[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3825":3,"related-3825":118},{"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":50,"search_phrases":93,"slug":96,"view_count":97,"doi":98,"paper":99,"created_at":117},3825,"Hybrid Rule-Based and Machine Learning Decision-Support Framework for Smart Paddy Farming","https:\u002F\u002Fdoi.org\u002F10.4038\u002Fatrsj.v6i1.70","Paddy farming decisions are affected by changing weather conditions, water availability, soil conditions, and crop health, creating a need for decision-support approaches that combine agronomic knowledge with data-driven prediction. This research presents a hybrid rule-based and machine-learning decision-support solution for paddy management that combines interpretable agronomic rules and lightweight predictive models. The temporal alignment, interpolation, normalization, feature engineering, and crop-stage aggregation of weather and NDVI data were integrated and preprocessed. It is a rule-based engine with 63 expert rules that was fused with Decision Tree, Logistic Regression, and Random Forest models using a weighted decision-fusion mechanism. The hybrid model performed with 90% accuracy, 0.91 precision, 0.88 recall, and 0.89 F1-score, while the rule-based model had 72% accuracy, and the best standalone Random Forest model had 86% accuracy. The hybrid model also yielded the minimum RMSE of 7.6. The results of the simulation scenarios showed increases in yield of 3–17% and savings in irrigation-water use of 12.4%. There were 10 farmers and 2 agricultural officers who participated in the preliminary usability test and were scored from 4.0 to 4.8 out of 5. The key is an adaptive machine-learning prediction integrated with clear agronomic logic, in a lightweight advisory system. The yields and water-saving benefits, however, are simulated, and experimental testing on a wider range of locations and seasons will be needed to determine the full agronomic benefits.","水稻种植决策受到天气条件变化、水资源可用性、土壤条件和作物健康状况的影响，因此需要将农艺知识与数据驱动预测相结合的决策支持方法。本研究提出了一种基于规则与机器学习混合的水稻管理决策支持方案，将可解释的农艺规则与轻量级预测模型相结合。研究对天气和NDVI数据进行了时间对齐、插值、归一化、特征工程和作物阶段聚合的集成与预处理。构建了一个包含63条专家规则的基于规则的引擎，并通过加权决策融合机制将其与决策树、逻辑回归和随机森林模型相融合。混合模型的准确率为90%，精确率为0.91，召回率为0.88，F1分数为0.89，而基于规则的模型准确率为72%，最佳独立随机森林模型准确率为86%。混合模型还取得了最低的RMSE值7.6。模拟情景结果显示，产量提高了3%–17%，灌溉用水节省了12.4%。共有10名农民和2名农业官员参与了初步可用性测试，评分为4.0至4.8分（满分5分）。其核心在于将自适应机器学习预测与清晰的农艺逻辑相结合，构建一个轻量级咨询系统。然而，产量提升和节水效益均为模拟结果，需要在更广泛的地点和季节进行实验测试，以确定其完整的农艺效益。",null,"Journal of Agro-Technology and Rural Sciences","2026-09-29T00:00:00Z","论文",10,false,68,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":20,"relevant":21,"comment":22},12,20,17,9,1,"提出规则与机器学习融合的水稻决策支持框架，指标与仿真结果具体，但验证样本小、增产节水为模拟值，属细分领域方法学进展。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","遥感监测","水稻种植","灌溉节水",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[38,44],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":51,"locked":14,"version":52,"method":53,"evidence":54},"classified","2026-10-22.2","rules",{"candidates":55,"agricultural_evidence":58},[56,63,69,75,83,88],{"code":32,"score":57,"supported":58,"title":59,"abstract":61,"tags":62},15,true,[60],"paddy",[34,60],[29],{"code":45,"score":17,"supported":58,"title":64,"abstract":66,"tags":68},[65],"machine learning",[67],"机器学习",[27],{"code":39,"score":20,"supported":58,"title":70,"abstract":71,"tags":74},[],[72,73,40],"灌溉","水资源",[],{"code":76,"score":77,"supported":58,"title":78,"abstract":79,"tags":82},"H2",6,[],[80,81],"土壤","soil",[],{"code":84,"score":21,"supported":14,"title":85,"abstract":86,"tags":87},"I1",[],[],[26],{"code":89,"score":21,"supported":14,"title":90,"abstract":91,"tags":92},"I3",[],[],[28],[94,95],"水稻 决策支持 机器学习","NDVI 水稻 