[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4479":3,"related-4479":96},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"embedding_ready":14,"score":15,"sources":16,"tags":18,"primary_topic":23,"secondary_topics":29,"search_phrases":30,"slug":33,"view_count":34,"doi":35,"paper":36,"created_at":70,"content":8,"score_detail":71,"classification":78},4479,"Improving field phenotyping accuracy in rice: a comparative study of smartphone image analysis and manual measurement","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10439-1","Improving field phenotyping accuracy in rice: a comparative study of smartphone image analysis and manual measurement。Precision Agriculture",null,"Precision Agriculture","2026-10-08T00:00:00Z","论文",10,false,true,68,[17],{"name":9,"url":6},[19,20,21,22],"智慧农业","作物育种","水稻表型","手机图像分析",{"code":24,"slug":25,"name":26,"path":27,"parent_code":28},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[],[31,32],"水稻 表型 手机图像","手机图像分析 作物育种 智慧农业 水稻表型","水稻表型手机图像-4479",1,"10.1007\u002Fs11119-026-10439-1",{"doi":35,"openalex_id":37,"authors":38,"venue":9,"cited_by_count":68,"oa_url":6,"card":8,"direction":8,"ingested_from":69},"W7220964981",[39,42,45,47,49,51,53,55,57,60,63,65],{"name":40,"orcid":41},"Jae‐Ryoung Park","https:\u002F\u002Forcid.org\u002F0000-0003-2207-5313",{"name":43,"orcid":44},"Jeonghwan Seo","https:\u002F\u002Forcid.org\u002F0000-0003-1740-5058",{"name":46,"orcid":8},"Jung-Pil Suh",{"name":48,"orcid":8},"Mina Jin",{"name":50,"orcid":8},"Su-Kyung Ha",{"name":52,"orcid":8},"Gileung Lee",{"name":54,"orcid":8},"Kyeong Min Kang",{"name":56,"orcid":8},"Seung Young Lee",{"name":58,"orcid":59},"Hyun-Su Park","https:\u002F\u002Forcid.org\u002F0000-0001-6815-3001",{"name":61,"orcid":62},"Woon-Ha Hwang","https:\u002F\u002Forcid.org\u002F0000-0002-4493-2446",{"name":64,"orcid":8},"Wan-Gyu Sang",{"name":66,"orcid":67},"Hyun‐Sook Lee","https:\u002F\u002Forcid.org\u002F0000-0002-1959-6209",0,"openalex","2026-10-09T23:30:02.130650Z",{"impact":72,"substance":73,"depth":74,"authority":75,"freshness":76,"relevant":34,"comment":77},12,18,16,14,8,"核心期刊论文，比较手机图像分析与人工测量在水稻田间表型上的精度，方法有参考价值但影响面偏细分领域。",{"status":79,"locked":13,"version":80,"method":81,"evidence":82},"classified","2026-10-22.2","rules",{"candidates":83,"agricultural_evidence":14},[84,89],{"code":24,"score":85,"supported":14,"title":86,"abstract":87,"tags":88},11,[25],[25],[],{"code":90,"score":91,"supported":14,"title":92,"abstract":93,"tags":95},"I1",4,[],[94],"precision agriculture",[19],{"total":97,"page":34,"page_size":97,"items":98},6,[99,194,234,275,310,358],{"id":100,"title":101,"url":102,"summary":103,"summary_zh":104,"source_name":105,"source_url":102,"published_at":106,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"embedding_ready":14,"score":107,"sources":108,"tags":110,"primary_topic":114,"secondary_topics":115,"search_phrases":128,"slug":131,"view_count":68,"doi":132,"paper":133,"created_at":193},5614,"Replacing blanket agronomic recommendations with agroecology-specific decision support: a simulation-based framework for irrigated dry-season rice production in coastal deltas","https:\u002F\u002Fdoi.org\u002F10.1071\u002Fcp26175","Context Irrigated dry-season (Boro) rice (Oryza sativa L.) is a major