[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1996":3,"related-1996":117},{"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":49,"search_phrases":91,"slug":94,"view_count":95,"doi":96,"paper":97,"created_at":116},1996,"An Interpretable BO-TCBDA Deep Learning Framework for Winter Wheat Yield Estimation Using Multi-Source Remote Sensing Data","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183061","Reliable crop yield estimation is fundamental to food security and efficient agricultural management. However, current deep learning models still face limitations in selecting and integrating multi-source features, and their high predictive accuracy is often accompanied by limited interpretability. This study introduces a Bayesian Optimization–Temporal Convolutional Network–Bidirectional Long Short-Term Memory–Dual Attention (BO-TCBDA) deep learning framework for winter wheat yield estimation. Using Henan Province, China, as the study area, county-level winter wheat yield from 2013 to 2022 was estimated using the Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), Solar-Induced Chlorophyll Fluorescence (SIF), and climate data. The proposed model was compared with five commonly used machine learning and deep learning models. BO-TCBDA achieved the best performance, with an R2 of 0.823 and an RMSE of 561.26 kg\u002Fha. SIF improved the predictive performance of all models, with statistically significant gains observed in the deep learning models. The dual-attention mechanism provided interpretable insights by revealing relatively balanced contributions among the input features and highlighting the grain-filling stage through temporal attention. Furthermore, SHAP-based cross-validation analysis identified T12, corresponding to the latter part of the jointing stage, as the period with the highest contribution to yield prediction. The model also achieved an R2 of approximately 0.80 about 25 days before harvest. Overall, BO-TCBDA provides an accurate and interpretable approach for county-level winter wheat yield estimation and supports regional food security assessments and precision agriculture.","可靠的作物产量估算对于粮食安全和高效农业管理至关重要。然而，当前的深度学习模型在多源特征的选择与整合方面仍存在局限，且高预测精度往往伴随着有限的可解释性。本研究提出了一种基于贝叶斯优化–时间卷积网络–双向长短期记忆–双重注意力（BO-TCBDA）的深度学习框架，用于冬小麦产量估算。以中国河南省为研究区域，利用增强型植被指数（EVI）、叶面积指数（LAI）、太阳诱导叶绿素荧光（SIF）及气候数据，对2013至2022年县级冬小麦产量进行了估算。将所提模型与五种常用的机器学习和深度学习模型进行了比较。BO-TCBDA取得了最佳性能，其决定系数（R²）为0.823，均方根误差（RMSE）为561.26千克\u002F公顷。SIF提升了所有模型的预测性能，其中在深度学习模型中观察到了统计学上显著的增益。双重注意力机制通过揭示输入特征间相对均衡的贡献，并借助时间注意力突出灌浆期，提供了可解释性的见解。此外，基于SHAP的交叉验证分析识别出T12时段（对应拔节期后期）对产量预测的贡献最大。该模型在收获前约25天时，R²亦达到约0.80。总体而言，BO-TCBDA为县级冬小麦产量估算提供了一种准确且可解释的方法，并支持区域粮食安全评估和精准农业实践。",null,"Remote Sensing","2026-09-08T00:00:00Z","论文",10,false,73,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,2,1,"提出可解释的深度学习框架，结合多源遥感数据提升冬小麦估产精度与可解释性，对精准农业有实质贡献。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","遥感","产量估算","冬小麦",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[38,44],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"code":45,"slug":46,"name":47,"path":48,"parent_code":43},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":50,"locked":14,"version":51,"method":52,"evidence":53},"classified","2026-10-22.2","rules",{"candidates":54,"agricultural_evidence":57},[55,62,70,75,83],{"code":32,"score":56,"supported":57,"title":58,"abstract":60,"tags":61},26,true,[59,33],"winter