[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1439":3,"related-1439":95},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":70,"slug":73,"view_count":74,"doi":75,"paper":76,"created_at":94},1439,"A dictionary-based method for crop yield prediction using satellite remote sensing data","https:\u002F\u002Fdoi.org\u002F10.1080\u002F2150704x.2026.2724307","HYDRA (HYbrid Dictionary–Rocket Architecture) has shown competitive performance across several benchmark datasets in prior Time-Series Classification (TSC) studies. It is a dictionary-based method that uses static convolutional kernels as dictionary patterns. HYDRA incorporates these non-learnable kernels in its method, which form the basis of ROCKET (RandOm Convolutional KErnel Transform) method designed for TSC tasks. This work presents a machine-learning-based framework built on HYDRA to perform regression on remotely sensed satellite data for crop yield prediction. It integrates HYDRA and crop phenological features through concatenation to generate a composite feature vector for XGBoost (eXtreme Gradient Boosting), enabling within-field corn yield prediction using time-series Sentinel-2 imagery. On the 2017 and 2018 datasets derived from a 22-ha corn field in Italy, our method achieved R2 values of 0.553 and 0.696, RMSE values of 1.101 t\u002Fha and 1.013 t\u002Fha, and MAE values of 0.796 t\u002Fha and 0.708 t\u002Fha, offering a modest improvement in yield prediction performance over frameworks based solely on HYDRA and phenological features. The application of HYDRA-based features in remote-sensing-based agricultural applications shows promising results for regression tasks.","HYDRA（HYbrid Dictionary–Rocket Architecture，混合字典-火箭架构）在先前的时间序列分类（TSC）研究中，已在多个基准数据集上展现出具有竞争力的性能。它是一种基于字典的方法，使用静态卷积核作为字典模式。HYDRA在其方法中融入了这些不可学习的核，这些核构成了专为TSC任务设计的ROCKET（RandOm Convolutional KErnel Transform，随机卷积核变换）方法的基础。本研究提出了一种基于HYDRA的机器学习框架，用于对遥感卫星数据进行回归分析，以实现作物产量预测。该框架通过拼接方式整合HYDRA特征与作物物候特征，生成复合特征向量供XGBoost（eXtreme Gradient Boosting，极端梯度提升）使用，从而利用时间序列Sentinel-2影像实现田块尺度内的玉米产量预测。在源自意大利一块22公顷玉米田的2017年和2018年数据集上，我们的方法分别取得了R²值为0.553和0.696、RMSE值为1.101吨\u002F公顷和1.013吨\u002F公顷、MAE值为0.796吨\u002F公顷和0.708吨\u002F公顷的结果，相较于仅基于HYDRA和物候特征的框架，在产量预测性能上实现了适度提升。基于HYDRA的特征在遥感农业应用中的使用，为回归任务展现了有前景的结果。",null,"Remote Sensing Letters","2026-09-01T00:00:00Z","论文",10,false,67,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},12,20,16,7,1,"基于HYDRA的遥感作物产量预测方法在意大利玉米田取得较好精度，为农业遥感应用提供新思路。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","产量预测","机器学习","遥感",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51,59,65],{"code":32,"score":45,"supported":46,"title":47,"abstract":49,"tags":50},13,true,[48],"remote sensing",[30],[28,30],{"code":52,"score":53,"supported":46,"title":54,"abstract":55,"tags":58},"A3",6,[],[56,57],"玉米","corn",[],{"code":60,"score":61,"supported":46,"title":62,"abstract":63,"tags":64},"I2",5,[],[29],[27,29],{"code":66,"score":21,"supported":14,"title":67,"abstract":68,"tags":69},"I1",[],[],[26],[71,72],"农业人工智能 产量预测 智慧农业 机器学习","农业人工智能 产量预测","农业人工智能产量预测智慧农业机器学习-1439",2,"10.1080\u002F2150704x.2026.2724307",{"doi":75,"openalex_id":77,"authors":78,"venue":10,"cited_by_count":86,"oa_url":9,"card":87,"direction":91,"ingested_from":93},"W7204870154",[79,81,84],{"name":80,"orcid":9},"M.R. Najam",{"name":82,"orcid":83},"Hasnat Khurshid","https:\u002F\u002Forcid.org\u002F0000-0001-8722-5904",{"name":85,"orcid":9},"Faisal