[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3328":3,"related-3328":60},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":59},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。",null,"Smart Agricultural Technology","2026-09-22T00:00:00Z","论文",10,false,74,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,22,18,13,9,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","产量预测","机器学习","小麦育种","遥感",[33,34],"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",0,"10.1016\u002Fj.atech.2026.102588",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":6,"card":52,"direction":56,"ingested_from":58},"W7214044328",[41,44,47,50],{"name":42,"orcid":43},"Dastan Yelubayev","https:\u002F\u002Forcid.org\u002F0000-0001-5358-7982",{"name":45,"orcid":46},"TIMUR SAVIN","https:\u002F\u002Forcid.org\u002F0000-0002-3550-647X",{"name":48,"orcid":49},"Ismail Tokbergenov","https:\u002F\u002Forcid.org\u002F0000-0002-0656-9914",{"name":51,"orcid":9},"Bakhtiyar Zhanzakov",{"tldr":53,"method":54,"finding":55,"direction":56,"opportunity":57},"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。","农业遥感与作物表型","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。","openalex","2026-09-24T23:30:03.201775Z",{"total":61,"page":22,"page_size":61,"items":62},6,[63,102,150,180,237,269],{"id":64,"title":65,"url":66,"summary":67,"summary_zh":68,"content":9,"source_name":69,"source_url":66,"published_at":70,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":71,"score_detail":72,"sources":77,"tags":79,"search_phrases":81,"slug":84,"view_count":22,"doi":85,"paper":86,"created_at":101},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。","Remote Sensing Letters","2026-09-01T00:00:00Z",67,{"impact":17,"substance":73,"depth":74,"authority":17,"freshness":75,"relevant":22,"comment":76},20,16,7,"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。",[78],{"name":69,"url":66},[27,80,28,29,31],"农业人工智能",[82,83],"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","10.1080\u002F2150704x.2026.2724307",{"doi":85,"openalex_id":87,"authors":88,"venue":69,"cited_by_count":36,"oa_url":9,"card":96,"direction":56,"ingested_from":58},"W7204870154",[89,91,94],{"name":90,"orcid":9},"M.R. Najam",{"name":92,"orcid":93},"Hasnat Khurshid","https:\u002F\u002Forcid.org\u002F0000-0001-8722-5904",{"name":95,"orcid":9},"Faisal Akram",{"tldr":97,"method":98,"finding":99,"direction":56,"opportunity":100},"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。","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。","2026-09-02T23:30:51.479183Z",{"id":103,"title":104,"url":105,"summary":106,"summary_zh":107,"content":9,"source_name":108,"source_url":105,"published_at":109,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":110,"score_detail":111,"sources":114,"tags":116,"search_phrases":117,"slug":120,"view_count":22,"doi":121,"paper":122,"created_at":149},1307,"Estimation of the Comprehensive High Photosynthetic-Efficiency Phenotypic Index in Winter Wheat Based on UAV Multimodal Remote Sensing Data","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fplants15172674","Accurate monitoring of photosynthetic