wheat",[34,59,33],[30],{"code":39,"score":17,"supported":57,"title":63,"abstract":65,"tags":69},[64],"deep learning",[66,67,68,64],"深度学习","机器学习","machine learning",[27],{"code":45,"score":13,"supported":57,"title":71,"abstract":73,"tags":74},[72],"remote sensing",[],[29,28],{"code":76,"score":77,"supported":57,"title":78,"abstract":79,"tags":82},"I1",7,[],[80,81],"精准农业","precision agriculture",[26],{"code":84,"score":85,"supported":57,"title":86,"abstract":87,"tags":90},"A8",6,[],[88,89],"粮食安全","food security",[],[92,93],"农业人工智能 产量估算 智慧农业 冬小麦","农业人工智能 产量估算","农业人工智能产量估算智慧农业冬小麦-1996",3,"10.3390\u002Frs18183061",{"doi":96,"openalex_id":98,"authors":99,"venue":10,"cited_by_count":107,"oa_url":6,"card":108,"direction":114,"ingested_from":115},"W7211938917",[100,102,104],{"name":101,"orcid":9},"Anqi Xue",{"name":103,"orcid":9},"Shufang Tian",{"name":105,"orcid":106},"Tingyan Fu","https:\u002F\u002Forcid.org\u002F0000-0003-4207-4211",0,{"tldr":109,"method":110,"finding":111,"direction":112,"opportunity":113},"提出BO-TCBDA深度学习框架，融合多源遥感数据估算冬小麦产量，兼具高精度与可解释性。","贝叶斯优化、TCN、BiLSTM、双注意力机制，结合EVI、LAI、SIF及气候","BO-TCBDA性能最优（R²=0.823），SIF提升预测，拔节后期贡献最大，收获前25天可预测。","农业人工智能与决策模型","可探索将双注意力与SHAP结合用于其他作物或区域，或开发实时预警系统，提升模型泛化与实用性。","农业遥感与作物表型","openalex","2026-09-09T23:30:22.411579Z",{"total":85,"page":21,"page_size":85,"items":118},[119,182,257,321,386,467],{"id":120,"title":121,"url":122,"summary":123,"summary_zh":9,"content":9,"source_name":124,"source_url":9,"published_at":125,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":126,"score_detail":127,"sources":133,"tags":135,"primary_topic":138,"secondary_topics":139,"classification":142,"search_phrases":169,"slug":172,"view_count":20,"doi":9,"paper":173,"created_at":181},2854,"面向冬小麦水分含量的无人机遥感自动机器学习预测——MDPI Remote Sensing","https:\u002F\u002Fwww.mdpi.com\u002F2072-4292\u002F18\u002F18\u002F3161","本研究探索了无人机遥感快速准确评估冬小麦水分含量的潜力。在开花期和灌浆期使用配备多光谱、RGB和热红外相机的无人机获取高分辨率冠层遥感图像。集成地面真值采样数据与无人机遥感数据,使用自动机器学习(AutoML)框架建立回归模型预测冬小麦水分含量(MC)。结果表明,MC预测在灌浆期表现最佳,TIR传感器精度最高(R²=0.812,MAE=0.0204,RMSE=0.0274)。多传感器融合相比单传感器方法进一步提升预测性能,MC预测的R²达0.876、MAE 0.0191、RMSE 0.0259。来自中国农业科学院农田灌溉研究所。","MDPI Remote Sensing","2026-09-15T00:00:00Z",74,{"impact":128,"substance":129,"depth":130,"authority":19,"freshness":131,"relevant":21,"comment":132},15,21,17,8,"中国农科院团队用AutoML融合多光谱、RGB与热红外无人机数据预测冬小麦水分含量，多传感器融合R²达0.876，方法新颖、结论可靠，对精准灌溉有实用价值，值得进入每日精选。",[134],{"name":124,"url":122},[26,27,136,30,137],"无人机遥感","多传感器融合",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[140,141],{"code":45,"slug":46,"name":47,"path":48,"parent_code":43},{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":50,"locked":14,"version":51,"method":52,"evidence":143},{"candidates":144,"agricultural_evidence":57},[145,150,155,159,165],{"code":45,"score":146,"supported":57,"title":147,"abstract":148,"tags":149},20,[28,72],[28],[136],{"code":32,"score":151,"supported":57,"title":152,"abstract":153,"tags":154},12,[34],[34],[30],{"code":39,"score":151,"supported":57,"title":156,"abstract":157,"tags":158},[67],[67],[27],{"code":160,"score":95,"supported":57,"title":161,"abstract":162,"tags":164},"H3",[],[163],"灌溉",[],{"code":76,"score":21,"supported":14,"title":166,"abstract":167,"tags":168},[],[],[26],[170,171],"农业人工智能 多传感器融合 无人机遥感 智慧农业","农业人工智能 