Akram",0,{"tldr":88,"method":89,"finding":90,"direction":91,"opportunity":92},"提出基于HYDRA字典方法的机器学习框架，结合物候特征预测玉米产量。","HYDRA特征与物候特征拼接，输入XGBoost回归，使用Sentinel-2时","在意大利玉米田上R²达0.696，优于单独使用HYDRA或物候特征。","农业遥感与作物表型","HYDRA在遥感回归中潜力大，可探索多作物、多区域及与其他时序特征融合。","openalex","2026-09-02T23:30:51.479183Z",{"total":53,"page":21,"page_size":53,"items":96},[97,151,228,340,417,484],{"id":98,"title":99,"url":100,"summary":101,"summary_zh":102,"content":9,"source_name":103,"source_url":100,"published_at":104,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":105,"sources":109,"tags":111,"primary_topic":113,"secondary_topics":117,"classification":119,"search_phrases":136,"slug":138,"view_count":86,"doi":139,"paper":140,"created_at":150},5570,"Evaluation of Counterfactual Explanation Robustness for Computer Vision Agriculture Yield-Forecast Models Under Satellite Cloud-Mask Gaps: An Interpretability Reliability Bound for Cooperative Storage Decisions","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545079","Counterfactual explanations are increasingly proposed as the interface through which cooperative storage managers interrogate computer-vision yield-forecast models, yet their reliability under the satellite cloud-mask gaps that routinely afflict agricultural remote sensing has not been bounded. The bound is derived from a Lipschitz continuity assumption on the forecast model and a masking-operator perturbation analysis, and it is expressed as a function of the cloud-mask gap fraction, the model's local Lipschitz constant, and the counterfactual search tolerance. A replication-based evaluation of the before-after-control-impact estimator was conducted to characterise the operating characteristics of the design under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64), with power estimated across 2000 design cells. The reliability bound degrades non-linearly as the gap fraction rises, and that the intraclass correlation of 0.64 implies that cooperative-level aggregation of counterfactual recourse is necessary to preserve explanation stability. The contribution is a decision-relevant reliability bound that storage cooperatives can use to set a maximum tolerable cloud-mask gap before counterfactual recourse is suspended.","反事实解释日益被提出作为合作式仓储管理者质询计算机视觉产量预测模型的接口，然而在农业遥感中经常出现的卫星云掩膜缺口条件下，其可靠性尚未得到界定。该界基于预测模型上的Lipschitz连续性假设和掩膜算子扰动分析推导得出，并表示为云掩膜缺口比例、模型局部Lipschitz常数和反事实搜索容差的函数。对前后对照影响估计量进行了基于重复的评估，以表征该设计在给定方差结构（组间标准差=6.0，残差标准差=4.5，组内相关系数=0.64）下的运行特征，并在2000个设计单元中估计了检验效能。可靠性界随缺口比例上升呈非线性退化，且组内相关系数0.64意味着需要对反事实救济进行合作社层面的聚合，以保持解释的稳定性。本文的贡献在于提供了一个与决策相关的可靠性界，仓储合作社可据此设定在暂停反事实救济之前可容忍的最大云掩膜缺口。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",{"impact":106,"substance":18,"depth":107,"authority":17,"freshness":13,"relevant":21,"comment":108},8,17,"该论文提出反事实解释在卫星云掩膜缺口下的可靠性边界，方法新颖且结论可用于合作社决策，但属细分方法学研究，产业影响有限。",[110],{"name":103,"url":100},[26,27,28,30,112],"可解释性",{"code":60,"slug":114,"name":115,"path":116,"parent_code":36},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[118],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":120},{"candidates":121,"agricultural_evidence":46},[122,128,132],{"code":60,"score":17,"supported":46,"title":123,"abstract":125,"tags":127},[124],"computer vision",[126],"计算机视觉",[27],{"code":32,"score":106,"supported":46,"title":129,"abstract":130,"tags":131},[],[30,48],[28,30],{"code":66,"score":21,"supported":14,"title":133,"abstract":134,"tags":135},[],[],[26],[137,72],"农业人工智能 