[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3483":3,"related-3483":92},{"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":68,"slug":71,"view_count":21,"doi":72,"paper":73,"created_at":91},3483,"АГРОМЕТЕОРОЛОГІЧНІ УМОВИ ТА ДИНАМІКА ВЕГЕТАЦІЙНИХ ІНДЕКСІВ ПШЕНИЦІ ОЗИМОЇ У ПІВДЕННОМУ СТЕПУ УКРАЇНИ","https:\u002F\u002Fdoi.org\u002F10.32848\u002Fagrar.innov.2026.37.23","Комплексно оцінити агрометеорологічні умови вегетаційних періодів 2021–2025 рр. для пшениці озимої в умовах Південного Степу України та встановити закономірності динаміки вегетаційних індексів NDVI, NDRE і NDMI під впливом сезонної мінливості температурного режиму, опадів і дефіциту вологи. Методи. Дослідження проведено на базі Навчально-науково- практичного центру Миколаївського національного аграрного університету. Використано метеорологічні дані автоматичної станції Pessl Instruments (iMETOS) за 2021–2025 рр. та супутникові дані Sentinel‑2 SR Harmonized, оброблені в середовищі Google Earth Engine. Для оцінки стану посівів розраховано індекси NDVI, NDRE і NDMI та виконано їх порівняльний аналіз у взаємозв’язку з температурним режимом, кількістю опадів і перебігом основних фенологічних фаз розвитку пшениці озимої. Результати. Встановлено істотні міжрічні відмінності агрометеорологічних умов, які визначали характер формування та сезонної динаміки вегетаційних індексів. Найсприятливішими для розвитку рослин були вегетаційні періоди 2021–2022 та 2022–2023 рр., коли значення NDVI досягали 0,8–0,9, а NDRE – 0,6–0,7. Вегетаційний період 2023–2024 рр. характеризувався пригніченим осіннім розвитком і компенсаторним весняним наростанням біомаси, тоді як у 2024–2025 рр. добрі умови осінньої вегетації не забезпечили тривалого весняного розвитку через холодну й відносно суху весну. Встановлено, що найбільш критичним періодом розвитку водного стресу був червень, коли рослини переходили від фази колосіння до наливу зерна. Індекс NDMI найраніше реагував на погіршення водного режиму посівів і був найбільш чутливим індикатором розвитку водного стресу, тоді як скорочення тривалості плато NDVI відображало обмеження реалізації продуктивного потенціалу посівів. Висновки. Динаміка вегетаційних індексів пшениці озимої тісно пов’язана з особливостями сезонного перебігу температурного режиму та зволоження. Вирішальне значення для формування фізіологічного стану рослин мали умови весняного періоду, а найбільш критичним щодо розвитку водного стресу був перехід від колосіння до наливу зерна. Індекс NDMI доцільно використовувати як ранній індикатор водного стресу, тоді як комплексне застосування NDVI, NDRE і NDMI забезпечує ефективний моніторинг стану посівів, а скорочення тривалості плато NDVI може бути використане як індикатор обмеження реалізації продуктивного потенціалу озимої пшениці","综合评估2021—2025年乌克兰南部草原冬小麦（пшениця озима）生育期农业气象条件，并揭示NDVI、NDRE和NDMI植被指数在温度状况、降水和水分亏缺季节性变化影响下的动态规律。方法。研究在尼古拉耶夫国立农业大学教学-科学-实践中心基地进行。使用了Pessl Instruments自动气象站（iMETOS）2021—2025年的气象数据，以及经Google Earth Engine环境处理的Sentinel-2 SR Harmonized卫星数据。为评估作物状况，计算了NDVI、NDRE和NDMI指数，并结合温度状况、降水量及冬小麦主要物候发育阶段进程对其进行了比较分析。结果。确定了农业气象条件的显著年际差异，这些差异决定了植被指数的形成特征和季节性动态。2021—2022年和2022—2023年生育期对植株发育最为有利，NDVI值达到0.8—0.9，NDRE为0.6—0.7。2023—2024年生育期的特点是秋季发育受抑制，春季生物量补偿性增长，而2024—2025年良好的秋季生长条件未能保障春季的持续发育，原因是春季寒冷且相对干燥。确定水分胁迫发展的最关键时期为6月，此时植株从抽穗期过渡到籽粒灌浆期。NDMI指数对作物水分状况恶化反应最早，是水分胁迫发展最敏感的指标，而NDVI平台期持续时间的缩短则反映了作物生产潜力实现的受限。结论。冬小麦植被指数动态与温度状况和湿润条件的季节性进程特征密切相关。春季条件对植株生理状态的形成具有决定性意义，而就水分胁迫发展而言，从抽穗到籽粒灌浆的过渡期最为关键。NDMI指数宜作为水分胁迫的早期指标使用，而NDVI、NDRE和NDMI的综合应用可有效监测作物状况，NDVI平台期持续时间的缩短可作为冬小麦生产潜力实现受限的指标。",null,"Аграрні інновації","2026-09-23T00:00:00Z","论文",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},12,21,17,8,1,"基于五年气象站与Sentinel-2数据系统分析冬小麦植被指数动态，方法规范、结论明确，对遥感作物监测有参考价值，但属区域性研究，公共影响有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","遥感监测","水分胁迫","冬小麦","植被指数",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,50,57,63],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":49},4,true,[],[34],[29],{"code":51,"score":52,"supported":46,"title":53,"abstract":54,"tags":56},"H7",3,[],[55],"农业气象",[],{"code":58,"score":59,"supported":14,"title":60,"abstract":61,"tags":62},"I3",2,[],[],[30,27],{"code":64,"score":21,"supported":14,"title":65,"abstract":66,"tags":67},"I1",[],[],[26],[69,70],"乌克兰 南方草原 冬小麦 NDVI","Sentinel-2 NDMI 冬小麦 水分胁迫","乌克兰南方草原冬小麦NDVI-3483","10.32848\u002Fagrar.innov.2026.37.23",{"doi":72,"openalex_id":74,"authors":75,"venue":10,"cited_by_count":82,"oa_url":83,"card":84,"direction":88,"ingested_from":90},"W7214071557",[76,79],{"name":77,"orcid":78},"Антонина Панфилова","https:\u002F\u002Forcid.org\u002F0000-0003-0006-4090",{"name":80,"orcid":81},"Dmytro