多传感器融合","农业人工智能多传感器融合无人机遥感智慧农业-2854",{"doi":9,"openalex_id":9,"authors":174,"venue":9,"cited_by_count":107,"oa_url":9,"card":175,"direction":114,"ingested_from":180},[],{"tldr":176,"method":177,"finding":178,"direction":114,"opportunity":179},"用无人机多传感器遥感结合AutoML预测冬小麦水分含量。","无人机多光谱、RGB、热红外图像+地面真值，AutoML回归建模。","灌浆期热红外精度最高，多传感器融合将R²提升至0.876。","可探索不同生育期与品种的泛化性，及将水分预测接入灌溉决策系统。","agent","2026-09-18T00:03:30.758419Z",{"id":183,"title":184,"url":185,"summary":186,"summary_zh":187,"content":9,"source_name":10,"source_url":185,"published_at":125,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":188,"score_detail":189,"sources":192,"tags":194,"primary_topic":196,"secondary_topics":197,"classification":200,"search_phrases":227,"slug":230,"view_count":95,"doi":231,"paper":232,"created_at":256},2666,"Automated Machine Learning-Driven UAV Remote Sensing for Accurate Winter Wheat Water Content Prediction","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183161","Crop water content is a critical indicator of crop growth status, and its efficient and accurate monitoring is essential for agricultural water resource management. Conventional methods for monitoring winter wheat water content, however, rely mainly on destructive sampling and are labor-intensive and time-consuming. To address these limitations, this study explored the potential of unmanned aerial vehicle (UAV) remote sensing for the rapid and accurate assessment of winter wheat water content. High-resolution canopy remote sensing images were acquired using UAVs equipped with multispectral (MS), RGB, and thermal infrared (TIR) cameras during the flowering and filling stages under six irrigation treatments. Ground-truth sampling data were integrated with the UAV-derived remote sensing data, and an automated machine learning (AutoML) framework—which automatically searches over a range of candidate algorithms and hyperparameters to select the optimal model—was employed to establish regression models for predicting winter wheat moisture content (MC). All models were evaluated using five-fold cross-validation. The results demonstrated that MC prediction performed best during the filling stage, with the TIR sensor achieving the highest accuracy (R2 = 0.812, MAE = 0.0204, RMSE = 0.0274). Compared with single-sensor approaches, multi-sensor fusion further improved predictive performance, achieving an R2 of 0.876, an MAE of 0.0191, and an RMSE of 0.0259 for MC prediction. These findings indicate that UAV-based multi-sensor remote sensing provides an effective means of monitoring winter wheat water content, facilitating timely assessment of crop growth status and optimized irrigation management. Moreover, the use of AutoML enables high-accuracy prediction with minimal human intervention, enhancing the precision of crop water monitoring and advancing precision agriculture.","作物含水量是反映作物生长状况的关键指标，对其进行高效、准确的监测对农业水资源管理至关重要。然而，传统冬小麦含水量监测方法主要依赖破坏性采样，费时费力。为解决这些局限，本研究探索了无人机（UAV）遥感在快速准确评估冬小麦含水量方面的潜力。在六种灌溉处理下，利用搭载多光谱（MS）、RGB和热红外（TIR）相机的无人机在开花期和灌浆期获取了高分辨率冠层遥感图像。将地面实测采样数据与无人机遥感数据相结合，采用自动化机器学习（AutoML）框架——该框架可在一系列候选算法和超参数中自动搜索以选择最优模型——建立预测冬小麦含水量（MC）的回归模型。所有模型均采用五折交叉验证进行评估。结果表明，灌浆期MC预测表现最佳，其中TIR传感器精度最高（R2 = 0.812，MAE = 0.0204，RMSE = 0.0274）。与单传感器方法相比，多传感器融合进一步提升了预测性能，MC预测的R2达到0.876，MAE为0.0191，RMSE为0.0259。这些发现表明，基于无人机的多传感器遥感为监测冬小麦含水量提供了有效手段，有助于及时评估作物生长状况并优化灌溉管理。