产量预测 可解释性 智慧农业","农业人工智能产量预测可解释性智慧农业-5570","10.5281\u002Fzenodo.23545079",{"doi":139,"openalex_id":141,"authors":142,"venue":103,"cited_by_count":86,"oa_url":100,"card":145,"direction":91,"ingested_from":93},"W7223114002",[143],{"name":144,"orcid":9},"Kadiatou Barry",{"tldr":146,"method":147,"finding":148,"direction":91,"opportunity":149},"推导卫星云掩膜缺口下反事实解释可靠性的理论界，并评估其对合作社储粮决策的影响。","基于Lipschitz连续性假设与掩膜扰动分析推导可靠性界，并用2000个设计单","缺口比例上升使可靠性非线性下降；ICC=0.64表明需合作社层级聚合以保持解释稳定。","可研究不同作物区云掩膜统计特性下的自适应解释暂停阈值与聚合策略。","2026-10-22T11:20:39.240791Z",{"id":152,"title":153,"url":154,"summary":155,"summary_zh":156,"content":9,"source_name":157,"source_url":154,"published_at":158,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":159,"score_detail":160,"sources":164,"tags":166,"primary_topic":168,"secondary_topics":173,"classification":175,"search_phrases":209,"slug":212,"view_count":86,"doi":213,"paper":214,"created_at":227},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,{"impact":161,"substance":162,"depth":161,"authority":17,"freshness":106,"relevant":21,"comment":163},18,22,"多模态回归集成方法在水稻估产上取得较高精度，方法新颖、数据规模明确，对农业信息化有参考价值，但属学术论文且数据集为模拟数据，产业影响有限。",[165],{"name":157,"url":154},[26,27,167,28,30],"水稻",{"code":169,"slug":170,"name":167,"path":171,"parent_code":172},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[174],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":176},{"candidates":177,"agricultural_evidence":46},[178,183,187,194,201,205],{"code":169,"score":179,"supported":46,"title":180,"abstract":181,"tags":182},15,[170],[167,170],[167],{"code":32,"score":106,"supported":46,"title":184,"abstract":185,"tags":186},[],[30,48],[28,30],{"code":188,"score":53,"supported":46,"title":189,"abstract":190,"tags":193},"A8",[],[191,192],"粮食安全","food security",[],{"code":195,"score":196,"supported":46,"title":197,"abstract":198,"tags":200},"J1",3,[],[199],"农业规划",[],{"code":66,"score":21,"supported":14,"title":202,"abstract":203,"tags":204},[],[],[26],{"code":60,"score":21,"supported":14,"title":206,"abstract":207,"tags":208},[],[],[27],[210,211],"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":213,"openalex_id":215,"authors":216,"venue":157,"cited_by_count":86,"oa_url":154,"card":222,"direction":91,"ingested_from":93},"W7222942064",[217,219],{"name":218,"orcid":9},"Nayana Venkatesh",{"name":220,"orcid":221},"Evangelin Geetha Devaraj","https:\u002F\u002Forcid.org\u002F0000-0002-7253-4314",{"tldr":223,"method":224,"finding":225,"direction":91,"opportunity":226},"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。","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":229,"title":230,"url":231,"summary":232,"summary_zh":233,"content":9,"source_name":234,"source_url":231,"published_at":235,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":236,"score_detail":237,"sources":241,"tags":243,"primary_topic":245,"secondary_topics":246,"classification":254,"search_phrases":311,"slug":314,"view_count":21,"doi":315,"paper":316,"created_at":339},4988,"Hierarchical cross-modal spatiotemporal attention network for crop yield prediction in complex Himalayan agroecosystems","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1933033","Introduction Accurate crop yield prediction is essential for precision agriculture, climate-resilient farm management, and timely decision-making. However, yield prediction remains challenging because of complex, nonlinear, and multi-scale