Koshkin","https:\u002F\u002Forcid.org\u002F0000-0002-6927-8487",0,"https:\u002F\u002Fagrarian-innovations.izpr.ks.ua\u002Findex.php\u002Fagrarian\u002Farticle\u002Fdownload\u002F1168\u002F1166",{"tldr":85,"method":86,"finding":87,"direction":88,"opportunity":89},"评估乌克兰南部2021–2025年冬小麦生育期农业气象条件与NDVI、NDRE、NDMI植被指数动态","iMETOS自动气象站数据与Sentinel-2影像，在Google Earth","NDMI对水分胁迫响应最早最敏感，NDVI平台期缩短可指示产量潜力受限，抽穗至灌浆期为关键期。","农业遥感与作物表型","可探索多指数融合的冬小麦水分胁迫早期预警阈值及平台期缩短与产量的定量模型。","openalex","2026-09-25T23:30:21.548558Z",{"total":93,"page":21,"page_size":93,"items":94},6,[95,189,260,317,386,472],{"id":96,"title":97,"url":98,"summary":99,"summary_zh":100,"content":9,"source_name":101,"source_url":98,"published_at":102,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":103,"score_detail":104,"sources":108,"tags":110,"primary_topic":113,"secondary_topics":114,"classification":127,"search_phrases":165,"slug":168,"view_count":82,"doi":169,"paper":170,"created_at":188},5558,"Estimation of Soil Water Content in Winter Wheat Field at Jointing Stage Using Multi-Source Features and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202071","Accurate and timely monitoring of soil water content (SWC) plays an essential supporting role in crop irrigation management and smart agricultural practice. Remote sensing technology provides rapid and convenient approaches to large-scale farmland SWC estimation or inversion. However, existing mechanistic modeling methods are limited by parameter localization and land surface heterogeneity, while machine learning data-driven methods lack quite good physical interpretability. To address deficiencies in single data-driven methods and single vegetation microwave scattering models, and explore the estimation effect of different feature variables in machine learning. This study proposes an innovative estimation model that integrates machine learning strategies and multi-source features, including spectral indices and features derived from the classic water cloud model (WCM). The novelty of the proposed model lies in balancing physical interpretability and data-driven accuracy, namely extracting relevant soil backscattering parameters through WCM, which are then used as input features in the machine learning. A combination of field-measured data and unmanned aerial vehicle (UAV) multi-source remote sensing data, including multispectral and synthetic aperture radar (SAR) data, was acquired synchronously at the jointing stage of winter wheat. Through extracting variables such as the SAR microwave backscattering coefficient and the optical vegetation index, and conducting feature screening and parameter sensitivity analysis, we optimized the input variable sets of models based on back-propagation (BP) neural network, support vector regression (SVR) and random forest (RF) algorithms. The study results showed that the established estimation model had excellent estimation performance, especially for the RF algorithm, with the highest R2 of 0.855, lowest MAE of 0.0212 cm3\u002Fcm3, and lowest RMSE of 0.0253 cm3\u002Fcm3. It was also shown that different feature input schemes have obvious effects on SWC estimation performance. On the whole, RF had the best estimation accuracy, followed by SVR and then BPNN. This study demonstrated that models integrating multi-source remote sensing data and machine learning could enhance SWC estimation ability for winter wheat fields, and could also serve as a promising approach for SWC monitoring in winter wheat fields in the North China Plain.","准确、及时地监测土壤含水量（SWC）对作物灌溉管理和智慧农业实践具有重要的支撑作用。遥感技术为大规模农田土壤含水量估算或反演提供了快速便捷的手段。然而，现有机理建模方法受参数本地化和地表异质性限制，机器学习数据驱动方法则缺乏良好的物理解释性。针对单一数据驱动方法和单一植被微波散射模型的不足，并探究不同特征变量在机器学习中的估算效果，本研究提出了一种融合机器学习策略与多源特征的创新估算模型，所采用特征包括光谱指数和经典水云模型（WCM）衍生的特征。该模型的新颖性在于平衡物理解释性与数据驱动精度，即通过WCM提取相关土壤后向散射参数，再将其作为机器学习的输入特征。研究在冬小麦拔节期同步获取了田间实测数据与无人机（UAV）多源遥感数据，包括多光谱和合成孔径雷达（SAR）数据。通过提取SAR微波后向散射系数和光学植被指数等变量，并进行特征筛选和参数敏感性分析，优化了基于反向传播（BP）神经网络、支持向量回归（SVR）和随机森林（RF）算法的模型输入变量集。研究结果表明，所建立的估算模型具有优良的估算性能，尤其是RF算法，最高R²为0.855，最低MAE为0.0212 cm³\u002Fcm³，最低RMSE为0.0253 cm³\u002Fcm³。研究还表明，不同特征输入方案对土壤含水量估算性能有明显影响。总体而言，RF估算精度最佳，SVR次之，BPNN再次之。