此外，AutoML的使用使得在最少人工干预下实现高精度预测成为可能，提升了作物水分监测的精度，推动了精准农业发展。",80,{"impact":17,"substance":18,"depth":17,"authority":190,"freshness":131,"relevant":21,"comment":191},14,"AutoML结合无人机多传感器遥感预测冬小麦含水量，方法新颖、数据扎实，对精准灌溉有实用价值，值得进入每日精选。",[193],{"name":10,"url":185},[26,27,34,28,195],"精准灌溉",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[198,199],{"code":45,"slug":46,"name":47,"path":48,"parent_code":43},{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":50,"locked":14,"version":51,"method":52,"evidence":201},{"candidates":202,"agricultural_evidence":57},[203,207,212,216,223],{"code":32,"score":56,"supported":57,"title":204,"abstract":205,"tags":206},[59,33],[34,59,33],[34],{"code":45,"score":17,"supported":57,"title":208,"abstract":209,"tags":211},[72],[28,72,210],"multispectral",[28],{"code":39,"score":128,"supported":57,"title":213,"abstract":214,"tags":215},[68],[67,68],[27],{"code":160,"score":13,"supported":57,"title":217,"abstract":218,"tags":222},[],[163,219,220,221],"水资源","irrigation","water resource",[195],{"code":76,"score":77,"supported":57,"title":224,"abstract":225,"tags":226},[],[80,81],[26],[228,229],"农业人工智能 智慧农业 精准灌溉 小麦","农业人工智能 智慧农业","农业人工智能智慧农业精准灌溉小麦-2666","10.3390\u002Frs18183161",{"doi":231,"openalex_id":233,"authors":234,"venue":10,"cited_by_count":107,"oa_url":185,"card":251,"direction":114,"ingested_from":115},"W7213246708",[235,238,240,242,245,248],{"name":236,"orcid":237},"Fan Ding","https:\u002F\u002Forcid.org\u002F0000-0001-5482-8290",{"name":239,"orcid":9},"Qian Cheng",{"name":241,"orcid":9},"Fuyi Duan",{"name":243,"orcid":244},"Shuaipeng Fei","https:\u002F\u002Forcid.org\u002F0000-0002-8774-7929",{"name":246,"orcid":247},"Junjie Feng","https:\u002F\u002Forcid.org\u002F0000-0001-8900-2691",{"name":249,"orcid":250},"Zhen Chen","https:\u002F\u002Forcid.org\u002F0000-0002-2847-0042",{"tldr":252,"method":253,"finding":254,"direction":114,"opportunity":255},"用无人机多光谱、RGB和热红外遥感结合AutoML预测冬小麦含水量。","无人机多传感器影像与地面采样，AutoML自动选模型，五折交叉验证。","灌浆期热红外精度最高R²=0.812，多传感器融合提升至R²=0.876。","可探索AutoML与多时相\u002F多源卫星遥感融合，实现区域尺度作物水分精准监测。","2026-09-16T23:30:29.163192Z",{"id":258,"title":259,"url":260,"summary":261,"summary_zh":9,"content":9,"source_name":262,"source_url":9,"published_at":263,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":264,"score_detail":265,"sources":267,"tags":269,"primary_topic":271,"secondary_topics":272,"classification":275,"search_phrases":309,"slug":312,"view_count":95,"doi":9,"paper":313,"created_at":320},1705,"VGG21 Deep Learning + Drone Multispectral Imagery Detects Wheat Waterlogging Stress and Silicon Fertilizer Effects","https:\u002F\u002Fagritechinsights.com\u002Findex.php\u002F2026\u002F09\u002F04\u002Fdrones-ai-spot-wheat-waterlogging-stress-in-real-time","江苏省农科院农业信息研究所梁万杰团队发表于《Smart Agriculture》。研究展示了一种基于多光谱相机的无人机快速检测小麦涝害并评估硅肥或氨基酸是否有助于作物恢复的方法。研究使用 DJI P4 多光谱无人机，在五个光谱波段捕获冠层图像。VGG21 模型识别胁迫和处理效应的整体准确率超过 91%，识别硅肥处理小麦的精确度达到 96.42%。研究指出早期涝害症状微妙多变导致健康与涝害小麦常被混淆，未来工作将量化胁迫检测与实际产量增益的相关性。","Agritech Insights","2026-09-04T00:00:00Z",78,{"impact":17,"substance":18,"depth":17,"authority":151,"freshness":131,"relevant":21,"comment":266},"省级农科院团队在核心期刊发表的研究，方法新颖且精度高，对小麦涝害监测有实际价值。",[268],{"name":262,"url":260},[26,27,270,34,28],"多光谱无人机",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[273,274],{"code":45,"slug":46,"name":47,"path":48,"parent_code":43},{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":50,"locked":14,"version":51,"method":52,"evidence":276},{"candidates":277,"agricultural_evidence":57},[278,282,286,291,297,303],{"code":32,"score":151,"supported":57,"title":279,"abstract":280,"tags":281},