phenotypes is fundamental for breeding high photosynthetic-efficiency wheat cultivars, and UAV remote sensing provides an effective approach for their large-scale identification. However, single photosynthetic parameters are limited in comprehensively evaluating crop photosynthetic efficiency. This study integrated multiple photosynthetic phenotypic parameters using principal component analysis (PCA) and the CRITIC objective weighting method (PCA-CRITIC) to construct a Comprehensive High Photosynthetic-Efficiency Phenotypic Index (CHPPI) for winter wheat. Concurrently, multispectral vegetation indices (MSVI), RGB vegetation indices (RGBVI), texture features (TF), and their combination, Comprehensive Multispectral-Visible-Texture Features (CMVTF), were extracted from UAV multimodal remote sensing data. Based on these features, four feature selection methods, namely PCC, VIP, SPA, and UVE, were evaluated in combination with five machine learning algorithms, including KNN, DT, SVR, RF, and XGBoost, to precisely estimate the CHPPI across key growth stages. The results demonstrated that the CHPPI exhibited significant cultivar variations across growth stages. Cultivars SH06144 and Liangxing19 consistently maintained high photosynthetic efficiency levels. The fused feature set CMVTF derived from multimodal remote sensing data outperformed individual feature categories, and the feature subset selected by the UVE method most significantly enhanced model accuracy. Regarding algorithms, the ensemble-based XGBoost and RF models markedly surpassed traditional machine learning algorithms. Specifically, the CMVTF-UVE-XGBoost model achieved the optimal comprehensive performance, yielding an R2 of 0.862, an RMSE of 0.032, and an MAE of 0.027 on the testing set, with its spatial distribution estimations highly consistent with measured values. This study provides robust technical support for the rapid and large-scale screening of high photosynthetic-efficiency wheat breeding materials.","光合表型精准监测是培育高光效小麦品种的基础，无人机遥感为其大规模鉴定提供了有效途径。然而，单一光合参数难以全面评价作物光合效率。本研究利用主成分分析(PCA)与CRITIC客观赋权法(PCA-CRITIC)整合多个光合表型参数，构建了冬小麦高光效综合表型指数(CHPPI)。同时，从无人机多模态遥感数据中提取多光谱植被指数(MSVI)、RGB植被指数(RGBVI)、纹理特征(TF)及其融合特征——多光谱-可见光-纹理综合特征(CMVTF)。基于上述特征，评估了PCC、VIP、SPA和UVE四种特征选择方法，并结合KNN、DT、SVR、RF和XGBoost五种机器学习算法，对关键生育期的CHPPI进行精准估算。结果表明，CHPPI在不同生育期表现出显著的品种间差异，品种SH06144和良星19始终保持较高的光合效率水平。基于多模态遥感数据构建的融合特征集CMVTF优于单一特征类别，且UVE方法筛选的特征子集对模型精度的提升最为显著。在算法方面，基于集成的XGBoost和RF模型明显优于传统机器学习算法。具体而言，CMVTF-UVE-XGBoost模型取得了最优的综合性能，在测试集上R²为0.862，RMSE为0.032，MAE为0.027，其空间分布估算结果与实测值高度一致。本研究为高光效小麦育种材料的快速大规模筛选提供了有力的技术支撑。","Plants","2026-08-31T00:00:00Z",73,{"impact":19,"substance":18,"depth":19,"authority":20,"freshness":112,"relevant":22,"comment":113},2,"基于无人机多模态遥感构建小麦光合效率综合指数，方法新颖且模型精度高，对育种筛选有实用价值。",[115],{"name":108,"url":105},[27,29,30,31],[118,119],"小麦育种 智慧农业 机器学习 遥感","小麦育种 智慧农业","小麦育种智慧农业机器学习遥感-1307","10.3390\u002Fplants15172674",{"doi":121,"openalex_id":123,"authors":124,"venue":108,"cited_by_count":36,"oa_url":105,"card":144,"direction":56,"ingested_from":58},"W7204845072",[125,127,129,131,133,135,137,139,141],{"name":126,"orcid":9},"Ning