灌溉","水稻决策支持机器学习-3825",2,"10.4038\u002Fatrsj.v6i1.70",{"doi":98,"openalex_id":100,"authors":101,"venue":10,"cited_by_count":107,"oa_url":108,"card":109,"direction":115,"ingested_from":116},"W7214735276",[102,105],{"name":103,"orcid":104},"Sawjanya Sathyaseelan","https:\u002F\u002Forcid.org\u002F0009-0003-1194-527X",{"name":106,"orcid":9},"K. Sriram",0,"https:\u002F\u002Fatrsj.sljol.info\u002Farticles\u002F70\u002Ffiles\u002F6abb528a0792a.pdf",{"tldr":110,"method":111,"finding":112,"direction":113,"opportunity":114},"提出规则与机器学习混合的水稻决策支持框架，提升决策精度并节水增产。","融合63条专家规则与决策树、逻辑回归、随机森林，处理天气和NDVI数据。","混合模型准确率90%，模拟增产3-17%，节水12.4%，可用性评分4.0-4.8。","农业人工智能与决策模型","当前增产节水为模拟结果，未来需在多地点多季节开展田间试验验证农艺效益。","智慧农业 \u002F 农业物联网","openalex","2026-09-30T23:30:09.772909Z",{"total":77,"page":21,"page_size":77,"items":119},[120,186,246,354,446,513],{"id":121,"title":122,"url":123,"summary":124,"summary_zh":125,"content":9,"source_name":126,"source_url":123,"published_at":127,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":128,"score_detail":129,"sources":134,"tags":136,"primary_topic":138,"secondary_topics":139,"classification":141,"search_phrases":166,"slug":169,"view_count":97,"doi":170,"paper":171,"created_at":185},4431,"A multimodal deep learning for rice crop health monitoring using vegetation index feature selection","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-74895-5","Crop health monitoring is essential for sustaining agricultural productivity and ensuring stable production, particularly in major crop-producing regions that face challenges due to climate variability, resource constraints, and spatial heterogeneity. Traditional methods struggle to effectively integrate spatial heterogeneity and environmental variability from multi-source remote sensing (RS) data. To address this problem, the proposed work explores the role of satellite imagery in monitoring rice crop health with the help of Vegetation Indices (VIs) under the impact of changing weather conditions. Multi-temporal Sentinel-1 and Sentinel-2 satellite data were acquired from a rice cultivation field in Gorakhpur, Uttar Pradesh, India, during three Kharif growing seasons (2023-2025). In this work, we propose a supervised MI-based feature selection framework employing Otsu’s adaptive thresholding for selection of VIs that strongly determine the health of rice crop. To ensure data integrity, cloud-contaminated observations were systematically excluded through a two-stage cleaning process. Further, we propose a multimodal deep learning (DL) framework that fuses a Swin Transformer (Swin-T) backbone for spatial feature extraction from vegetation index imagery with a Multi-Layer Perceptron (MLP) for structured numerical modeling of meteorological variables. The proposed framework achieved 94.05% accuracy on an independent held-out test set and a mean accuracy of \\(92.12 \\pm 5.28\\) % under 10-fold cross-validation, consistently outperforming competing hybrid deep learning baselines. These results highlight that integrating transformer-based spatial representations with environmental information provides an efficient approach for rice crop health classification. The proposed framework can be extended to multi-region crop monitoring by incorporating additional vegetation index modalities and diverse climatic conditions.","作物健康监测对于维持农业生产力、保障稳定生产至关重要，尤其是在面临气候变异性、资源约束和空间异质性挑战的主要作物产区。传统方法难以有效整合多源遥感（RS）数据中的空间异质性和环境变异性。为解决这一问题，本研究探索了卫星影像在天气条件变化影响下借助植被指数（VIs）监测水稻作物健康的作用。研究在印度北方邦戈勒克布尔的稻田中，于三个水稻生长季（2023—2025年）获取了多时相Sentinel-1和Sentinel-2卫星数据。在本研究中，我们提出了一种基于互信息（MI）的监督式特征选择框架，采用Otsu自适应阈值法筛选对水稻作物健康具有强判别能力的植被指数。为确保数据完整性，通过两阶段清洗流程系统性地剔除了受云污染的观测数据。此外，我们提出了一种多模态深度学习（DL）框架，该框架融合了Swin Transformer（Swin-T）主干网络用于从植被指数影像中提取空间特征，以及多层感知机（MLP）用于气象变量的结构化数值建模。所提框架在独立留出测试集上达到了94.05%的准确率，在10折交叉验证下平均准确率为\\(92.12 \\pm 5.28\\)%，持续优于竞争的混合深度学习基线模型。这些结果表明，将基于Transformer的空间表征与环境信息相结合，为水稻作物健康分类提供了一种高效方法。