interactions among crop physiology, soil conditions, weather variability, and management practices. Conventional statistical, machine learning, and deep learning approaches, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks, often have limitations in capturing long-range temporal dependencies, hierarchical spatial relationships, heterogeneous data modalities, and predictive uncertainty. Methods This study proposes a Hierarchical Cross-Modal Spatio-Temporal Attention Network (HCM-STAN) for crop yield prediction using a unified attention-based framework for integrating heterogeneous agro-environmental data. The architecture incorporates four principal components: (i) hierarchical spatio-temporal attention operating across tree, block, orchard, and regional scales; (ii) a cross-modal attention module for adaptive fusion of IoT-based soil sensor observations, weather data, plant physiological measurements, and drone-based multispectral imagery; (iii) a soil-aware attention head that explicitly models soil memory, hysteresis effects, and root-zone dynamics; and (iv) an uncertainty-aware prediction module combining Monte Carlo dropout and quantile regression to generate calibrated prediction intervals. The framework was evaluated using a multi-source dataset collected from Himalayan apple orchards during 2023–2026, comprising 1,247 monitored trees across three commercial orchards. Results The proposed HCM-STAN achieved an R 2 of 0.89 for four-week-ahead crop yield prediction, representing a 15.6% improvement over the CNN-LSTM baseline. Furthermore, zero-shot transfer to mango orchards achieved an R 2 of 0.67, corresponding to approximately 75% of the performance obtained under full training. Discussion The results demonstrate that integrating hierarchical spatio-temporal representation learning, cross-modal attention, soil-aware modelling, and uncertainty estimation can effectively address the heterogeneous and dynamic nature of crop yield prediction in complex mountain agroecosystems. The observed zero-shot transfer performance further indicates the potential of HCM-STAN to generalize across crop systems without crop-specific retraining. Overall, the proposed framework provides a promising foundation for reliable, uncertainty-aware yield forecasting and can support precision agriculture, climate-smart farm management, and data-driven decision-making in heterogeneous agroecosystems.","引言 准确的作物产量预测对于精准农业、气候适应性农场管理以及及时决策至关重要。然而，由于作物生理、土壤条件、天气变异和管理措施之间复杂、非线性且多尺度的相互作用，产量预测仍然具有挑战性。传统的统计方法、机器学习以及深度学习方法，包括卷积神经网络（CNN）、循环神经网络（RNN）和长短期记忆（LSTM）网络，在捕捉长程时间依赖关系、层次化空间关系、异构数据模态以及预测不确定性方面往往存在局限。方法 本研究提出了一种用于作物产量预测的层次化跨模态时空注意力网络（HCM-STAN），采用统一的基于注意力的框架来整合异构农业环境数据。该架构包含四个主要组成部分：（i）在树、地块、果园和区域尺度上运行的层次化时空注意力；（ii）用于自适应融合基于物联网的土壤传感器观测、天气数据、植物生理测量和无人机多光谱影像的跨模态注意力模块；（iii）显式建模土壤记忆、滞后效应和根区动态的土壤感知注意力头；（iv）结合蒙特卡洛 dropout 和分位数回归以生成校准预测区间的不确定性感知预测模块。该框架使用2023—2026年在喜马拉雅苹果园采集的多源数据集进行了评估，该数据集包含三个商业果园中1，247棵监测树木。结果 所提出的 HCM-STAN 在提前四周作物产量预测中达到了0.89的 R²，相较于 CNN-LSTM 基线提升了15.6%。此外，零样本迁移至芒果园达到了0.67的 R²，相当于完整训练下所获性能的约75%。讨论 结果表明，整合层次化时空表征学习、跨模态注意力、土壤感知建模和不确定性估计，能够有效应对复杂山地农业生态系统中作物产量预测的异构性和动态性。观察到的零样本迁移性能进一步表明，HCM-STAN 具有在无需作物特定再训练的情况下跨作物系统泛化的潜力。