Yang",{"name":128,"orcid":9},"Dayong Cui",{"name":130,"orcid":9},"Songming Lin",{"name":132,"orcid":9},"Changliang Du",{"name":134,"orcid":9},"Liwen Wang",{"name":136,"orcid":9},"Fei Zhang",{"name":138,"orcid":9},"Zhu Shi",{"name":140,"orcid":9},"Bingqian Hou",{"name":142,"orcid":143},"Junke Zhu","https:\u002F\u002Forcid.org\u002F0009-0005-7778-9614",{"tldr":145,"method":146,"finding":147,"direction":56,"opportunity":148},"基于无人机多模态遥感数据，构建综合高光效表型指数并精准估算，用于小麦育种材料筛选。","PCA-CRITIC构建CHPPI，提取多模态特征，结合UVE特征选择与XGBo","CMVTF-UVE-XGBoost模型表现最优，测试集R²=0.862，可快速大规模筛选高光效小麦。","可探索将CHPPI估算模型推广至其他作物或不同环境，并引入深度学习或时序数据提升泛化能力。","2026-09-01T23:30:16.317443Z",{"id":151,"title":152,"url":153,"summary":154,"summary_zh":9,"content":9,"source_name":155,"source_url":9,"published_at":156,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":157,"score_detail":158,"sources":161,"tags":163,"search_phrases":166,"slug":169,"view_count":36,"doi":9,"paper":170,"created_at":179},3324,"Diag-STFN：全球收获前作物产量预测的诊断时空多模态融合网络——覆盖38国玉米29国小麦（Ecological Informatics 2026）","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F37354974?&&labelLang=hun","《Ecological Informatics》2026年第96期：Zhuang等提出Diag-STFN——一种诊断时空多模态融合网络，用于全球收获前作物产量预测。该网络基于数据集特征选择模型结构，以确定是否需要时间趋势耦合和空间模块激活。在三种前置期（早、中、晚季）下，基于覆盖38国玉米和29国小麦的CY-Bench基准数据集进行评估。结果表明，所提方法在所有前置期均实现了两种作物的最低汇总NRMSE，并在MAPE和KGE等补充指标上保持领先。消融研究表明诊断模块选择提供了主要的性能提升；方差分解显示性能差异在国家之间大于模型之间。","《Ecological Informatics》96 (2026) 103860","2026-09-17T00:00:00Z",78,{"impact":19,"substance":18,"depth":19,"authority":159,"freshness":61,"relevant":22,"comment":160},14,"方法新颖、覆盖38国玉米与29国小麦的全球收获前产量预测研究，学术价值突出但产业落地尚早，适合作为前沿技术资讯收录。",[162],{"name":155,"url":153},[27,80,28,164,165,31],"小麦","玉米",[167,168],"Diag-STFN 作物产量预测","CY-Bench 玉米 小麦","Diag-STFN作物产量预测-3324",{"doi":9,"openalex_id":9,"authors":171,"venue":9,"cited_by_count":36,"oa_url":9,"card":172,"direction":176,"ingested_from":178},[],{"tldr":173,"method":174,"finding":175,"direction":176,"opportunity":177},"提出诊断式时空多模态融合网络Diag-STFN，实现全球收获前玉米小麦产量预测。","基于CY-Bench基准，按数据特征诊断选择时间趋势与空间模块，覆盖38国玉米2","各前置期均取得最低NRMSE，诊断模块选择贡献最大，国家间差异大于模型间差异。","农业人工智能与决策模型","可探索自适应诊断机制迁移至其他作物，并针对国家间差异开展区域化建模与不确定性量化。","agent","2026-09-24T00:04:02.684732Z",{"id":181,"title":182,"url":183,"summary":184,"summary_zh":185,"content":9,"source_name":10,"source_url":183,"published_at":186,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":187,"score_detail":188,"sources":191,"tags":193,"search_phrases":195,"slug":198,"view_count":36,"doi":199,"paper":200,"created_at":236},3258,"Machine learning models combined with feature importance methods for honey yield classification: A replicable approach","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102575","Beekeepers require planning tools supported by modern technologies, such as machine learning and the Internet of Things, to address agricultural challenges such as the decrease and irregularity in honey production. To ensure replicability, this article presents a research workflow that begins with the creation of an open-access database, developed from annual production records and climatic variables (temperature and rainfall), integrating data