所提框架可通过纳入更多植被指数模态和多样化气候条件，扩展至多区域作物监测。","Scientific Reports","2026-10-07T00:00:00Z",81,{"impact":130,"substance":131,"depth":130,"authority":132,"freshness":20,"relevant":21,"comment":133},18,22,14,"方法新颖、数据跨三个生长季且精度可靠，属农业遥感与AI交叉的细分领域进展，值得进入每日精选。",[135],{"name":126,"url":123},[26,27,34,28,137],"作物长势",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[140],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":14,"version":52,"method":53,"evidence":142},{"candidates":143,"agricultural_evidence":58},[144,149,155,162],{"code":32,"score":130,"supported":58,"title":145,"abstract":146,"tags":148},[33],[34,147,33],"稻田",[34],{"code":45,"score":57,"supported":58,"title":150,"abstract":152,"tags":154},[151],"deep learning",[153,151],"深度学习",[27],{"code":89,"score":156,"supported":58,"title":157,"abstract":158,"tags":161},7,[],[159,160],"遥感","remote sensing",[28],{"code":84,"score":21,"supported":14,"title":163,"abstract":164,"tags":165},[],[],[26],[167,168],"Sentinel-2 水稻 遥感监测","Swin Transformer 作物健康","Sentinel-2水稻遥感监测-4431","10.1038\u002Fs41598-026-74895-5",{"doi":170,"openalex_id":172,"authors":173,"venue":126,"cited_by_count":107,"oa_url":123,"card":179,"direction":183,"ingested_from":116},"W7220838898",[174,177],{"name":175,"orcid":176},"Shashank Gupta","https:\u002F\u002Forcid.org\u002F0000-0003-1291-7951",{"name":178,"orcid":9},"Shefali Arora Chouhan",{"tldr":180,"method":181,"finding":182,"direction":183,"opportunity":184},"融合Sentinel-1\u002F2植被指数与气象数据，用多模态深度学习监测水稻健康。","Sentinel-1\u002F2多时相数据、MI特征选择、Otsu阈值、Swin-T+M","独立测试准确率94.05%，10折交叉验证92.12%，优于混合基线。","农业遥感与作物表型","可扩展至多区域多作物，探索更多植被指数模态与极端气候下的泛化能力。","2026-10-08T23:30:29.254071Z",{"id":187,"title":188,"url":189,"summary":190,"summary_zh":191,"content":9,"source_name":192,"source_url":189,"published_at":193,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":194,"score_detail":195,"sources":198,"tags":200,"primary_topic":204,"secondary_topics":205,"classification":207,"search_phrases":228,"slug":231,"view_count":107,"doi":232,"paper":233,"created_at":245},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":196,"substance":131,"depth":130,"authority":17,"freshness":20,"relevant":21,"comment":197},8,"该研究提出在入渗模型失配条件下仍有效的灌溉决策安全保证框架，并给出可审计的试验设计，方法新颖、结论可靠，但属细分方法学研究，产业影响有限，适合作为专业精选而非头条。",[199],{"name":192,"url":189},[26,27,201,202,203],"灌溉决策","安全保证","试验设计",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[206],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":14,"version":52,"method":53,"evidence":208},{"candidates":209,"agricultural_evidence":58},[210,214,220,224],{"code":39,"score":57,"supported":58,"title":211,"abstract":212,"tags":213},[40],[72,40],[201],{"code":45,"score":17,"supported":58,"title":215,"abstract":217,"tags":219},[216],"artificial intelligence",[218],"decision support",[27],{"code":76,"score":77,"supported":58,"title":221,"abstract":222,"tags":223},[],[80,81],[],{"code":84,"score":21,"supported":14,"title":225,"abstract":226,"tags":227},[],[],[26],[229,230],"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":232,"openalex_id":234,"authors":235,"venue":192,"cited_by_count":107,"oa_url":189,"card":240,"direction":113,"ingested_from":116},"W7223131205",[236,238],{"name":237,"orcid":9},"Nassima Benkhelifa",{"name":239,"orcid":9},"Yacine Aït