本研究证明，融合多源遥感数据与机器学习的模型能够提升冬小麦田土壤含水量估算能力，也可为华北平原冬小麦田土壤含水量监测提供一种有前景的方法。","Agronomy","2026-10-21T00:00:00Z",77,{"impact":105,"substance":18,"depth":19,"authority":106,"freshness":13,"relevant":21,"comment":107},16,13,"该研究融合多源遥感与机器学习估算冬小麦拔节期土壤含水量，方法有创新、结论可靠，对智慧灌溉有参考价值，但属细分领域论文，影响范围有限。",[109],{"name":101,"url":98},[26,111,27,112,29],"机器学习","土壤墒情",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[115,121],{"code":116,"slug":117,"name":118,"path":119,"parent_code":120},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":122,"slug":123,"name":124,"path":125,"parent_code":126},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":128},{"candidates":129,"agricultural_evidence":46},[130,136,142,147,154,161],{"code":32,"score":131,"supported":46,"title":132,"abstract":134,"tags":135},26,[133,33],"winter wheat",[34,133,33],[29],{"code":116,"score":137,"supported":46,"title":138,"abstract":139,"tags":141},15,[117],[140,117],"土壤",[112],{"code":122,"score":137,"supported":46,"title":143,"abstract":145,"tags":146},[144],"machine learning",[111,144],[111],{"code":58,"score":13,"supported":46,"title":148,"abstract":149,"tags":153},[],[150,151,152],"遥感","remote sensing","multispectral",[27],{"code":155,"score":93,"supported":46,"title":156,"abstract":157,"tags":160},"H3",[],[158,159],"灌溉","irrigation",[],{"code":64,"score":45,"supported":46,"title":162,"abstract":163,"tags":164},[],[26],[26],[166,167],"冬小麦 土壤含水量 遥感","多源遥感 机器学习 墒情","冬小麦土壤含水量遥感-5558","10.3390\u002Fagronomy16202071",{"doi":169,"openalex_id":171,"authors":172,"venue":101,"cited_by_count":82,"oa_url":98,"card":182,"direction":187,"ingested_from":90},"W7223156204",[173,176,178,180],{"name":174,"orcid":175},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":177,"orcid":9},"Yi Zhang",{"name":179,"orcid":9},"WEI Lei",{"name":181,"orcid":9},"Chengcai Zhang",{"tldr":183,"method":184,"finding":185,"direction":88,"opportunity":186},"融合WCM物理特征与多源遥感，用机器学习估算冬小麦拔节期土壤含水量。","无人机多光谱与SAR数据，WCM参数+光谱指数，BP\u002FSVR\u002FRF建模。","RF最优，R²达0.855，RMSE为0.0253 cm³\u002Fcm³，特征方案影响显著。","可探索物理约束与深度学习耦合，并迁移至多生育期、多作物及不同土壤质地验证。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:18.887301Z",{"id":190,"title":191,"url":192,"summary":193,"summary_zh":194,"content":9,"source_name":195,"source_url":192,"published_at":196,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":103,"score_detail":197,"sources":201,"tags":203,"primary_topic":206,"secondary_topics":207,"classification":212,"search_phrases":231,"slug":234,"view_count":82,"doi":235,"paper":236,"created_at":259},5367,"Integration of multi-source UAV remote sensing features and derived features for winter wheat aboveground biomass estimation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102656","Accurate estimation of winter wheat ( Triticum aestivum L.) aboveground biomass (AGB) is important for crop growth monitoring and field management. However, unmanned aerial vehicle (UAV)-based AGB estimation remains challenging due to variations in canopy conditions across growth stages, and different remote sensing features can capture complementary characteristics related to AGB estimation. This study integrated multi-source UAV-derived features, including vegetation indices (VIs), texture features (TFs), light detection and ranging (LiDAR)-derived features, and derived features (DFs) generated from the original feature sets, with four machine learning algorithms for winter wheat AGB estimation. Results showed that TFs provided better prediction performance than other single-source feature sets. Compared with single-source feature sets, the integration of VIs, TFs, LiDAR-derived features, and DFs further improved AGB estimation, and the random forest (RF) model achieved the best performance among the evaluated models (R² = 0.923, RMSE = 1.731 Mg ha⁻¹). Incremental R² analysis based on a stage-only baseline model showed that the RF model explained additional variation in AGB beyond growth-stage information, with an incremental R² of 0.479. Stage-specific evaluation indicated that model performance varied among growth stages, with the highest accuracy observed at the booting stage (R² = 0.632, RMSE = 1.425 Mg ha⁻¹). Feature contribution analysis based on the full-feature fusion model using all growth-stage data identified canopy coverage and several DFs as important predictors, while stage-specific analyses showed differences in dominant predictors across growth stages. The integration of multi-source UAV-derived features and DFs with machine learning provides an effective approach for winter wheat AGB estimation, while model performance remains strongly dependent on growth stage.","准确估算冬小麦（Triticum aestivum L.）地上生物量（aboveground biomass, AGB）对作物生长监测和田间管理具有重要意义。然而，由于不同生育期冠层条件存在差异，且不同遥感特征能够捕捉与AGB估算相关的互补信息，基于无人机（unmanned aerial vehicle, UAV）的AGB估算仍面临挑战。本研究将多源无人机衍生特征，包括植被指数（vegetation indices, VIs）、纹理特征（texture features, TFs）、激光雷达（light detection and ranging, LiDAR）衍生特征以及由原始特征集生成的衍生特征（derived features, DFs），与四种机器学习算法相结合，用于冬小麦AGB估算。结果表明，TFs的预测性能优于其他单一来源特征集。与单一来源特征集相比，融合VIs、TFs、LiDAR衍生特征和DFs进一步提高了AGB估算精度，其中随机森林（random forest, RF）模型在所评估的模型中表现最佳（R² = 0.923，RMSE = 1.731 Mg ha⁻¹）。