construction, explainability analysis, and model evaluation. Then, feature importance methods and explainability techniques are applied, such as feature importance, the depth-wise frequency of each feature in random forest, and the Shapley Additive Explanations method. Finally, machine learning approaches are evaluated for honey yield prediction: logistic regression, k-nearest neighbors, support vector machine, decision tree, multilayer perceptron, random forest, linear discriminant analysis, gradient boosting, and Naive Bayes. These algorithms are compared considering: (1) a baseline corresponding to models without hyperparameter optimization, using leave-one-out cross-validation and stratified 10-fold cross-validation; (2) the baseline plus normalization\u002Fstandardization (div-max, min-max, and z-score); (3) the configuration in point 2 plus bagging; (4) evaluation of a data-augmentation and class-balancing strategy using SMOTE, together with model combination via the Voting Classifier. The results suggest that rainfall is one of the most important variables for honey yield prediction. By selecting certain features, the models improve in some cases or do not significantly degrade their performance. The min-max and z-score methods led to improved predictions in some algorithms; for example, support vector machine achieved an accuracy of 0.80, compared with the 0.67 accuracy reported in the reference study based on random forest, representing an increase of 13 percentage points. Finally, bagging techniques, SMOTE oversampling, and the Voting Classifier, using algorithms such as KNN and SVM, can achieve an ACC of 0.82. Overall, this study proposes a replicable data mining-based workflow that integrates machine learning techniques for predicting honey yield from climatic variables, including the use of an open-access dataset, explainability analysis, and a comparative evaluation of machine learning models, contributing to the development of future analysis and planning tools in the beekeeping sector.","养蜂人需要借助机器学习和物联网等现代技术支持的规划工具，以应对蜂蜜产量下降和波动等农业挑战。为确保可复现性，本文提出了一套研究流程：首先构建一个开放获取数据库，该数据库基于年度生产记录和气候变量（温度和降雨量）开发，并整合了数据构建、可解释性分析和模型评估。随后，应用特征重要性方法和可解释性技术，如特征重要性、随机森林中各特征的深度频率以及Shapley加性解释方法。最后，评估多种机器学习方法用于蜂蜜产量预测：逻辑回归、k近邻、支持向量机、决策树、多层感知机、随机森林、线性判别分析、梯度提升和朴素贝叶斯。这些算法在以下方面进行比较：（1）基线模型，即未进行超参数优化的模型，采用留一交叉验证和分层10折交叉验证；（2）基线加归一化\u002F标准化（最大值除法、最小-最大和z-score）；（3）第2点配置加装袋法；（4）使用SMOTE评估数据增强和类别平衡策略，并结合投票分类器进行模型组合。结果表明，降雨量是蜂蜜产量预测中最重要的变量之一。通过选择特定特征，模型在某些情况下性能得到提升，或性能未显著下降。最小-最大和z-score方法使部分算法的预测效果得到改善；例如，支持向量机达到了0.80的准确率，而参考研究中基于随机森林的准确率为0.67，提升了13个百分点。最后，使用KNN和SVM等算法结合装袋技术、SMOTE过采样和投票分类器，可以达到0.82的准确率。总体而言，本研究提出了一套可复现的、基于数据挖掘的工作流程，整合了机器学习技术以从气候变量预测蜂蜜产量，包括使用开放获取数据集、可解释性分析以及机器学习模型的比较评估，为养蜂领域未来分析和规划工具的开发做出了贡献。","2026-09-20T00:00:00Z",71,{"impact":17,"substance":73,"depth":189,"authority":20,"freshness":21,"relevant":22,"comment":190},17,"该论文提出可复现的机器学习工作流，结合开放数据集与可解释性方法预测蜂蜜产量，方法新颖、结论可靠，对养蜂业数字化规划有参考价值。",[192],{"name":10,"url":183},[27,80,28,29,194],"蜂产业",[196,197],"蜂蜜产量 机器学习 预测","Smart Agricultural Technology 