contributor to food security in Bangladesh and inland West Bengal, but its cultivation is limited in the coastal Sundarbans due to high salinity, tidal influences, and scarcity of fresh irrigation water. Current policy frameworks promoting uniform transplanting windows and cultivar choices disregard the marked spatial heterogeneity of abiotic stress regimes in these deltaic agroecosystems. Aims This study aimed to characterize genotype × environment × management (G × E × M) interactions and identify location-specific sowing date–cultivar combinations to improve Boro rice productivity, irrigation water productivity, and yield stability under coastal saline conditions. Methods The Agricultural Production Systems Simulator (APSIM-Oryza) was calibrated and validated using multi-location field experimental data from Bangladesh and India collected during 2016–2018. Model performance was evaluated for phenology, above-ground biomass, and grain yield, with root mean square errors (RMSEs) of 6.7 days, 996 kg ha−1, and 1574 kg ha−1, respectively. Subsequently, 35 years of climate-driven simulations (1984–2018) were conducted to assess the effects of sowing dates and cultivar choices on crop performance, stress exposure, irrigation requirements, and irrigation water productivity across contrasting environments. Results Thirty-five years of climate-driven simulations (1984–2018) showed strong spatiotemporal variability in stress exposure. Early sowing (October) predisposed crops to cold-induced spikelet sterility, whereas late sowing (mid-December onwards) intensified exposure to terminal salinity and heat stress, elevating irrigation demand while reducing water productivity. Mid-November sowing consistently minimized risk trade-offs, achieving the highest probabilities of exceeding 5.0 Mg ha−1 with superior irrigation water productivity (9–11 kg grain ha−1 mm−1). Salt-tolerant cultivars (e.g. BRRI Dhan67, WGL20471) maintained yield across variable stress environments, whereas susceptible genotypes (e.g. BRRI Dhan28, IET4786) exhibited high interannual yield variability and frequent failure. Conclusions Sowing date and cultivar selection jointly determine Boro rice productivity and yield stability in coastal saline environments. Mid-November sowing, combined with salt-tolerant cultivars, emerged as a promising strategy for reducing climatic and salinity-related production risks. However, the magnitude of these benefits depends on local stress conditions and hydrological characteristics. Implications The findings support a shift from blanket regional recommendations towards dynamic, simulation-based decision-support systems that integrate sowing windows, stress-resilient cultivars, and location-specific hydrological and salinity conditions. Integrating these systems into agricultural extension services and policy frameworks could improve climate resilience, irrigation water productivity, and sustainable Boro rice production across the coastal saline zones of South Asia.","背景 灌溉旱季（Boro）水稻（Oryza sativa L.）是孟加拉国和西孟加拉邦内陆地区粮食安全的主要贡献者，但由于高盐度、潮汐影响和淡水灌溉水源稀缺，其种植在沿海孙德尔本斯地区受到限制。当前促进统一移栽窗口和品种选择的政策框架忽视了这些三角洲农业生态系统中非生物胁迫状况的显著空间异质性。目的 本研究旨在表征基因型×环境×管理（G×E×M）互作，并确定地点特定的播期—品种组合，以提高沿海盐渍条件下Boro水稻的生产力、灌溉水生产力和产量稳定性。