叶植物、观赏球根植物和苗木。该产业日益受到气候变率的威胁，表现为气温升高、干旱、降雨不规律、极端天气事件、大气CO₂浓度升高以及病虫害压力变化。这些胁迫可影响营养生长、开花、花卉品质、生产调度和采后性能，进而影响观赏作物的经济价值。气候智慧型花卉产业提供了一个综合框架，可在保持生产力和资源利用效率的同时提高韧性。关键策略包括气候韧性品种选择、高效灌溉、土壤水分保持、设施栽培、微气候调控、病虫害综合治理和精准技术。遗传改良为培育能够在环境胁迫下保持理想观赏性状的品种提供了长期途径，而设施栽培可缓冲作物免受外部气候波动的影响。高效灌溉、雨水收集和改善的土壤管理可降低对水资源短缺的脆弱性，而病虫害综合治理可应对气候相关的病虫害压力变化。精准农业、受控环境农业和基于机器学习的决策系统为实时监测和资源优化提供了额外机会。然而，技术采用受到投资成本、知识可得性、推广服务、市场准入和支持性政策的影响。本文讨论了花卉产业面临的主要气候相关风险，并评估了发展高产、资源高效和气候韧性花卉生产系统的综合策略。",77,{"impact":16,"substance":18,"depth":390,"authority":19,"freshness":111,"relevant":21,"comment":533},"系统综述花卉业气候风险与综合应对策略，具行业参考价值，但属综述性论文、非突破性成果。",[535],{"name":106,"url":528},[301,537,28,538,539],"病虫害防控","花卉产业","设施园艺",{"code":541,"slug":542,"name":543,"path":544,"parent_code":545},"B4","flowers","花卉与观赏植物","\u002Ftopic\u002Fhorticulture\u002Fflowers","B",[547],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":13,"version":40,"method":41,"evidence":549},{"candidates":550,"agricultural_evidence":47},[551,558,562,568,572,576,580],{"code":541,"score":16,"supported":47,"title":552,"abstract":554,"tags":557},[553],"floriculture",[555,556,553],"花卉","ornamental",[538],{"code":32,"score":111,"supported":47,"title":559,"abstract":560,"tags":561},[],[135,333,33],[],{"code":63,"score":218,"supported":47,"title":563,"abstract":564,"tags":567},[],[565,566],"干旱","drought",[28],{"code":45,"score":218,"supported":47,"title":569,"abstract":570,"tags":571},[],[301,321],[301],{"code":144,"score":98,"supported":47,"title":573,"abstract":574,"tags":575},[],[147,148],[],{"code":123,"score":53,"supported":47,"title":577,"abstract":578,"tags":579},[],[207],[],{"code":581,"score":21,"supported":13,"title":582,"abstract":583,"tags":584},"B7",[],[],[539],[586,587],"花卉 可持续生产 气候变化","气候智慧农业 病虫害防控 精准农业 花卉产业","花卉可持续生产气候变化-5381","10.5281\u002Fzenodo.23467834",{"doi":589,"openalex_id":591,"authors":592,"venue":106,"cited_by_count":87,"oa_url":528,"card":595,"direction":286,"ingested_from":95},"W7222973900",[593],{"name":594,"orcid":8},"Shriya Gupta1 and Harish Kumar2",{"tldr":596,"method":597,"finding":598,"direction":286,"opportunity":599},"综述气候智慧型花卉生产策略，评估气候变化风险与综合适应措施。","文献综述，整合抗逆品种、灌溉、保护地栽培、IPM与精准技术。","综合策略可提升花卉气候韧性，但技术采纳受成本、知识与政策制约。","花卉领域精准农业与机器学习决策应用少，可研究低成本智能环境调控与品种-技术协同。","2026-10-20T23:30:08.681802Z"]