蜂蜜","蜂蜜产量机器学习预测-3258","10.1016\u002Fj.atech.2026.102575",{"doi":199,"openalex_id":201,"authors":202,"venue":10,"cited_by_count":36,"oa_url":183,"card":231,"direction":176,"ingested_from":58},"W7213773430",[203,206,209,212,214,217,220,223,225,228],{"name":204,"orcid":205},"Roberto Ahumada‐García","https:\u002F\u002Forcid.org\u002F0000-0003-1107-4606",{"name":207,"orcid":208},"David Zabala‐Blanco","https:\u002F\u002Forcid.org\u002F0000-0002-5692-5673",{"name":210,"orcid":211},"Víctor Hugo Monzón","https:\u002F\u002Forcid.org\u002F0000-0001-9729-7768",{"name":213,"orcid":9},"Iván Sánchez",{"name":215,"orcid":216},"Nádia Félix Felipe da Silva","https:\u002F\u002Forcid.org\u002F0000-0002-3875-2211",{"name":218,"orcid":219},"Thierson Couto Rosa","https:\u002F\u002Forcid.org\u002F0000-0001-7117-3994",{"name":221,"orcid":222},"Alef Iury Siqueira Ferreira","https:\u002F\u002Forcid.org\u002F0000-0002-9119-6357",{"name":224,"orcid":9},"Xaviera López-Cortés",{"name":226,"orcid":227},"Marco Javier Flores-Calero","https:\u002F\u002Forcid.org\u002F0000-0001-7507-3325",{"name":229,"orcid":230},"Philip Vásquez-Iglesias","https:\u002F\u002Forcid.org\u002F0009-0008-2109-8787",{"tldr":232,"method":233,"finding":234,"direction":176,"opportunity":235},"构建可复现工作流，用气候变量与机器学习分类蜂蜜产量。","开放数据库、特征重要性\u002FSHAP、9种ML模型、SMOTE与投票集成。","降雨是最重要变量；SVM准确率0.80，集成后达0.82。","可扩展至多源物联网数据与实时预测，开发养蜂决策支持工具。","2026-09-23T23:30:03.793669Z",{"id":238,"title":239,"url":240,"summary":241,"summary_zh":242,"content":9,"source_name":243,"source_url":240,"published_at":244,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":245,"score_detail":246,"sources":249,"tags":251,"search_phrases":253,"slug":256,"view_count":36,"doi":257,"paper":258,"created_at":268},3197,"YieldVision: Model-Driven Decision Support System for Crop Yield Prediction","https:\u002F\u002Fdoi.org\u002F10.56201\u002Frjpst.vol.9.no1.2026.pg176.194","Agriculture, a cornerstone of global food security, faces unprecedented challenges in achieving sustainable productivity. Crop yield prediction, a crucial aspect of agricultural planning, suffers from inefficiencies rooted in technological and methodological gaps. While traditional approaches rely on historical data and empirical estimations, they often fail to address the complexity of environmental interactions because they lack real-time data integration and limited access to accurate data, leading to inaccuracies and resource mismanagement. Addressing these issues requires the development of advanced Machine Learning (ML) models-based system that can predict crop yield with high accuracy in dynamic environments. This paper presents YieldVision, a decision support system driven by advanced ML models for Crop Yield Prediction based on environmental, soil, and crop-specific factors. In this work, we proposed three crop prediction models: Random Forest (RF), Long Short Time Memory (LSTM) and Hybrid (RF LSTM). RF regressor effectively captured the complex, nonlinear relationships among soil nutrients, fertilizer application, and temperature. The LSTM network learned the way short-term weather fluctuations impact crop development over time, and the hybrid model combined LSTM for environmental-related factors, and