Ahmed",{"tldr":241,"method":242,"finding":243,"direction":113,"opportunity":244},"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":247,"title":248,"url":249,"summary":250,"summary_zh":251,"content":9,"source_name":252,"source_url":249,"published_at":193,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":253,"score_detail":254,"sources":256,"tags":258,"primary_topic":261,"secondary_topics":262,"classification":269,"search_phrases":325,"slug":328,"view_count":107,"doi":329,"paper":330,"created_at":353},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":130,"substance":131,"depth":130,"authority":17,"freshness":20,"relevant":21,"comment":255},"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。",[257],{"name":252,"url":249},[26,27,159,259,260],"作物水分胁迫","精准灌溉",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[263,264],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"code":265,"slug":266,"name":267,"path":268,"parent_code":49},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":51,"locked":14,"version":52,"method":53,"evidence":270},{"candidates":271,"agricultural_evidence":58},[272,276,280,287,293,297,304,311,318],{"code":39,"score":57,"supported":58,"title":273,"abstract":274,"tags":275},[40],[72,40],[260],{"code":45,"score":13,"supported":58,"title":277,"abstract":278,"tags":279},[],[67,65,216],[27],{"code":265,"score":20,"supported":58,"title":281,"abstract":282,"tags":286},[],[283,284,285],"物联网","internet of things","IoT",[],{"code":84,"score":156,"supported":58,"title":288,"abstract":289,"tags":292},[],[290,291],"数字孪生","digital twin",[26],{"code":89,"score":156,"supported":58,"title":294,"abstract":295,"tags":296},[],[159,160],[159],{"code":298,"score":77,"supported":58,"title":299,"abstract":300,"tags":303},"A2",[],[301,302],"小麦","wheat",[],{"code":305,"score":77,"supported":58,"title":306,"abstract":307,"tags":310},"A3",[],[308,309],"玉米","maize",[],{"code":312,"score":77,"supported":58,"title":313,"abstract":314,"tags":317},"A5",[],[315,316],"马铃薯","potato",[],{"code":319,"score":320,"supported":58,"title":321,"abstract":322,"tags":324},"B2",3,[],[323],"葡萄",[],[326,327],"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":329,"openalex_id":331,"authors":332,"venue":252,"cited_by_count":107,"oa_url":249,"card":348,"direction":115,"ingested_from":116},"W7223088921",[333,336,339,342,345],{"name":334,"orcid":335},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":337,"orcid":338},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":340,"orcid":341},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":343,"orcid":344},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":346,"orcid":347},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":349,"method":350,"finding":351,"direction":183,"opportunity":352},"综述物候期对热成像CWSI阈值的影响，提出物候感知灌溉调度框架。","综述葡萄、小麦、玉米等作物的热遥感CWSI研究，并审计56项AI\u002F热成像研究。","78.6%研究未显式纳入物候，仅5.4%编码物候期；CWSI与产量在生殖期关联最强。","可开展多站点多年物候期特异性CWSI阈值标定与标准化物候定义研究。","2026-10-22T11:20:24.526683Z",{"id":355,"title":356,"url":357,"summary":358,"summary_zh":359,"content":9,"source_name":360,"source_url":357,"published_at":193,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":361,"score_detail":362,"sources":365,"tags":367,"primary_topic":370,"secondary_topics":371,"classification":374,"search_phrases":422,"slug":425,"view_count":107,"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":130,"substance":131,"depth":130,"authority":363,"freshness":20,"relevant":21,"comment":364},13,"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。",[366],{"name":360,"url":357},[26,27,283,368,369],"精准农业","气候韧性",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},[372,373],{"code":265,"slug":266,"name":267,"path":268,"parent_code":49},{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":51,"locked":14,"version":52,"method":53,"evidence":375},{"candidates":376,"agricultural_evidence":58},[377,383,389,395,399,406,410,416],{"code":45,"score":130,"supported":58,"title":378,"abstract":379,"tags":382},[216],[153,67,380,65,151,381],"计算机视觉","computer vision",[27],{"code":84,"score":384,"supported":58,"title":385,"abstract":387,"tags":388},16,[386],"precision agriculture",[368,386],[26,368],{"code":265,"score":13,"supported":58,"title":390,"abstract":391,"tags":394},[],[283,392,285,393],"边缘计算","edge