基于仅含生育期信息的基线模型进行的增量R²分析表明，RF模型在生育期信息之外解释了AGB的额外变异，增量R²为0.479。分生育期评估表明，模型性能在不同生育期之间存在差异，其中在孕穗期观察到最高精度（R² = 0.632，RMSE = 1.425 Mg ha⁻¹）。基于使用全部生育期数据的全特征融合模型进行的特征贡献分析识别出冠层覆盖度和若干DFs为重要预测因子，而分生育期分析则表明不同生育期的主导预测因子存在差异。将多源无人机衍生特征和DFs与机器学习相结合，为冬小麦AGB估算提供了一种有效方法，但模型性能仍强烈依赖于生育期。","Smart Agricultural Technology","2026-10-17T00:00:00Z",{"impact":105,"substance":198,"depth":199,"authority":106,"freshness":20,"relevant":21,"comment":200},22,18,"该研究融合无人机多源遥感特征与衍生特征，用随机森林估算冬小麦地上生物量，方法新颖、结论可靠，对作物长势监测有参考价值。",[202],{"name":195,"url":192},[26,204,27,29,205],"无人机","生物量估算",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[208],{"code":58,"slug":209,"name":210,"path":211,"parent_code":126},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":39,"locked":14,"version":40,"method":41,"evidence":213},{"candidates":214,"agricultural_evidence":46},[215,219,223,227],{"code":32,"score":131,"supported":46,"title":216,"abstract":217,"tags":218},[133,33],[34,133,33],[29],{"code":58,"score":105,"supported":46,"title":220,"abstract":221,"tags":222},[151],[150,151],[205,27],{"code":122,"score":93,"supported":46,"title":224,"abstract":225,"tags":226},[],[111,144],[],{"code":64,"score":21,"supported":14,"title":228,"abstract":229,"tags":230},[],[],[26],[232,233],"无人机 多源遥感 冬小麦 生物量","冬小麦 地上生物量 随机森林","无人机多源遥感冬小麦生物量-5367","10.1016\u002Fj.atech.2026.102656",{"doi":235,"openalex_id":237,"authors":238,"venue":195,"cited_by_count":82,"oa_url":192,"card":254,"direction":88,"ingested_from":90},"W7222806066",[239,241,243,245,248,250,252],{"name":240,"orcid":9},"Guangtao Wang",{"name":242,"orcid":9},"Sumei Yao",{"name":244,"orcid":9},"Yanzheng Xie",{"name":246,"orcid":247},"Anzhen Qin","https:\u002F\u002Forcid.org\u002F0000-0002-2550-0015",{"name":249,"orcid":9},"Xiangdong Chen",{"name":251,"orcid":9},"Yuhao Zhang",{"name":253,"orcid":9},"Lina Jiang",{"tldr":255,"method":256,"finding":257,"direction":88,"opportunity":258},"融合无人机多源遥感特征与衍生特征，用机器学习估算冬小麦地上生物量。","无人机植被指数、纹理、LiDAR特征及衍生特征结合四种机器学习算法建模。","多源特征融合优于单一特征，随机森林最优（R²=0.923），精度随生育期变化。","可探索生育期自适应建模与特征选择，提升跨期生物量估算的稳健性与可迁移性。","2026-10-20T23:30:03.106267Z",{"id":261,"title":262,"url":263,"summary":264,"summary_zh":9,"content":9,"source_name":265,"source_url":9,"published_at":266,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":267,"score_detail":268,"sources":270,"tags":272,"primary_topic":276,"secondary_topics":277,"classification":278,"search_phrases":303,"slug":306,"view_count":20,"doi":9,"paper":307,"created_at":316},3325,"MBF-HybridNet：在极端气候下仍可提前一月预测冬小麦产量的多分支AI模型——Qingdao六县R² 0.756-0.765 MAPE 4.2%","https:\u002F\u002Fbioengineer.org\u002Fnew-multi-branch-ai-model-predicts-winter-wheat-yields-weeks-before-harvest-even-under-extreme-weather\u002F","MBF-HybridNet由青岛六县研究团队开发和测试：采用多分支并行架构，包括处理日常遥感和气象数据的动态变量模块、处理年度尺度极端气候指数数据的动态ECI模块以及处理土壤属性的静态变量模块。动态模块堆叠三个二维卷积层，插入自注意力机制；静态模块独立处理土壤有机碳、阳离子交换容量、pH、砂和粘土含量。研究团队计算了九个极端气候指数（热日、热应力强度、连续热日、霜日、冷应力强度、连续冷日、强降水日、连续湿日和连续干日）用于每个生长阶段。2004至2019年留一年交叉验证，MBF-HybridNet在三个累积生长阶段的R²达到0.756-0.765，平均绝对百分比误差约为4.2%。","MBF-HybridNet 2026 (Qingdao)","2026-09-17T00:00:00Z",78,{"impact":199,"substance":198,"depth":199,"authority":17,"freshness":20,"relevant":21,"comment":269},"多分支AI融合遥感气象与极端气候指数，提前一月预测冬小麦产量且精度可靠，方法新颖、数据扎实，对农业信息化与智慧农业有较高参考价值。",[271],{"name":265,"url":263},[26,273,274,27,29,275],"农业人工智能","产量预测","极端气候",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":279},{"candidates":280,"agricultural_evidence":46},[281,286,291,295,299],{"code":32,"score":282,"supported":46,"title":283,"abstract":284,"tags":285},9,[34],[],[29],{"code":58,"score":287,"supported":46,"title":288,"abstract":289,"tags":290},5,[],[150],[274,27],{"code":116,"score":52,"supported":46,"title":292,"abstract":293,"tags":294},[],[140],[],{"code":64,"score":21,"supported":14,"title":296,"abstract":297,"tags":298},[],[],[26],{"code":122,"score":21,"supported":14,"title":300,"abstract":301,"tags":302},[],[],[273],[304,305],"MBF-HybridNet 冬小麦 产量预测","青岛 冬小麦 遥感 极端气候","MBF-HybridNet冬小麦产量预测-3325",{"doi":9,"openalex_id":9,"authors":308,"venue":9,"cited_by_count":82,"oa_url":9,"card":309,"direction":313,"ingested_from":315},[],{"tldr":310,"method":311,"finding":312,"direction":313,"opportunity":314},"提出多分支AI模型MBF-HybridNet，融合遥感、气象与土壤数据，提前一月预测冬小麦产量。","多分支并行架构，含2D卷积、自注意力与极端气候指数，2004-2019年留一年交","在青岛六县三个累积生长阶段R²达0.756-0.765，MAPE约4.2%，极端气候下仍可提前一月预","农业人工智能与决策模型","可探索极端气候指数与深度学习结合在其他作物或区域的泛化能力，并提升可解释性。","agent","2026-09-24T00:04:02.748442Z",{"id":318,"title":319,"url":320,"summary":321,"summary_zh":322,"content":9,"source_name":101,"source_url":320,"published_at":323,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":324,"score_detail":325,"sources":328,"tags":330,"primary_topic":334,"secondary_topics":335,"classification":338,"search_phrases":363,"slug":366,"view_count":287,"doi":367,"paper":368,"created_at":385},4433,"PROSAIL-DNN-TL: A Hyperspectral Inversion Framework for Winter Wheat Leaf Chlorophyll Content Based on Fine-Tuned Transfer Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16191971","Accurate and efficient estimation of leaf chlorophyll content (LCC) is essential for crop physiological monitoring and precision agriculture. Current remote sensing approaches for LCC estimation mainly rely on machine learning and deep learning methods, which exhibit strong nonlinear modelling capabilities but require sufficient and representative samples. Radiative transfer models (RTMs) can simulate canopy spectral response based on physical mechanisms and generate large-scale datasets, thereby overcoming the limitations of purely data-driven approaches. However, their applications under diverse environmental conditions remain challenging due to uncertainties caused by model parameterization and discrepancies between simulated and observed data. This study proposed a hybrid framework, PROSAIL-DNN-TL, integrating the PROSAIL RTM with full-network fine-tuned transfer learning for winter wheat LCC estimation. First, 85,680 simulated samples generated by PROSAIL were used to pre-train a deep neural network (DNN), and field observations (n = 270) were subsequently used for full-network fine-tuning. A conventional DNN model trained only with field data and machine learning models based on different feature selection strategies were established for comparison. The results showed that: (1) UVE-selected bands were distributed across visible, red-edge, and near-infrared regions; CARS retained more bands in the visible region, whereas SPA mainly selected near-infrared bands; (2) RR and SVR achieved higher accuracy than KNN, with the full-spectrum and CARS feature sets showing superior performance and SPA showing the lowest accuracy; (3) PROSAIL-DNN-TL achieved reliable LCC estimation performance, with an average test R2 of 0.79 across 500 repeated experiments, and showed improved performance compared with conventional machine learning models; (4) PROSAIL-DNN-TL achieved reliable predictive performance when evaluated using datasets collected during the 2024 and 2025 growing seasons, with R2 values of 0.75 and 0.92, respectively. Overall, the proposed hybrid framework integrates physical knowledge from RTMs with data-driven modelling through transfer learning, providing a potential approach for crop chlorophyll monitoring. However, the SPAD-to-LCC conversion used in this study was not specifically calibrated for the instrument and crop, and therefore, the absolute LCC values should be interpreted with caution.","准确、高效地估算叶片叶绿素含量（leaf chlorophyll content，LCC）对作物生理监测和精准农业至关重要。当前遥感估算LCC的方法主要依赖机器学习和深度学习方法，这类方法具有较强的非线性建模能力，但需要充足且具有代表性的样本。辐射传输模型（radiative transfer models，RTMs）能够基于物理机制模拟冠层光谱响应并生成大规模数据集，从而克服纯数据驱动方法的局限性。然而，由于模型参数化带来的不确定性以及模拟数据与观测数据之间的差异，其在不同环境条件下的应用仍面临挑战。本研究提出了一种混合框架PROSAIL-DNN-TL，将PROSAIL辐射传输模型与全网络微调迁移学习相结合，用于冬小麦LCC估算。首先，利用PROSAIL生成的85 680个模拟样本预训练深度神经网络（deep neural network，DNN），随后利用田间观测数据（n = 270）进行全网络微调。同时建立了仅使用田间数据训练的传统DNN模型以及基于不同特征选择策略的机器学习模型作为对比。结果表明：（1）UVE选择的波段分布在可见光、红边和近红外区域；CARS在可见光区域保留了更多波段，而SPA主要选择近红外波段；（2）RR和SVR的精度高于KNN，全光谱和CARS特征集表现较优，SPA精度最低；（3）PROSAIL-DNN-TL取得了可靠的LCC估算性能，在500次重复实验中平均测试R²为0.79，且相比传统机器学习模型表现出更优的性能；（4）利用2024年和2025年生长季采集的数据集进行评估时，PROSAIL-DNN-TL均表现出可靠的预测性能，R²分别为0.75和0.92。总体而言，所提出的混合框架通过迁移学习将辐射传输模型的物理知识与数据驱动建模相结合，为作物叶绿素监测提供了一种潜在途径。然而，本研究中使用的SPAD到LCC的转换并未针对仪器和作物进行专门校准，因此绝对LCC值应谨慎解读。","2026-10-07T00:00:00Z",81,{"impact":199,"substance":198,"depth":199,"authority":326,"freshness":282,"relevant":21,"comment":327},14,"将PROSAIL辐射传输模型与深度神经网络迁移学习结合，用8万余模拟样本预训练、270个田间样本微调，冬小麦叶绿素反演精度可靠，方法新颖且数据规模扎实，对作物生理监测与精准农业有参考价值。",[329],{"name":101,"url":320},[26,331,29,332,333],"农业遥感","迁移学习","叶绿素监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[336,337],{"code":58,"slug":209,"name":210,"path":211,"parent_code":126},{"code":122,"slug":123,"name":124,"path":125,"parent_code":126},{"status":39,"locked":14,"version":40,"method":41,"evidence":339},{"candidates":340,"agricultural_evidence":46},[341,345,350,356],{"code":32,"score":131,"supported":46,"title":342,"abstract":343,"tags":344},[133,33],[34,133,33],[29],{"code":58,"score":137,"supported":46,"title":346,"abstract":348,"tags":349},[347],"hyperspectral",[150,151],[331],{"code":122,"score":13,"supported":46,"title":351,"abstract":352,"tags":355},[],[353,111,144,354],"深度学习","deep