RF for soil-related factors. Experimental results on the Crop Yield Prediction Dataset containing soil and weather parameters showed that RF has the highest accuracy 𝑅2 = 99.10% and outperforms both LSTM and hybrid which have 87.50% and 97.30% accuracy respectively, confirming its suitability for the dataset. RF has the lowest RMSE and MAE compared to LSTM and hybrid models. The models are integrated into a decision support system that is deployed to operationalize the models in real-world settings with user interfaces accessible via mobile or web-delivered real-time alerts and yield forecasts to farmers and agronomists.","农业作为全球粮食安全的基石，在实现可持续生产力方面面临着前所未有的挑战。作物产量预测作为农业规划的关键环节，因技术和方法上的不足而效率低下。传统方法依赖历史数据和经验估算，但由于缺乏实时数据集成且难以获取准确数据，往往无法应对环境相互作用的复杂性，导致预测不准确和资源管理不善。解决这些问题需要开发基于先进机器学习（ML）模型的系统，能够在动态环境中高精度地预测作物产量。本文提出了YieldVision，一个由先进机器学习模型驱动的决策支持系统，用于基于环境、土壤和作物特定因素的作物产量预测。在本研究中，我们提出了三种作物预测模型：随机森林（RF）、长短期记忆网络（LSTM）和混合模型（RF-LSTM）。随机森林回归器有效捕捉了土壤养分、施肥量和温度之间复杂的非线性关系。LSTM网络学习了短期天气波动如何随时间影响作物发育，而混合模型将LSTM用于环境相关因素，RF用于土壤相关因素。在包含土壤和天气参数的作物产量预测数据集上的实验结果表明，随机森林具有最高的准确率𝑅2 = 99.10%，优于LSTM和混合模型，后两者的准确率分别为87.50%和97.30%，证实了随机森林对该数据集的适用性。与LSTM和混合模型相比，随机森林具有最低的RMSE和MAE。这些模型被集成到一个决策支持系统中，该系统已部署用于在实际环境中运行这些模型，其用户界面可通过移动端或网页访问，向农民和农艺师提供实时警报和产量预测。","RESEARCH JOURNAL OF PURE SCIENCE AND TECHNOLOGY","2026-09-21T00:00:00Z",68,{"impact":17,"substance":73,"depth":74,"authority":17,"freshness":247,"relevant":22,"comment":248},8,"提出RF、LSTM及混合模型并集成决策支持系统，方法对比与精度数据扎实，对智慧农业产量预测有参考价值，但属单篇论文且来源期刊影响力有限。",[250],{"name":243,"url":240},[27,80,28,29,252],"决策支持系统",[254,255],"YieldVision 作物产量预测","RF LSTM 混合模型 产量预测","YieldVision作物产量预测-3197","10.56201\u002Frjpst.vol.9.no1.2026.pg176.194",{"doi":257,"openalex_id":259,"authors":260,"venue":243,"cited_by_count":36,"oa_url":9,"card":263,"direction":176,"ingested_from":58},"W7213883348",[261],{"name":262,"orcid":9},"D.J.S. Sako",{"tldr":264,"method":265,"finding":266,"direction":176,"opportunity":267},"提出YieldVision决策支持系统，用RF、LSTM及混合模型预测作物产量并部署应用。","基于土壤、环境与作物数据，构建RF、LSTM和RF-LSTM混合模型并集成到We","随机森林表现最佳，R²达99.10%，优于LSTM（87.50%）和混合模型（97.30%）。","可探索多源实时数据融合与模型可解释性，提升跨区域泛化能力并降低对高精度历史数据的依赖。","2026-09-22T23:30:43.591303Z",{"id":270,"title":271,"url":272,"summary":273,"summary_zh":274,"content":9,"source_name":275,"source_url":272,"published_at":244,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":276,"score_detail":277,"sources":279,"tags":281,"search_phrases":283,"slug":286,"view_count":36,"doi":287,"paper":288,"created_at":306},3177,"A comparative analysis on maize yield prediction using sentinel 2A and Landsat 8 satellite image in Sundarganj, Gaibandha, Bangladesh","https:\u002F\u002Fdoi.org\u002F10.3329\u002Fbjar.v51i1.92530","Maize is an important cereal crops in Bangladesh. Over the last two decades, its cultivation has increased promisingly, especially in the Northern part of Bangladesh. The effective estimation of crop yields at a regional scale holds significant importance in facilitating decision-making within the agricultural sector, thereby ensuring grain security. The traditional ground-based measurement techniques suffer from inefficiencies, and there exists a need