方法 利用2016—2018年期间从孟加拉国和印度收集的多地点田间试验数据，对农业生产系统模拟器（APSIM-Oryza）进行了校准和验证。模型性能针对物候、地上生物量和籽粒产量进行了评估，均方根误差（RMSE）分别为6.7天、996 kg ha−1和1574 kg ha−1。随后，进行了35年气候驱动模拟（1984—2018），以评估播期和品种选择对 contrasting 环境下作物表现、胁迫暴露、灌溉需求和灌溉水生产力的影响。结果 35年气候驱动模拟（1984—2018）显示胁迫暴露存在强烈的时空变异性。早播（10月）使作物易受低温诱导的小穗不育影响，而晚播（12月中旬以后）则加剧了终期盐度和热胁迫的暴露，提高了灌溉需求同时降低了水分生产力。11月中旬播种持续最小化了风险权衡，实现了超过5.0 Mg ha−1的最高概率以及优越的灌溉水生产力（9—11 kg grain ha−1 mm−1）。耐盐品种（如BRRI Dhan67、WGL20471）在变化的胁迫环境中保持了产量，而敏感基因型（如BRRI Dhan28、IET4786）则表现出高的年际产量变异性和频繁的失败。结论 播期和品种选择共同决定了沿海盐渍环境中Boro水稻的生产力和产量稳定性。11月中旬播种结合耐盐品种，成为降低气候和盐度相关生产风险的有前景的策略。然而，其幅度","Australian Journal of Agricultural Research","2026-10-22T00:00:00Z",82,[109],{"name":105,"url":102},[19,26,111,112,113],"盐碱地","决策支持","灌溉管理",{"code":24,"slug":25,"name":26,"path":27,"parent_code":28},[116,122],{"code":117,"slug":118,"name":119,"path":120,"parent_code":121},"H6","biodiversity","生态保护与生物多样性","\u002Ftopic\u002Fenvironment\u002Fbiodiversity","H",{"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",[129,130],"APSIM-Oryza 水稻 模拟","沿海盐渍区 Boro 水稻 播期","APSIM-Oryza水稻模拟-5614","10.1071\u002Fcp26175",{"doi":132,"openalex_id":134,"authors":135,"venue":105,"cited_by_count":68,"oa_url":8,"card":187,"direction":191,"ingested_from":69},"W7223185956",[136,139,142,145,147,150,153,156,159,162,165,168,171,173,176,179,182,184],{"name":137,"orcid":138},"Sukamal Sarkar","https:\u002F\u002Forcid.org\u002F0000-0002-1438-1778",{"name":140,"orcid":141},"Donald S. Gaydon","https:\u002F\u002Forcid.org\u002F0000-0002-0078-4154",{"name":143,"orcid":144},"Mohammed Mainuddin","https:\u002F\u002Forcid.org\u002F0000-0002-6057-5688",{"name":146,"orcid":8},"Md. Maniruzzaman",{"name":148,"orcid":149},"M. B. Hossain","https:\u002F\u002Forcid.org\u002F0009-0003-5394-1381",{"name":151,"orcid":152},"Buddheswar Maji","https:\u002F\u002Forcid.org\u002F0000-0002-9924-6160",{"name":154,"orcid":155},"Sukanta Kumar Sarangi","https:\u002F\u002Forcid.org\u002F0000-0002-0356-7665",{"name":157,"orcid":158},"Anannya Dhar","https:\u002F\u002Forcid.org\u002F0009-0001-4160-2193",{"name":160,"orcid":161},"Saikat Dey","https:\u002F\u002Forcid.org\u002F0009-0000-7304-6219",{"name":163,"orcid":164},"Richard William Bell","https:\u002F\u002Forcid.org\u002F0000-0002-7756-3755",{"name":166,"orcid":167},"Mst Shetara Yesmin","https:\u002F\u002Forcid.org\u002F0009-0003-9249-8626",{"name":169,"orcid":170},"Palash Kumar Kundu","https:\u002F\u002Forcid.org\u002F0000-0001-6076-9493",{"name":172,"orcid":8},"Mark R. Glover",{"name":174,"orcid":175},"D. Burman","https:\u002F\u002Forcid.org\u002F0000-0002-2322-0044",{"name":177,"orcid":178},"Jatish Chandra Biswas","https:\u002F\u002Forcid.org\u002F0000-0002-4972-6676",{"name":180,"orcid":181},"Uttam Kumar Mandal","https:\u002F\u002Forcid.org\u002F0000-0002-7259-5037",{"name":183,"orcid":8},"K. K. Mahanta",{"name":185,"orcid":186},"E.G. Barrett-Lennard","https:\u002F\u002Forcid.org\u002F0000-0001-9945-1044",{"tldr":188,"method":189,"finding":190,"direction":191,"opportunity":192},"用APSIM模拟35年气候，为沿海盐渍稻区定制播期与品种决策。","APSIM-Oryza模型校准验证，1984–2018年多地点气候模拟。","11月中旬播种配耐盐品种可兼顾高产、节水与稳产。","农业人工智能与决策模型","可构建面向异质胁迫区的分区精准决策支持系统，替代统一农艺推荐。","2026-10-22T23:31:14.689445Z",{"id":195,"title":196,"url":197,"summary":198,"summary_zh":199,"source_name":200,"source_url":197,"published_at":106,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"embedding_ready":14,"score":201,"sources":202,"tags":204,"primary_topic":208,"secondary_topics":209,"search_phrases":211,"slug":214,"view_count":68,"doi":215,"paper":216,"created_at":233},5613,"ExplainableCropAI: Interpretable Deep Learning for Agricultural Disease Diagnosis and Decision Support","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44196-026-01654-9","Deep learning models classify crop diseases accurately, but their black-box nature limits practical use: farmers and extension officers need to understand a diagnosis before acting on it. We present ExplainableCropAI, a decision-support system for paddy disease diagnosis that attaches three complementary explanation channels to a ConvNeXt V2-Tiny classifier: Grad-CAM++ visual attribution, Monte Carlo Dropout uncertainty with temperature-scaled calibration, and natural-language explanations generated by Phi-3-mini under a grounding constraint that restricts the model to typed fields drawn from the prediction, the attribution map and a curated agronomic knowledge base. None of the components is new; the contribution lies in their integration into a single pipeline and in measuring the properties that integration is meant to deliver. On a leakage-controlled, held-out test split of 1,540 images from the Paddy Doctor dataset (10,407 images, nine diseases and healthy), the classifier reaches 97.5% accuracy (95% CI 96.7–98.3) and macro-F1 of 96.9%. Six attribution methods are compared under identical settings with model-based faithfulness metrics; the calibrated confidence separates correct from incorrect predictions with an AUROC of 0.93, and referring the 10% least-confident cases to an expert removes 74% of all errors. Under eight image corruptions at five severities the model retains 89.7% mean accuracy against 72–78% for six baselines trained identically, and its referral rate rises with the degree of degradation. Zero-shot evaluation on two independently collected rice-disease datasets from Bangladesh and India shows that accuracy roughly halves under cross-dataset shift and that confidence no longer identifies errors, while fifty local images per class recover accuracy above 90%. The code, split files, trained weights and evaluation scripts are released.","深度学习模型能够准确分类作物病害，但其黑箱性质限制了实际应用：农民和推广人员需要在理解诊断结果后才能采取行动。我们提出ExplainableCropAI，一个用于水稻病害诊断的决策支持系统，它在ConvNeXt V2-Tiny分类器上附加了三条互补的解释通道：Grad-CAM++视觉归因、经温度缩放校准的蒙特卡洛Dropout不确定性，以及由Phi-3-mini在接地约束下生成的自然语言解释——该约束将模型限制为从预测结果、归因图和精选农学知识库中提取的类型化字段。各组件均非新创；贡献在于将其集成到单一流水线中，并测量该集成旨在提供的特性。在来自Paddy Doctor数据集（10,407张图像，九种病害及健康）的1,540张图像上，经泄漏控制的留出测试划分显示，分类器达到97.5%的准确率（95% CI 96.7–98.3）和96.9%的宏F1值。在相同设置下，使用基于模型的忠实性指标比较了六种归因方法；校准后的置信度以0.93的AUROC区分正确与错误预测，将置信度最低的10%案例转交专家可消除全部错误的74%。在五种严重程度的八种图像损坏下，模型保持89.7%的平均准确率，而相同训练的六个基线为72–78%，且其转交率随退化程度上升。在来自孟加拉国和印度的两个独立采集的水稻病害数据集上进行零样本评估显示，跨数据集偏移下准确率大约减半，置信度不再能识别错误，而每类五十张本地图像可将准确率恢复至90%以上。