learning",[332],{"code":64,"score":357,"supported":46,"title":358,"abstract":359,"tags":362},7,[],[360,361],"精准农业","precision agriculture",[26],[364,365],"PROSAIL 冬小麦 叶绿素","高光谱 叶绿素 反演","PROSAIL冬小麦叶绿素-4433","10.3390\u002Fagronomy16191971",{"doi":367,"openalex_id":369,"authors":370,"venue":101,"cited_by_count":82,"oa_url":320,"card":380,"direction":88,"ingested_from":90},"W7220844556",[371,373,375,378],{"name":372,"orcid":9},"Xueqing Zhu",{"name":374,"orcid":9},"Ying Nian",{"name":376,"orcid":377},"Jikai Liu","https:\u002F\u002Forcid.org\u002F0000-0003-2365-5221",{"name":379,"orcid":9},"Xinwei Li",{"tldr":381,"method":382,"finding":383,"direction":88,"opportunity":384},"提出PROSAIL-DNN-TL框架，用PROSAIL模拟数据预训练DNN并全网络微调，反演冬小麦叶","PROSAIL辐射传输模型生成85680样本预训练DNN，270个田间样本全网络","500次重复实验平均测试R²为0.79，2024和2025年生长季验证R²分别为0.75和0.92，","SPAD到LCC转换未针对仪器和作物标定，可研究跨传感器、跨作物、跨生长季的迁移学习泛化与绝对叶绿素标定。","2026-10-08T23:30:29.383957Z",{"id":387,"title":388,"url":389,"summary":390,"summary_zh":391,"content":9,"source_name":392,"source_url":389,"published_at":393,"category":12,"cover_url":9,"hotness":13,"is_selected":46,"score":394,"score_detail":395,"sources":398,"tags":400,"primary_topic":404,"secondary_topics":405,"classification":407,"search_phrases":436,"slug":439,"view_count":20,"doi":440,"paper":441,"created_at":471},4087,"Improving regional winter wheat yield estimation by assimilating cross-scale heterogeneity information from UAV and satellite remote sensing","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rse.2026.115701","Accurate high-resolution winter wheat yield estimation remains challenging in heterogeneous agricultural systems. Conventional yield estimation approaches remain limited by high uncertainty (process-based crop models) or weak transferability due to insufficient physical knowledge and scarce sample sizes (statistical models based on remote sensing data). Data assimilation offers a promising approach to overcome these limitations, but its performance is constrained by the neglect of heterogeneous information within assimilation grids, particularly in low-intensity agricultural systems. Therefore, we developed a novel cross-scale data assimilation framework to enhance winter wheat yield estimation by integrating unmanned aerial vehicle (UAV) and satellite remote sensing with the WOFOST crop model. First, UAV-derived crop-soil parameters, including leaf area index (LAI), aboveground biomass (AGB), and soil moisture at 10 and 20 cm depths (SMC-10 and SMC-20), served as a bridge linking in situ sampling data with Sentinel satellite images. Next, a mixture density network (MDN) was employed to construct an upscaling model for predicting probability distributions, thereby obtaining the distribution of crop-soil parameters within each satellite pixel as field heterogeneity information. Finally, these heterogeneity distributions were incorporated into WOFOST through an improved particle filter to generate high-resolution yield (10 m × 10 m) estimates at the regional scale. The results showed that robust crop-soil parameter inversion models were constructed using random forest with multispectral and thermal UAV images (R 2 : 0.73–0.88; NRMSE: 12.30%–22.83%). The MDN quantified within-pixel heterogeneity of crop-soil parameters through a UAV-satellite upscaling approach. The joint assimilation of LAI, AGB, and SMC-10 achieved optimal winter wheat yield estimation performance for MDN-based cross-scale data assimilation (R 2 = 0.83, NRMSE = 15.70% in 2024–2025), outperforming satellite-based and mean-based cross-scale assimilation. The proposed framework showed stable interannual performance in Xianyang (RMSE ≤758 kg ha −1 , NRMSE ≤17.20% in 2017–2023) and reasonable transferability in two external winter wheat regions (RMSE ≤943 kg ha −1 ; NRMSE ≤18.29% in Baoding and Changji). Overall, the cross-scale data assimilation framework exhibited strong performance in wheat yield estimation and would provide scientific support for intensive agricultural development.","在异质性农业系统中，实现准确的高分辨率冬小麦产量估算仍具挑战性。传统产量估算方法受限于较高不确定性（基于过程的作物模型）或可迁移性较弱（基于遥感数据的统计模型），后者源于物理知识不足和样本量稀缺。数据同化为克服这些局限提供了一种有前景的途径，但其性能受到同化网格内异质性信息被忽略的制约，尤其是在低强度农业系统中。因此，我们开发了一种新的跨尺度数据同化框架，通过将无人机（UAV）和卫星遥感与WOFOST作物模型相结合，提升冬小麦产量估算。首先，无人机获取的作物-土壤参数，包括叶面积指数（LAI）、地上生物量（AGB）以及10和20 cm深度土壤水分（SMC-10和SMC-20），作为连接原位采样数据与Sentinel卫星影像的桥梁。接下来，采用混合密度网络（MDN）构建升尺度模型以预测概率分布，从而获得每个卫星像元内作物-土壤参数的分布，作为田块异质性信息。最后，通过改进的粒子滤波器将这些异质性分布纳入WOFOST，以生成区域尺度的高分辨率产量（10 m × 10 m）估算。结果表明，利用随机森林结合多光谱和热红外无人机影像构建了稳健的作物-土壤参数反演模型（R²：0.73–0.88；NRMSE：12.30%–22.83%）。MDN通过无人机-卫星升尺度方法量化了作物-土壤参数的像元内异质性。LAI、AGB和SMC-10的联合同化使基于MDN的跨尺度数据同化取得了最优的冬小麦产量估算性能（2024–2025年R² = 0.83，NRMSE = 15.70%），优于基于卫星和基于均值的跨尺度同化。