总体而言，该 p","Frontiers in Plant Science","2026-10-14T00:00:00Z",81,{"impact":161,"substance":162,"depth":161,"authority":238,"freshness":239,"relevant":21,"comment":240},14,9,"提出分层跨模态时空注意力网络，融合物联网土壤传感、气象、生理与无人机多光谱数据，苹果园四周产量预测R²达0.89并实现芒果园零样本迁移，方法新颖、数据扎实，对智慧农业产量预报有参考价值。",[242],{"name":234,"url":231},[26,27,28,244,30],"多模态融合",{"code":60,"slug":114,"name":115,"path":116,"parent_code":36},[247,253],{"code":248,"slug":249,"name":250,"path":251,"parent_code":252},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":255},{"candidates":256,"agricultural_evidence":46},[257,265,273,279,285,292,299,305],{"code":60,"score":258,"supported":46,"title":259,"abstract":260,"tags":264},11,[],[261,29,262,263],"深度学习","machine learning","deep learning",[27,244],{"code":248,"score":239,"supported":46,"title":266,"abstract":267,"tags":272},[],[268,269,270,271],"苹果","芒果","orchard","apple",[],{"code":32,"score":106,"supported":46,"title":274,"abstract":275,"tags":278},[],[276,277],"无人机多光谱","multispectral",[28,30],{"code":66,"score":20,"supported":46,"title":280,"abstract":281,"tags":284},[],[282,283],"精准农业","precision agriculture",[26],{"code":286,"score":53,"supported":46,"title":287,"abstract":288,"tags":291},"H2",[],[289,290],"土壤","soil",[],{"code":293,"score":53,"supported":46,"title":294,"abstract":295,"tags":298},"I5",[],[296,297],"物联网","IoT",[],{"code":300,"score":196,"supported":46,"title":301,"abstract":302,"tags":304},"H6",[],[303],"农业生态",[],{"code":306,"score":196,"supported":46,"title":307,"abstract":308,"tags":310},"H7",[],[309],"气候适应",[],[312,313],"喜马拉雅 苹果园 产量预测","HCM-STAN 作物产量预测","喜马拉雅苹果园产量预测-4988","10.3389\u002Ffpls.2026.1933033",{"doi":315,"openalex_id":317,"authors":318,"venue":234,"cited_by_count":86,"oa_url":231,"card":332,"direction":338,"ingested_from":93},"W7221795850",[319,322,324,326,328,330],{"name":320,"orcid":321},"Shabir Ahmad Mir","https:\u002F\u002Forcid.org\u002F0000-0003-2311-218X",{"name":323,"orcid":9},"Umarfarooque Momin",{"name":325,"orcid":9},"Sudeep Kumar E",{"name":327,"orcid":9},"Itfaq Ahmad Mir",{"name":329,"orcid":9},"Ab Rouf Khan",{"name":331,"orcid":9},"Ghulam Mohammad Mir",{"tldr":333,"method":334,"finding":335,"direction":336,"opportunity":337},"提出分层跨模态时空注意力网络HCM-STAN，融合多源农业数据预测喜马拉雅山区苹果产量。","用IoT土壤、气象、生理和无人机多光谱数据，结合分层时空与跨模态注意力及不确定性","四周前产量预测R²达0.89，比CNN-LSTM提升15.6%，零样本迁移芒果R²为0.67。","农业人工智能与决策模型","可探索跨作物零样本迁移的领域自适应机制，并验证模型在更多山地农业生态系统的泛化能力。","智慧农业 \u002F 农业物联网","2026-10-15T23:30:08.750140Z",{"id":341,"title":342,"url":343,"summary":344,"summary_zh":345,"content":9,"source_name":103,"source_url":343,"published_at":346,"category":12,"cover_url":9,"hotness":347,"is_selected":14,"score":348,"score_detail":349,"sources":352,"tags":356,"primary_topic":357,"secondary_topics":358,"classification":361,"search_phrases":401,"slug":404,"view_count":86,"doi":405,"paper":406,"created_at":416},4310,"Comprehensive remote sensing and machine learning framework for multi-regional rice yield forecasting under hydroclimatic variability in Sahelian Niger","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23149579","It is crucial to forecast rice yields effectively in order to improve agricultural planning and resilience to climate change in the Sahel, where production is greatly affected by hydroclimatic variability. This research has developed an integrated framework combining remote sensing analysis and machine learning to assess the variability in rice production in Niamey, Dosso, Tillaberi and Diffa, in Niger, over the period 2010–2025. They combined Landsat-derived NDVI and EVI with parameters such as evapotranspiration, precipitation, temperature, humidity, solar radiation, soil moisture, cultivated area, year and