代码、划分文件、训练权重和评估脚本均已发布。","International Journal of Computational Intelligence Systems",84,[203],{"name":200,"url":197},[19,205,26,206,207],"农业人工智能","可解释AI","作物病害诊断",{"code":123,"slug":124,"name":125,"path":126,"parent_code":127},[210],{"code":24,"slug":25,"name":26,"path":27,"parent_code":28},[212,213],"ExplainableCropAI 水稻病害","Paddy Doctor 数据集","ExplainableCropAI水稻病害-5613","10.1007\u002Fs44196-026-01654-9",{"doi":215,"openalex_id":217,"authors":218,"venue":200,"cited_by_count":68,"oa_url":197,"card":228,"direction":191,"ingested_from":69},"W7166554515",[219,221,222,225,227],{"name":220,"orcid":8},"Santosh Kumar",{"name":220,"orcid":8},{"name":223,"orcid":224},"Durgesh Nandan","https:\u002F\u002Forcid.org\u002F0000-0002-9762-3559",{"name":226,"orcid":8},"Sumit Kumar",{"name":226,"orcid":8},{"tldr":229,"method":230,"finding":231,"direction":191,"opportunity":232},"构建可解释水稻病害诊断系统，融合归因、不确定性与自然语言解释。","ConvNeXt V2-Tiny分类器，Grad-CAM++、MC Dropou","测试准确率97.5%，低置信转诊消除74%错误，跨数据集精度减半。","跨数据集置信失效，少量本地样本微调可恢复精度，值得研究田间自适应与主动学习。","2026-10-22T23:31:14.582261Z",{"id":235,"title":236,"url":237,"summary":238,"summary_zh":239,"source_name":240,"source_url":237,"published_at":241,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"embedding_ready":14,"score":242,"sources":243,"tags":245,"primary_topic":248,"secondary_topics":249,"search_phrases":255,"slug":258,"view_count":68,"doi":259,"paper":260,"created_at":274},5394,"Rice Yield Prediction Using Multi-Modal Regression Learning with Optimized Ensemble Models","https:\u002F\u002Fdoi.org\u002F10.3390\u002Feng7100572","Accurate rice yield estimation is important for agricultural planning, resource allocation, and food security assessment, but yield depends on interacting climatic, vegetation, remote sensing, seasonal, and crop growth factors. This study presents a Multi-Modal Regression Learning (MMRL) framework for continuous rice yield prediction. The framework integrates rainfall, temperature, humidity, normalized difference vegetation index (NDVI), VH synthetic aperture radar (SAR) backscatter, season, rice cultivation area, and leaf wetness information. Predictor relevance is assessed before model development, and multiple regression learners are trained using a common feature set. Their validation predictions are combined through non-negative ensemble weights constrained to sum to one, thereby producing a single optimized prediction. Experiments were performed on a modeled spatio-temporal agricultural dataset representing Karnataka conditions, with 10,000 observations used for the regression comparison. The proposed MMRL ensemble achieved an R2 of 0.9515, an RMSE of 0.6334 tons\u002Fha, an MAE of 0.5075 tons\u002Fha, and an MAPE of 9.81%, outperforming the evaluated individual regression models. Diagnostic plots further indicated close agreement between predicted and reference yields across most of the evaluated range. The MMRL workflow was also evaluated on a separately reconstructed synthetic dataset generated with fully specified distributions, sampling rules, noise terms, a yield generation equation, and fixed random seeds.","准确的水稻产量估算对农业规划、资源配置和粮食安全评估具有重要意义，但产量取决于气候、植被、遥感、季节和作物生长等多种因素的相互作用。本研究提出了一种用于连续水稻产量预测的多模态回归学习（Multi-Modal Regression Learning，MMRL）框架。该框架整合了降雨量、温度、湿度、归一化植被指数（NDVI）、VH合成孔径雷达（SAR）后向散射、季节、水稻种植面积和叶片湿润信息。在模型开发之前评估预测变量的相关性，并使用共同特征集训练多个回归学习器。其验证预测结果通过约束为和等于1的非负集成权重进行组合，从而生成单一优化预测。实验在代表卡纳塔克邦条件的建模时空农业数据集上进行，使用10,000个观测值进行回归比较。所提出的MMRL集成模型实现了R²为0.9515、RMSE为0.6334吨\u002F公顷、MAE为0.5075吨\u002F公顷、MAPE为9.81%，优于所评估的各个回归模型。诊断图进一步表明，在大多数评估范围内，预测产量与参考产量高度一致。