computing",[283],{"code":39,"score":20,"supported":58,"title":396,"abstract":397,"tags":398},[],[72,73,40],[],{"code":400,"score":156,"supported":58,"title":401,"abstract":402,"tags":405},"H7",[],[403,404],"气候变化","climate change",[369],{"code":76,"score":77,"supported":58,"title":407,"abstract":408,"tags":409},[],[80,81],[],{"code":411,"score":320,"supported":58,"title":412,"abstract":413,"tags":415},"A7",[],[414],"间作",[],{"code":417,"score":320,"supported":58,"title":418,"abstract":419,"tags":421},"F3",[],[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":360,"cited_by_count":107,"oa_url":357,"card":440,"direction":115,"ingested_from":116},"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":113,"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":452,"source_url":449,"published_at":453,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":194,"score_detail":454,"sources":456,"tags":458,"primary_topic":459,"secondary_topics":460,"classification":462,"search_phrases":492,"slug":495,"view_count":107,"doi":496,"paper":497,"created_at":512},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":17,"substance":18,"depth":19,"authority":17,"freshness":196,"relevant":21,"comment":455},"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。",[457],{"name":452,"url":449},[26,27,34,308,201],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},[461],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":51,"locked":14,"version":52,"method":53,"evidence":463},{"candidates":464,"agricultural_evidence":58},[465,469,473,477,481,488],{"code":45,"score":130,"supported":58,"title":466,"abstract":467,"tags":468},[65],[67,65,218],[27],{"code":39,"score":57,"supported":58,"title":470,"abstract":471,"tags":472},[40],[72,40],[201],{"code":32,"score":156,"supported":58,"title":474,"abstract":475,"tags":476},[],[34,60],[34],{"code":305,"score":156,"supported":58,"title":478,"abstract":479,"tags":480},[],[308,309],[308],{"code":482,"score":77,"supported":58,"title":483,"abstract":484,"tags":487},"G2",[],[485,486],"小农户","smallholder",[],{"code":84,"score":21,"supported":14,"title":489,"abstract":490,"tags":491},[],[],[26],[493,494],"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":496,"openalex_id":498,"authors":499,"venue":452,"cited_by_count":107,"oa_url":449,"card":507,"direction":113,"ingested_from":116},"W7223016583",[500,503,505],{"name":501,"orcid":502},"Muhammad Rusdi","https:\u002F\u002Forcid.org\u002F0000-0002-1048-5000",{"name":504,"orcid":9},"Zaenuddin Zaenuddin",{"name":506,"orcid":9},"Hendra Sanjaya",{"tldr":508,"method":509,"finding":510,"direction":115,"opportunity":511},"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":514,"title":515,"url":516,"summary":517,"summary_zh":518,"content":9,"source_name":126,"source_url":516,"published_at":519,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":520,"score_detail":521,"sources":523,"tags":525,"primary_topic":529,"secondary_topics":530,"classification":532,"search_phrases":552,"slug":555,"view_count":21,"doi":556,"paper":557,"created_at":582},4993,"Effects of within-season temporal aggregation of weather variables on performance and interpretability in rice yield machine learning models","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-75646-2","While crop responses are inherently stage-dependent, machine learning yield models often rely on weather variables aggregated over the entire growing season. This study evaluates how within-season temporal aggregation of weather variables affects both predictive performance and interpretability in machine learning-based rice yield modeling. Using CYGMA (Crop Yield Growth Model with Assumptions on Climate and Socioeconomics)-simulated potential yield as