season. We processed and harmonised the datasets using Google Earth Engine and Python. We then evaluated four regression algorithms separately, Random Forest (RF), k-nearest neighbours (KNN), support vector regression (SVR) and XGBoost (XGB), for each region. The correlation analysis revealed modest positive links between rice yield and the NDVI (r = 0.27) and the EVI (r = 0.14), while a strong correlation was observed between the NDVI and the EVI (r = 0.80). The vegetation indices showed marked seasonal and interannual variability, generally exhibiting higher values during the wet season. Model performance varied considerably from one region to another","在萨赫勒地区，有效预测水稻产量对于改善农业规划和增强气候变化适应能力至关重要，因为该地区的生产在很大程度上受到水文气候变率的制约。本研究开发了一个集成框架，将遥感分析与机器学习相结合，以评估尼日尔尼亚美、多索、蒂拉贝里和迪法在2010—2025年期间水稻产量的变异性。研究将基于Landsat的归一化植被指数（NDVI）和增强型植被指数（EVI）与蒸散发、降水、温度、湿度、太阳辐射、土壤水分、耕地面积、年份和季节等参数相结合。利用Google Earth Engine和Python对数据集进行了处理和协调。随后，针对每个地区分别评估了四种回归算法：随机森林（RF）、k近邻（KNN）、支持向量回归（SVR）和XGBoost（XGB）。相关分析显示，水稻产量与NDVI（r = 0.27）和EVI（r = 0.14）之间存在弱正相关，而NDVI与EVI之间则观察到强相关（r = 0.80）。植被指数表现出明显的季节性和年际变异性，通常在湿季呈现较高值。模型性能在不同地区之间差异显著。","2026-10-05T00:00:00Z",25,72,{"impact":17,"substance":350,"depth":107,"authority":45,"freshness":239,"relevant":21,"comment":351},21,"基于Landsat植被指数与四种机器学习模型的多区域水稻产量预测研究，方法扎实、数据跨度长，但属区域性学术成果，对国内农业信息化有方法借鉴价值。",[353,354],{"name":103,"url":343},{"name":103,"url":355},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23149580",[26,27,167,28,30],{"code":169,"slug":170,"name":167,"path":171,"parent_code":172},[359,360],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":60,"slug":114,"name":115,"path":116,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":362},{"candidates":363,"agricultural_evidence":46},[364,368,372,376,380,387,393,397],{"code":32,"score":19,"supported":46,"title":365,"abstract":366,"tags":367},[48],[30,48],[28,30],{"code":169,"score":179,"supported":46,"title":369,"abstract":370,"tags":371},[170],[167,170],[167],{"code":60,"score":179,"supported":46,"title":373,"abstract":374,"tags":375},[262],[29,262],[27],{"code":286,"score":53,"supported":46,"title":377,"abstract":378,"tags":379},[],[289,290],[],{"code":381,"score":53,"supported":46,"title":382,"abstract":383,"tags":386},"H3",[],[384,385],"蒸散发","evapotranspiration",[],{"code":306,"score":53,"supported":46,"title":388,"abstract":389,"tags":392},[],[390,391],"气候变化","climate change",[],{"code":195,"score":196,"supported":46,"title":394,"abstract":395,"tags":396},[],[199],[],{"code":66,"score":21,"supported":14,"title":398,"abstract":399,"tags":400},[],[],[26],[402,403],"尼日尔 水稻 遥感 产量预测","Landsat NDVI 水稻 机器学习","尼日尔水稻遥感产量预测-4310","10.5281\u002Fzenodo.23149579",{"doi":405,"openalex_id":407,"authors":408,"venue":103,"cited_by_count":86,"oa_url":343,"card":411,"direction":91,"ingested_from":93},"W7220391503",[409],{"name":410,"orcid":9},"Battula Vijaya Saradhi Halarou Saidou Souley",{"tldr":412,"method":413,"finding":414,"direction":91,"opportunity":415},"结合遥感植被指数与机器学习，预测尼日尔萨赫勒四地区2010–2025年水稻产量。","Landsat NDVI\u002FEVI 加气象土壤变量，GEE与Python处理，比较","NDVI\u002FEVI与产量仅弱相关，模型精度区域差异大，植被指数呈明显季节与年际波动。","可引入时序深度学习与迁移学习，解决跨区域泛化差及植被指数解释力弱的问题。","2026-10-06T23:30:20.077977Z",{"id":418,"title":419,"url":420,"summary":421,"summary_zh":422,"content":9,"source_name":423,"source_url":420,"published_at":424,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":425,"score_detail":426,"sources":428,"tags":430,"primary_topic":432,"secondary_topics":437,"classification":439,"search_phrases":458,"slug":461,"view_count":462,"doi":463,"paper":464,"created_at":483},3328,"Yield