[33],[34],[34],{"code":45,"score":13,"supported":57,"title":283,"abstract":284,"tags":285},[210],[],[270,28],{"code":39,"score":287,"supported":57,"title":288,"abstract":289,"tags":290},9,[64],[],[27],{"code":292,"score":131,"supported":57,"title":293,"abstract":295,"tags":296},"F3",[294],"fertilizer",[],[],{"code":298,"score":131,"supported":57,"title":299,"abstract":301,"tags":302},"H7",[300],"waterlogging",[],[],{"code":76,"score":304,"supported":57,"title":305,"abstract":306,"tags":308},4,[],[307],"smart agriculture",[26],[310,311],"农业人工智能 多光谱无人机 智慧农业 小麦","农业人工智能 多光谱无人机","农业人工智能多光谱无人机智慧农业小麦-1705",{"doi":9,"openalex_id":9,"authors":314,"venue":9,"cited_by_count":107,"oa_url":9,"card":315,"direction":114,"ingested_from":180},[],{"tldr":316,"method":317,"finding":318,"direction":114,"opportunity":319},"用无人机多光谱和VGG21深度学习检测小麦涝害并评估硅肥效果。","DJI P4多光谱无人机采集五波段图像，VGG21模型分类。","VGG21识别胁迫与处理效应准确率超91%，硅肥处理精确度96.42%。","早期涝害症状微妙，可研究多时相数据或结合产量数据量化胁迫与恢复效果。","2026-09-05T00:04:40.858350Z",{"id":322,"title":323,"url":324,"summary":325,"summary_zh":326,"content":9,"source_name":327,"source_url":324,"published_at":328,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":329,"score_detail":330,"sources":332,"tags":334,"primary_topic":338,"secondary_topics":339,"classification":342,"search_phrases":362,"slug":365,"view_count":366,"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。","Agronomy","2026-10-07T00:00:00Z",81,{"impact":17,"substance":18,"depth":17,"authority":190,"freshness":287,"relevant":21,"comment":331},"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。",[333],{"name":327,"url":324},[26,335,30,336,337],"农业遥感","迁移学习","叶绿素监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[340,341],{"code":45,"slug":46,"name":47,"path":48,"parent_code":43},{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":50,"locked":14,"version":51,"method":52,"evidence":343},{"candidates":344,"agricultural_evidence":57},[345,349,354,358],{"code":32,"score":56,"supported":57,"title":346,"abstract":347,"tags":348},[59,33],[34,59,33],[30],{"code":45,"score":128,"supported":57,"title":350,"abstract":352,"tags":353},[351],"hyperspectral",[28,72],[335],{"code":39,"score":13,"supported":57,"title":355,"abstract":356,"tags":357},[],[66,67,68,64],[336],{"code":76,"score":77,"supported":57,"title":359,"abstract":360,"tags":361},[],[80,81],[26],[363,364],"PROSAIL 冬小麦 叶绿素","高光谱 叶绿素 反演","PROSAIL冬小麦叶绿素-4433",5,"10.3390\u002Fagronomy16191971",{"doi":367,"openalex_id":369,"authors":370,"venue":327,"cited_by_count":107,"oa_url":324,"card":380,"direction":114,"ingested_from":115},"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":114,"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":14,"score":394,"score_detail":395,"sources":397,"tags":399,"primary_topic":401,"secondary_topics":402,"classification":405,"search_phrases":435,"slug":438,"view_count":21,"doi":439,"paper":440,"created_at":466},3863,"Species identification of crops and weeds in wheat fields using hyperspectral data and machine learning algorithms","https:\u002F\u002Fdoi.org\u002F10.31489\u002F2026feb3\u002F137-151","Based on establishing the relationship between the morphological and anatomical properties of plant tissues and the recognition efficiency of the machine learning algorithm, a comprehensive assessment of the limitations of ML algorithms (using Random Forest as an example) in the multiclass