所提出的框架在咸阳表现出稳定的年际性能（2017–2023年RMSE ≤758 kg ha⁻¹，NRMSE ≤17.20%），并在两个外部冬小麦区域具有合理的可迁移性（保定和昌吉RMSE ≤943 kg ha⁻¹；NRMSE ≤18.29%）。总体而言，该跨尺度数据同化框架在小麦产量估算中表现出较强性能，可为集约化农业发展提供科学支持。","Remote Sensing of Environment","2026-10-01T00:00:00Z",83,{"impact":199,"substance":396,"depth":199,"authority":137,"freshness":282,"relevant":21,"comment":397},23,"该研究提出跨尺度数据同化框架，融合无人机与卫星遥感提升区域冬小麦估产精度，方法新颖、验证充分，对精准农业与农业遥感应用具有较高参考价值。",[399],{"name":392,"url":389},[26,401,27,402,403],"作物模型","数据同化","小麦产量",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[406],{"code":58,"slug":209,"name":210,"path":211,"parent_code":126},{"status":39,"locked":14,"version":40,"method":41,"evidence":408},{"candidates":409,"agricultural_evidence":46},[410,415,419,423,427,431],{"code":32,"score":411,"supported":46,"title":412,"abstract":413,"tags":414},25,[133,33],[34,133,33],[],{"code":58,"score":199,"supported":46,"title":416,"abstract":417,"tags":418},[151],[150,151,152],[27],{"code":116,"score":93,"supported":46,"title":420,"abstract":421,"tags":422},[],[140,117],[],{"code":64,"score":21,"supported":14,"title":424,"abstract":425,"tags":426},[],[],[26],{"code":122,"score":21,"supported":14,"title":428,"abstract":429,"tags":430},[],[],[401],{"code":432,"score":21,"supported":14,"title":433,"abstract":434,"tags":435},"I5",[],[],[402],[437,438],"无人机 卫星 遥感 冬小麦 估产","WOFOST 数据同化 小麦","无人机卫星遥感冬小麦估产-4087","10.1016\u002Fj.rse.2026.115701",{"doi":440,"openalex_id":442,"authors":443,"venue":392,"cited_by_count":82,"oa_url":389,"card":466,"direction":88,"ingested_from":90},"W7215051418",[444,446,448,450,453,455,458,460,462,464],{"name":445,"orcid":9},"Zhikai Cheng",{"name":447,"orcid":9},"Xiaobo Gu",{"name":449,"orcid":9},"Yang Xu",{"name":451,"orcid":452},"Xiaohai Fang","https:\u002F\u002Forcid.org\u002F0000-0003-1322-1366",{"name":454,"orcid":9},"Zhengtao Zhang",{"name":456,"orcid":457},"Penglin Li","https:\u002F\u002Forcid.org\u002F0009-0005-2706-1326",{"name":459,"orcid":9},"Yupeng Li",{"name":461,"orcid":9},"Shikun Sun",{"name":463,"orcid":9},"Yadan Du",{"name":465,"orcid":9},"Huanjie Cai",{"tldr":467,"method":468,"finding":469,"direction":88,"opportunity":470},"构建跨尺度数据同化框架，融合无人机与卫星遥感提升区域冬小麦产量估算精度。","UAV-Sentinel数据、混合密度网络升尺度、改进粒子滤波同化WOFOST模","联合同化LAI、AGB与SMC-10最优，R²=0.83，跨年与跨区表现稳定。","可探索异质农田下多源遥感与作物模型同化的普适性及低成本无人机采样优化。","2026-10-03T23:30:33.709814Z",{"id":473,"title":474,"url":475,"summary":476,"summary_zh":477,"content":9,"source_name":478,"source_url":475,"published_at":479,"category":12,"cover_url":9,"hotness":13,"is_selected":46,"score":480,"score_detail":481,"sources":483,"tags":485,"primary_topic":490,"secondary_topics":491,"classification":498,"search_phrases":547,"slug":550,"view_count":59,"doi":551,"paper":552,"created_at":593},4083,"Wheat Rust Diseases: Global Impact, Integrated Disease Management, and Emerging Biotechnological Innovations for Sustainable Crop Protection","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fbiology15191756","Wheat (Triticum aestivum L.) remains one of the most important staple crops worldwide and serves as a primary source of calories and protein for billions of people. Global wheat production, however, continues to be threatened by rust diseases caused by Puccinia triticina, Puccinia striiformis f. sp. tritici and Puccinia graminis f. sp. tritici. These pathogens are highly adaptive and frequently give rise to new virulent races, causing resistance in wheat cultivars to breakdown. Under favorable environmental conditions, severe epidemics can develop and substantial yield losses may occur. In response to these challenges, traditional management approaches such as resistant cultivars, crop rotation, sanitation and fungicide application have long been employed in wheat production systems. Yet these strategies have increasingly been constrained by resistance breakdown, genetic uniformity in modern cultivars, and climate-driven changes in pathogen distribution. Conventional management practices are evaluated alongside emerging technological solutions including genomic-assisted breeding, gene pyramiding, CRISPR-based genome editing, biological control agents and precision agriculture tools. In addition, nanotechnology has emerged as a promising tool in plant disease management, where nanoparticles such as silver, copper and zinc oxide exhibit strong antifungal activity and enable targeted delivery of agrochemicals. Nano-enabled formulations can enhance fungicide efficiency, reduce chemical inputs and improve plant defense responses, offering a sustainable complement to existing wheat rust management