ranking of spring wheat breeding lines absent from model training within two contrasting seasons in northern Kazakhstan","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102588","Reliable assessment of yield models in breeding trials requires evaluation on lines that have not contributed observations to model development. We examined this problem in an advanced spring wheat yield nursery in northern Kazakhstan during the 2024 and 2025 seasons. Of 300 plots, 176 from 22 entries formed the model development set, while 124 plots representing 31 breeding lines were completely excluded from training. Eleven variables were retained from 82 candidates using the training data alone; the candidates were derived from UAV multispectral imagery, ERA5-Land reanalysis, LiDAR-derived static plot microtopography, phenology and maturity group. LightGBM yielded a pooled R² of 0.587 for the excluded lines; resampling breeding lines within season gave a mean R² of 0.564 with a 95 % confidence interval from 0.252 to 0.776. The pooled value partly reflected the strong contrast between seasons rather than discrimination among lines within a season. Random division of plots produced an R² of 0.890, indicating that apparent predictive accuracy depended strongly on validation design. Agreement between observed and predicted line rankings was moderate, with Spearman correlations of 0.612 in 2024 and 0.650 in 2025, although uncertainty was wide because only 14 and 17 independent lines were available. Six modelling approaches produced overlapping uncertainty intervals, with no clear evidence of superiority, and the complete combination of data sources was not separable from simpler configurations retaining the multispectral block. A separate analysis in which screening, selection and fitting used 2024 data alone and evaluation used 2025 data showed a severe loss of performance, with R² = −8.81 and a Spearman correlation of −0.082 that was indistinguishable from zero. Poor transfer persisted among entries grown in both seasons, so genotype novelty alone was insufficient to explain the failure. Breeding lines absent from training could therefore be ranked with moderate consistency when both observed seasonal regimes were represented during model development, whereas prediction in an unobserved season remained unreliable. The study covers one nursery complex and two seasons, so transfer across locations and broader conditions remains to be established.","在育种试验中，要对产量模型进行可靠评估，必须在未参与模型开发的品系上进行评价。我们在2024年和2025年生长季于哈萨克斯坦北部的一个高级春小麦产量圃中考察了这一问题。在300个小区中，来自22个品系的176个小区构成模型开发集，而代表31个育种品系的124个小区则完全排除在训练之外。仅使用训练数据，从82个候选变量中保留了11个变量；这些候选变量来源于无人机多光谱影像、ERA5-Land再分析数据、LiDAR衍生的静态小区微地形、物候和成熟期组。LightGBM对被排除品系给出的合并R²为0.587；在生长季内对育种品系进行重采样得到的平均R²为0.564，95%置信区间为0.252至0.776。合并值部分反映了生长季之间的强烈差异，而非生长季内品系之间的区分能力。对小区进行随机划分得到的R²为0.890，表明表观预测精度在很大程度上取决于验证设计。观测品系排名与预测品系排名之间的一致性为中等，2024年和2025年的Spearman相关系数分别为0.612和0.650，但由于仅有14个和17个独立品系可用，不确定性范围较宽。六种建模方法产生了相互重叠的不确定性区间，没有明确证据表明哪一种更优，并且完整的数据源组合与保留多光谱模块的较简单配置无法区分。另一项分析中，筛选、选择和拟合仅使用2024年数据，而评估使用2025年数据，结果显示性能严重下降，R² = −8.81，Spearman相关系数为−0.082，与零无法区分。在两个生长季均种植的品系之间，较差的迁移性依然存在，因此仅用品系新颖性不足以解释这种失败。因此，当模型开发过程中涵盖了所观测到的两种生长季情形时，未参与训练的育种品系可以以中等一致性进行排名，而在未观测生长季中的预测仍然不可靠。