for a reliable, precise, and effective method for estimating regional crop yields. This study used remote sensing (RS) techniques for forecasting pre-harvest maize yield to improve the management system. Currently, the normalized difference vegetation index (NDVI) is widely used to predict crop yield including maize. However, the present study used Landsat 8 (~ 30 m) and Sentinel 2A (~ 10 m) high resolution data for 2018-2019 and 2019-2020 to predict maize yield based on the year 2020-2021 at Sundarganj Upazila in Gaibandha district. The single cloud free image acquisition date based on maximum NDVI for both satellite images was used for each maize growing period to develop a yield prediction model. A regression model was performed between NDVI values and 20 farmers field-level maize yields. The absolute mean error of prediction was about 10.30% for Landsat 8 and 6.70% for Sentinel 2A compared to the actual maize yield during 2020-2021. The study revealed that NDVI data extracted from Sentinel 2A high resolution satellite images can be successfully used to predict the maize yield with appreciable accuracy. Finally, this study has demonstrated the efficacy of combining multi-temporal remote sensing data for accurate maize yield estimation, aiding agricultural authorities and production enterprises in the timely formulation and refinement of cropping strategies and management policies for the ongoing season. Bangladesh J. Agril. Res. 51(1): 501-521, March 2026","玉米是孟加拉国重要的谷类作物。过去二十年间，其种植面积增长显著，尤其是在孟加拉国北部地区。在区域尺度上有效估算作物产量对于促进农业部门决策、进而保障粮食安全具有重要意义。传统的地面测量技术效率低下，亟需一种可靠、精确且有效的区域作物产量估算方法。本研究采用遥感（RS）技术预测收获前玉米产量，以改进管理体系。目前，归一化植被指数（NDVI）被广泛用于预测包括玉米在内的作物产量。然而，本研究利用Landsat 8（约30 m）和Sentinel 2A（约10 m）高分辨率数据，基于2018—2019年和2019—2020年的数据，对盖班达县孙达尔甘杰乌帕齐拉2020—2021年的玉米产量进行预测。在每个玉米生长期，选取两颗卫星影像中NDVI最大值对应的单幅无云影像获取日期，用于建立产量预测模型。对NDVI值与20户农民田块级玉米产量进行回归建模。与2020—2021年实际玉米产量相比，Landsat 8的绝对平均预测误差约为10.30%，Sentinel 2A约为6.70%。研究表明，利用Sentinel 2A高分辨率卫星影像提取的NDVI数据可成功用于预测玉米产量，且精度令人满意。最后，本研究证明了结合多时相遥感数据进行准确玉米产量估算的有效性，有助于农业主管部门和生产企业在当季及时制定和完善种植策略与管理政策。Bangladesh J. Agril. Res. 51(1): 501-521, March 2026","Bangladesh Journal of Agricultural Research",65,{"impact":247,"substance":73,"depth":74,"authority":20,"freshness":247,"relevant":22,"comment":278},"基于Sentinel 2A与Landsat 8的玉米遥感估产对比研究，方法清晰、误差数据具体，对遥感估产有参考价值，但属区域小尺度研究，公共影响有限。",[280],{"name":275,"url":272},[27,28,165,31,282],"NDVI",[284,285],"Sentinel 2A Landsat 8 玉米产量预测","孟加拉国 Sundarganj 玉米遥感估产","Sentinel2ALandsat8玉米产量预测-3177","10.3329\u002Fbjar.v51i1.92530",{"doi":287,"openalex_id":289,"authors":290,"venue":275,"cited_by_count":36,"oa_url":272,"card":301,"direction":56,"ingested_from":58},"W7213918280",[291,293,295,297,299],{"name":292,"orcid":9},"N Mohammad",{"name":294,"orcid":9},"MA Islam",{"name":296,"orcid":9},"MG Mahboob",{"name":298,"orcid":9},"MM Rahman",{"name":300,"orcid":9},"I Ahmed",{"tldr":302,"method":303,"finding":304,"direction":56,"opportunity":305},"用Sentinel 2A与Landsat 8的NDVI回归模型预测孟加拉国玉米产量并比较精度。","基于最大NDVI单期影像与20个农户地块产量做回归，比较两种卫星。","Sentinel 2A预测绝对平均误差6.70%，优于Landsat 8的10.30%。","可探索多时相NDVI与机器学习融合，提升小农户尺度玉米估产精度与迁移性。","2026-09-22T23:30:23.551878Z"]