MMRL工作流程还在一个单独重建的合成数据集上进行了评估，该数据集使用完全指定的分布、采样规则、噪声项、产量生成方程和固定随机种子生成。","Eng—Advances in Engineering","2026-10-18T00:00:00Z",78,[244],{"name":240,"url":237},[19,205,26,246,247],"产量预测","遥感",{"code":24,"slug":25,"name":26,"path":27,"parent_code":28},[250],{"code":251,"slug":252,"name":253,"path":254,"parent_code":127},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[256,257],"水稻 产量预测 多模态","NDVI SAR 水稻估产","水稻产量预测多模态-5394","10.3390\u002Feng7100572",{"doi":259,"openalex_id":261,"authors":262,"venue":240,"cited_by_count":68,"oa_url":237,"card":268,"direction":272,"ingested_from":69},"W7222942064",[263,265],{"name":264,"orcid":8},"Nayana Venkatesh",{"name":266,"orcid":267},"Evangelin Geetha Devaraj","https:\u002F\u002Forcid.org\u002F0000-0002-7253-4314",{"tldr":269,"method":270,"finding":271,"direction":272,"opportunity":273},"构建多模态回归学习框架，融合气候、遥感与作物因子，实现水稻产量连续预测。","融合降雨、温度、NDVI、SAR后向散射等多源特征，用非负权重集成多个回归器。","集成模型R²达0.9515，RMSE为0.6334吨\u002F公顷，优于各单一回归模型。","农业遥感与作物表型","可引入真实田间多时相遥感与深度时序模型，验证合成数据结论在真实产区的泛化性。","2026-10-20T23:30:19.292634Z",{"id":276,"title":277,"url":278,"summary":279,"summary_zh":280,"source_name":281,"source_url":278,"published_at":282,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"embedding_ready":14,"score":283,"sources":284,"tags":286,"primary_topic":289,"secondary_topics":290,"search_phrases":291,"slug":294,"view_count":68,"doi":295,"paper":296,"created_at":309},5388,"A digital twin framework for proactive disease management in paddy cultivation using spatio-temporal graph neural networks","https:\u002F\u002Fdoi.org\u002F10.1080\u002F20421338.2026.2707951","Rice production is highly affected by disease outbreaks, particularly in Asian agricultural systems, resulting in major yield losses and excessive pesticide usage. This study proposes a digital twin framework for paddy cultivation that integrates predictive disease modelling with real-time crop phenology simulation to support proactive disease management. The framework utilizes historical phenological records, IoT-based microclimatic sensor data, irrigation information, and field management records collected from 150 hectares of paddy fields in Maharashtra, India. A Spatio-Temporal Graph Neural Network (ST-GNN), integrating Graph Convolutional Networks (GCNs) and Long Short-Term Memory (LSTM) networks, is employed to model spatial disease propagation and temporal crop dynamics for predicting blast, brown planthopper, and bacterial leaf blight outbreaks up to 14 days in advance. Experimental results demonstrate 87.3% prediction accuracy, 23.7% reduction in pesticide usage, and 31.2% reduction in crop losses, while outperforming traditional Random Forest and SVM models in F1-score performance. The proposed framework enables real-time decision support for sustainable precision agriculture and optimized resource management.","水稻生产极易受到病害暴发的影响，在亚洲农业体系中尤为突出，造成重大产量损失和农药过度使用。本研究提出一个水稻栽培数字孪生（digital twin）框架，将预测性病害建模与实时作物物候模拟相结合，以支持主动式病害管理。该框架利用从印度马哈拉施特拉邦150公顷稻田采集的历史物候记录、基于物联网的微气候传感器数据、灌溉信息及田间管理记录。采用融合图卷积网络（Graph Convolutional Networks, GCN）与长短期记忆网络（Long Short-Term Memory, LSTM）的时空图神经网络（Spatio-Temporal Graph Neural Network, ST-GNN），对病害空间传播与作物时序动态进行建模，以提前14天预测稻瘟病、褐飞虱和白叶枯病的暴发。实验结果表明，预测准确率达87.3%，农药使用量减少23.7%，作物损失减少31.2%，且在F1分数上优于传统随机森林（Random Forest）和支持向量机（SVM）模型。