本研究仅涵盖一个圃系复合体和两个生长季，因此跨地点和更广泛条件下的迁移性仍有待确立。","Smart Agricultural Technology","2026-09-22T00:00:00Z",74,{"impact":17,"substance":162,"depth":161,"authority":45,"freshness":239,"relevant":21,"comment":427},"基于无人机多光谱与气象数据的春小麦育种品系产量预测研究，验证设计严谨、结论审慎，对智慧育种与遥感估产有参考价值，但属单点试验、地域性强，未达重大突破层级。",[429],{"name":423,"url":420},[26,28,29,431,30],"小麦育种",{"code":433,"slug":434,"name":435,"path":436,"parent_code":172},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat",[438],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":440},{"candidates":441,"agricultural_evidence":46},[442,446,450,454],{"code":433,"score":179,"supported":46,"title":443,"abstract":444,"tags":445},[434],[435,434],[431],{"code":32,"score":106,"supported":46,"title":447,"abstract":448,"tags":449},[],[276,277],[28,30],{"code":66,"score":21,"supported":14,"title":451,"abstract":452,"tags":453},[],[],[26],{"code":60,"score":21,"supported":14,"title":455,"abstract":456,"tags":457},[],[],[29],[459,460],"哈萨克斯坦 春小麦 产量预测","UAV 多光谱 育种试验","哈萨克斯坦春小麦产量预测-3328",4,"10.1016\u002Fj.atech.2026.102588",{"doi":463,"openalex_id":465,"authors":466,"venue":423,"cited_by_count":86,"oa_url":420,"card":478,"direction":91,"ingested_from":93},"W7214044328",[467,470,473,476],{"name":468,"orcid":469},"Dastan Yelubayev","https:\u002F\u002Forcid.org\u002F0000-0001-5358-7982",{"name":471,"orcid":472},"TIMUR SAVIN","https:\u002F\u002Forcid.org\u002F0000-0002-3550-647X",{"name":474,"orcid":475},"Ismail Tokbergenov","https:\u002F\u002Forcid.org\u002F0000-0002-0656-9914",{"name":477,"orcid":9},"Bakhtiyar Zhanzakov",{"tldr":479,"method":480,"finding":481,"direction":91,"opportunity":482},"评估春小麦育种品系产量模型在未参与训练品系上的跨季预测能力。","无人机多光谱、ERA5-Land、LiDAR与物候数据，LightGBM等六种模","两季均参与训练时品系排名中等一致，但预测未观测季节完全失效。","需研究跨地点、跨年份可迁移的表型预测模型与验证设计，避免随机划分高估精度。","2026-09-24T23:30:03.201775Z",{"id":485,"title":486,"url":487,"summary":488,"summary_zh":489,"content":9,"source_name":490,"source_url":487,"published_at":491,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":492,"score_detail":493,"sources":495,"tags":497,"primary_topic":499,"secondary_topics":500,"classification":508,"search_phrases":543,"slug":546,"view_count":462,"doi":547,"paper":548,"created_at":568},2659,"Application of Remote Sensing and Machine Learning in Sustainable Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu18189472","Global agriculture is undergoing a period of profound transformation, driven by the need to increase food production in a context characterized by climate change, the degradation of natural resources and increasing pressure on agricultural ecosystems [...]","全球农业正经历一场深刻变革，其驱动力是在气候变化、自然资源退化以及农业生态系统压力日益增大的背景下提高粮食产量的需求。[...]","Sustainability","2026-09-16T00:00:00Z",77,{"impact":161,"substance":18,"depth":107,"authority":45,"freshness":239,"relevant":21,"comment":494},"发表于核心期刊的遥感与机器学习综述，方法视角新颖、时效性强，对智慧农业技术路线有参考价值，值得进入每日精选。",[496],{"name":490,"url":487},[26,27,29,498,30],"可持续农业",{"code":60,"slug":114,"name":115,"path":116,"parent_code":36},[501,507],{"code":502,"slug":503,"name":504,"path":505,"parent_code":506},"H8","sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture","H",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":509},{"candidates":510,"agricultural_evidence":46},[511,515,520,524,528,535,539],{"code":60,"score":13,"supported":46,"title":512,"abstract":513,"tags":514},[262],[],[27,29],{"code":502,"score":239,"supported":46,"title":516,"abstract":518,"tags":519},[517],"sustainable 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