strategies. Particular attention is given to integrated disease management strategies, which combine traditional agronomic practices with modern surveillance technologies such as remote sensing, artificial intelligence-based forecasting models, and pathogen genomics. These combined approaches can improve resistance durability and build more resilient wheat production systems. Future directions emphasize climate adaptation, global surveillance and collaborative breeding. In an era of increasing climatic uncertainty and evolving pathogen populations, durable rust management will depend on coordinated global efforts that align scientific innovation, farmer adoption, and policy support to safeguard wheat productivity and global food security.","小麦（Triticum aestivum L.）仍是全球最重要的主粮作物之一，是数十亿人热量和蛋白质的主要来源。然而，全球小麦生产持续受到由小麦叶锈菌（Puccinia triticina）、小麦条锈菌（Puccinia striiformis f. sp. tritici）和小麦秆锈菌（Puccinia graminis f. sp. tritici）引起的锈病威胁。这些病原菌适应性极强，频繁产生新的毒性小种，导致小麦品种抗性丧失。在有利的环境条件下，病害可严重流行并造成大幅产量损失。为应对这些挑战，小麦生产体系中长期采用抗病品种、轮作、田园卫生和杀菌剂施用等传统管理措施。然而，这些策略日益受到抗性丧失、现代品种遗传单一化以及气候驱动的病原菌分布变化的制约。本文在评估传统管理实践的同时，也探讨了新兴技术解决方案，包括基因组辅助育种、基因聚合、基于CRISPR的基因组编辑、生物防治制剂和精准农业工具。此外，纳米技术已成为植物病害管理中有前景的工具，其中银、铜和氧化锌等纳米颗粒表现出强抗真菌活性，并能够实现农化品的靶向递送。纳米制剂可提高杀菌剂效率、减少化学投入并改善植物防御反应，为现有小麦锈病管理策略提供了可持续的补充。本文特别关注病害综合管理策略，即将传统农艺措施与遥感、基于人工智能的预测模型和病原菌基因组学等现代监测技术相结合。这些综合方法可提高抗性持久性并构建更具韧性的小麦生产体系。未来方向强调气候适应、全球监测和协作育种。在气候不确定性日益加剧和病原菌群体不断演化的时代，持久的锈病管理将依赖于协调一致的全球努力，将科学创新、农民采纳和政策支持相结合，以保障小麦生产和全球粮食安全。","Biology","2026-10-02T00:00:00Z",82,{"impact":198,"substance":18,"depth":199,"authority":106,"freshness":20,"relevant":21,"comment":482},"系统综述小麦锈病全球影响与基因组育种、纳米农药、遥感与AI预测等前沿防控手段，专业性强，对保障粮食安全具参考价值。",[484],{"name":478,"url":475},[26,486,487,488,27,489],"粮食安全","基因编辑","纳米农药","小麦锈病",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[492],{"code":493,"slug":494,"name":495,"path":496,"parent_code":497},"F4","crop-protection","农药与绿色防控","\u002Ftopic\u002Finputs-equipment\u002Fcrop-protection","F",{"status":39,"locked":14,"version":40,"method":41,"evidence":499},{"candidates":500,"agricultural_evidence":46},[501,505,511,517,521,525,532,537,542],{"code":32,"score":326,"supported":46,"title":502,"abstract":503,"tags":504},[33],[34,33],[],{"code":493,"score":17,"supported":46,"title":506,"abstract":508,"tags":510},[507],"crop protection",[509],"生物防治",[488],{"code":512,"score":357,"supported":46,"title":513,"abstract":514,"tags":516},"A8",[],[486,515],"food security",[486],{"code":64,"score":357,"supported":46,"title":518,"abstract":519,"tags":520},[],[360,361],[26],{"code":58,"score":357,"supported":46,"title":522,"abstract":523,"tags":524},[],[150,151],[27],{"code":526,"score":93,"supported":46,"title":527,"abstract":528,"tags":531},"A7",[],[529,530],"轮作","crop rotation",[],{"code":51,"score":52,"supported":46,"title":533,"abstract":534,"tags":536},[],[535],"气候适应",[],{"code":122,"score":52,"supported":46,"title":538,"abstract":539,"tags":541},[],[540],"artificial intelligence",[],{"code":543,"score":21,"supported":14,"title":544,"abstract":545,"tags":546},"F2",[],[],[487],[548,549],"小麦锈病 综合防控","Puccinia triticina 抗性育种","小麦锈病综合防控-4083","10.3390\u002Fbiology15191756",{"doi":551,"openalex_id":553,"authors":554,"venue":478,"cited_by_count":82,"oa_url":475,"card":588,"direction":88,"ingested_from":90},"W7215995869",[555,558,561,564,567,570,572,574,576,579,582,585],{"name":556,"orcid":557},"Mingzheng Duan","https:\u002F\u002Forcid.org\u002F0000-0002-3486-024X",{"name":559,"orcid":560},"Muhammad Asif Shabbir","https:\u002F\u002Forcid.org\u002F0009-0002-5452-5040",{"name":562,"orcid":563},"Mustansar Mubeen","https:\u002F\u002Forcid.org\u002F0009-0003-9231-9560",{"name":565,"orcid":566},"Yasir Iftikhar","https:\u002F\u002Forcid.org\u002F0000-0002-4182-8969",{"name":568,"orcid":569},"Naureen Anwar","https:\u002F\u002Forcid.org\u002F0000-0003-1254-1720",{"name":571,"orcid":9},"Shiping Jiang",{"name":573,"orcid":9},"Zhifang He",{"name":575,"orcid":9},"Sirui Han",{"name":577,"orcid":578},"Ahmed Mahmoud Ismail","https:\u002F\u002Forcid.org\u002F0000-0001-9679-640X",{"name":580,"orcid":581},"Khalid Mushabab Al Syaad","https:\u002F\u002Forcid.org\u002F0000-0002-2372-9975",{"name":583,"orcid":584},"Ahmed Ezzat Ahmed","https:\u002F\u002Forcid.org\u002F0000-0002-4532-0636",{"name":586,"orcid":587},"Muhammad Umer","https:\u002F\u002Forcid.org\u002F0000-0002-3957-0196",{"tldr":589,"method":590,"finding":591,"direction":313,"opportunity":592},"综述小麦锈病全球影响与综合防控，并展望基因编辑、纳米技术、遥感与AI预测等新兴手段。","文献综述，整合抗病育种、CRISPR、纳米制剂、遥感与AI预测模型。","综合传统农艺与现代监测技术可提升抗性持久性，纳米与基因编辑是可持续补充。","可探索遥感与AI预测模型同病原基因组数据融合的实时预警系统，并评估纳米制剂田间长期生态效应。","2026-10-03T23:30:32.282761Z"]