所提框架可为可持续精准农业和优化资源管理提供实时决策支持。","African Journal of Science Technology Innovation and Development","2026-10-19T00:00:00Z",80,[285],{"name":281,"url":278},[19,205,26,287,288],"数字孪生","病虫害预警",{"code":24,"slug":25,"name":26,"path":27,"parent_code":28},[],[292,293],"水稻 病害预测 数字孪生","ST-GNN 水稻 病虫害","水稻病害预测数字孪生-5388","10.1080\u002F20421338.2026.2707951",{"doi":295,"openalex_id":297,"authors":298,"venue":281,"cited_by_count":68,"oa_url":8,"card":303,"direction":308,"ingested_from":69},"W7222969403",[299,301],{"name":300,"orcid":8},"Jyoti Rani",{"name":302,"orcid":8},"Dr.  Narendra S.  Jadhav",{"tldr":304,"method":305,"finding":306,"direction":191,"opportunity":307},"构建水稻数字孪生框架，用时空图神经网络提前14天预测病害。","融合物联网微气候、物候与管理数据，用GCN+LSTM的ST-GNN建模。","预测准确率87.3%，农药减23.7%、损失减31.2%，优于RF与SVM。","可探索多尺度时空图建模与数字孪生闭环决策，并迁移至其他作物与区域。","智慧农业 \u002F 农业物联网","2026-10-20T23:30:12.636299Z",{"id":311,"title":312,"url":313,"summary":314,"summary_zh":315,"source_name":316,"source_url":313,"published_at":317,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"embedding_ready":14,"score":318,"sources":319,"tags":321,"primary_topic":324,"secondary_topics":325,"search_phrases":327,"slug":330,"view_count":34,"doi":331,"paper":332,"created_at":357},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。","Scientific Reports","2026-10-13T00:00:00Z",72,[320],{"name":316,"url":313},[19,205,206,322,323],"农业气象","水稻产量预测",{"code":24,"slug":25,"name":26,"path":27,"parent_code":28},[326],{"code":123,"slug":124,"name":125,"path":126,"parent_code":127},[328,329],"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":331,"openalex_id":333,"authors":334,"venue":316,"cited_by_count":68,"oa_url":351,"card":352,"direction":308,"ingested_from":69},"W7221657053",[335,337,339,341,343,345,348],{"name":336,"orcid":8},"Noh-Hyun Lee",{"name":338,"orcid":8},"Wonjae Jeong",{"name":340,"orcid":8},"Hyeon-Ji Yang",{"name":342,"orcid":8},"Hana Cho",{"name":344,"orcid":8},"Hyeon-Yeong Ju",{"name":346,"orcid":347},"Toshichika Iizumi","https:\u002F\u002Forcid.org\u002F0000-0002-0611-4637",{"name":349,"orcid":350},"Ki‐Tae Kim","https:\u002F\u002Forcid.org\u002F0000-0002-8249-5283","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-75646-2_reference.pdf",{"tldr":353,"method":354,"finding":355,"direction":191,"opportunity":356},"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",{"id":359,"title":360,"url":361,"summary":362,"summary_zh":8,"source_name":363,"source_url":8,"published_at":317,"category":364,"cover_url":8,"hotness":12,"is_selected":13,"embedding_ready":14,"score":365,"sources":366,"tags":368,"primary_topic":372,"secondary_topics":373,"search_phrases":374,"slug":377,"view_count":68,"doi":8,"paper":8,"created_at":378},4950,"'袁稻思源'杂交水稻智能育种平台发布 业界首个智能育种平台落户湖南","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7696096616441774642\u002F","10月11日,'袁稻思源'杂交水稻智能育种平台发布会在岳麓山实验室举行,由中国科学院自动化研究所联合湖南省农业科学院、北京中科闻歌科技股份有限公司共同研发。平台集成'问答助手''育种资源库''知识图谱''文献库'四大功能模块,收纳35570个水稻品种、689项性状、28648条基因数据和624757篇中英文文献。中国科学院院士李家洋、钱前、谢道昕,中国工程院院士柏连阳等共同见证。","环球网 今日头条 2026 年 10 月 13 日","报道",88,[367],{"name":363,"url":361},[19,205,369,370,371],"种业振兴","智能育种","杂交水稻",{"code":24,"slug":25,"name":26,"path":27,"parent_code":28},[],[375,376],"袁稻思源 杂交水稻 智能育种平台","湖南省农科院 中科闻歌 智能育种","袁稻思源杂交水稻智能育种平台-4950","2026-10-15T00:06:02.857637Z"]