a synthetic response variable, we minimized confounding effects from observational noise and management variability, enabling a clearer evaluation of the role of within-season weather aggregation. Daily weather variables were averaged over the rice growing period after dividing each season into multiple equal-length segments, and several machine learning algorithms were trained and evaluated using temporally independent validation. Increasing within-season temporal resolution improved model performance up to a moderate level, after which gains diminished. Interpretation using SHAP (SHapley Additive exPlanations) and response diagnostics consistently indicated that yield sensitivity concentrates in specific windows of the season, with temperature-related effects emerging most strongly early in the season and humidity-related effects later. Finer temporal aggregation sharpened the identification of window-specific nonlinear and threshold-like responses that were obscured under coarse seasonal averages. Overall, moderate within-season aggregation provides a practical balance between predictive skill and mechanistic interpretability, supporting more reliable identification of phenology-relevant meteorological risks for climate-impact assessment and decision support.","尽管作物响应本质上具有阶段依赖性，但机器学习产量模型往往依赖于整个生长季聚合的天气变量。本研究评估了天气变量的季节内时间聚合如何影响基于机器学习的水稻产量建模的预测性能与可解释性。以CYGMA（Crop Yield Growth Model with Assumptions on Climate and Socioeconomics，气候与社会经济假设下的作物产量生长模型）模拟的潜在产量作为合成响应变量，我们最大限度地减少了观测噪声和管理变异性带来的混杂效应，从而能够更清晰地评估季节内天气聚合的作用。将每个生长季划分为多个等长时段后，对水稻生长期内的逐日天气变量进行平均，并采用时间独立验证对多种机器学习算法进行训练和评估。提高季节内时间分辨率可提升模型性能，直至达到中等水平，此后增益递减。利用SHAP（SHapley Additive exPlanations，沙普利加性解释）和响应诊断进行的解释一致表明，产量敏感性集中在季节的特定窗口，温度相关效应在季节早期最为显著，湿度相关效应则在后期出现。更精细的时间聚合增强了对窗口特异性非线性和阈值型响应的识别，而这些响应在粗粒度季节平均下被掩盖。总体而言，中等程度的季节内聚合在预测能力与机制可解释性之间提供了实用平衡，有助于更可靠地识别与物候相关的气象风险，以支持气候影响评估和决策支持。","2026-10-13T00:00:00Z",72,{"impact":17,"substance":18,"depth":130,"authority":132,"freshness":196,"relevant":21,"comment":522},"该研究通过CYGMA模拟数据系统评估了生长季内气象变量时间聚合对水稻产量机器学习模型性能与可解释性的影响，方法新颖、结论可靠，对农业气象风险识别与决策支持有参考价值，但属学术论文，公共影响范围有限。",[524],{"name":126,"url":516},[26,27,526,527,528],"可解释AI","农业气象","水稻产量预测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[531],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":14,"version":52,"method":53,"evidence":533},{"candidates":534,"agricultural_evidence":58},[535,540,544,548],{"code":45,"score":536,"supported":58,"title":537,"abstract":538,"tags":539},19,[65],[67,65,218],[27,526],{"code":32,"score":132,"supported":58,"title":541,"abstract":542,"tags":543},[33],[34,33],[],{"code":400,"score":21,"supported":14,"title":545,"abstract":546,"tags":547},[],[],[527],{"code":84,"score":21,"supported":14,"title":549,"abstract":550,"tags":551},[],[],[26],[553,554],"水稻产量 机器学习 气象","SHAP 水稻 产量预测","水稻产量机器学习气象-4993","10.1038\u002Fs41598-026-75646-2",{"doi":556,"openalex_id":558,"authors":559,"venue":126,"cited_by_count":107,"oa_url":576,"card":577,"direction":115,"ingested_from":116},"W7221657053",[560,562,564,566,568,570,573],{"name":561,"orcid":9},"Noh-Hyun Lee",{"name":563,"orcid":9},"Wonjae Jeong",{"name":565,"orcid":9},"Hyeon-Ji Yang",{"name":567,"orcid":9},"Hana Cho",{"name":569,"orcid":9},"Hyeon-Yeong Ju",{"name":571,"orcid":572},"Toshichika Iizumi","https:\u002F\u002Forcid.org\u002F0000-0002-0611-4637",{"name":574,"orcid":575},"Ki‐Tae Kim","https:\u002F\u002Forcid.org\u002F0000-0002-8249-5283","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-75646-2_reference.pdf",{"tldr":578,"method":579,"finding":580,"direction":113,"opportunity":581},"评估生长季内天气变量时间聚合粒度对水稻产量机器学习模型预测与可解释性的影响。","用CYGMA模拟潜在产量作响应变量，按等长时段聚合逐日天气，训练多种机器学习模型","适度提高季内时间分辨率可提升性能，产量敏感性集中在特定窗口，温度效应偏早、湿度效应偏晚。","可探索不同作物与气候区下最优聚合粒度，并将窗口特异性气象风险嵌入气候影响评估与决策支持系统。","2026-10-15T23:30:10.483881Z"]