identification of crops and weeds in the agroecosystems of northeastern Kazakhstan was carried out for the first time. The study substantiates the principal biological causes manifested in the optical overlap of classes in the hyperspectral feature space. Data collection was performed with a hyperspectral sensor in the VIS-NIR range. The analysis of spectral profiles was carried out based on variance distribution assessment using principal component analysis and the construction of reflectance curves. Classification of the extracted data was performed using the Random Forest algorithm. Spring wheat (Triticum aestivum) crops at different phenological phases and five dominant weed species (Euphorbia virgata, Descurainia sophia, Vicia hirsuta, Fallopia convolvulus, Echinochloa crus-galli) were selected as research objects. The algorithm demonstrated \"crop-weed\" segregation efficiency with an overall classification accuracy on test data of 72.94%. High identification accuracy was revealed for wheat (MCC=0.994) due to its spectral stability and pronounced red edge features (680-730 nm). Reduced accuracy in intra-class weed separation is driven by species-specific anatomy: thick cuticles in Euphorbia virgata increase reflectance, complex structure in Vicia hirsuta causes spectral dispersion, and mesophyll similarity between Echinochloa crus-galli and grasses leads to optical overlap with crop features. Thus, the model enables reliable crop-weed differentiation, a key requirement for precision agriculture and targeted agrochemical application.","在建立植物组织形态解剖特性与机器学习算法识别效率之间关系的基础上，首次对机器学习算法（以随机森林为例）在哈萨克斯坦东北部农业生态系统中作物与杂草多类识别方面的局限性进行了综合评估。研究证实了在高光谱特征空间中表现为类别光谱重叠的主要生物学成因。数据采集使用VIS-NIR波段的高光谱传感器完成。光谱剖面分析基于主成分分析的方差分布评估及反射率曲线构建进行。提取数据的分类采用随机森林算法完成。选取不同物候期的春小麦（Triticum aestivum）作物及五种优势杂草物种（Euphorbia virgata、Descurainia sophia、Vicia hirsuta、Fallopia convolvulus、Echinochloa crus-galli）作为研究对象。该算法在测试数据上实现了“作物-杂草”分离，总体分类精度为72.94%。小麦因其光谱稳定性及显著的红边特征（680-730 nm）而表现出较高的识别精度（MCC=0.994）。杂草类内分离精度降低由物种特异性解剖结构所致：Euphorbia virgata的厚角质层增加了反射率，Vicia hirsuta的复杂结构导致光谱离散，而Echinochloa crus-galli与禾本科植物之间的叶肉相似性导致其与作物特征发生光谱重叠。因此，该模型能够实现可靠的作物-杂草区分，这是精准农业和靶向农化施用的关键要求。","Fundamental and Experimental Biology","2026-09-29T00:00:00Z",70,{"impact":151,"substance":146,"depth":130,"authority":151,"freshness":287,"relevant":21,"comment":396},"高光谱结合机器学习实现麦田作物与杂草识别，方法新颖、结论具体，对精准农业有参考价值，但属细分领域研究，影响范围有限。",[398],{"name":392,"url":389},[26,67,34,400,28],"杂草识别",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[403,404],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":45,"slug":46,"name":47,"path":48,"parent_code":43},{"status":50,"locked":14,"version":51,"method":52,"evidence":406},{"candidates":407,"agricultural_evidence":57},[408,412,416,420,424,430],{"code":32,"score":128,"supported":57,"title":409,"abstract":410,"tags":411},[33],[34,33],[34],{"code":39,"score":128,"supported":57,"title":413,"abstract":414,"tags":415},[68],[67,68],[67],{"code":45,"score":151,"supported":57,"title":417,"abstract":418,"tags":419},[351],[351],[28],{"code":76,"score":77,"supported":57,"title":421,"abstract":422,"tags":423},[],[80,81],[26],{"code":425,"score":95,"supported":57,"title":426,"abstract":427,"tags":429},"H6",[],[428],"农业生态",[],{"code":431,"score":21,"supported":14,"title":432,"abstract":433,"tags":434},"F4",[],[],[400],[436,437],"高光谱 小麦 杂草 识别","随机森林 作物 杂草 分类","高光谱小麦杂草识别-3863","10.31489\u002F2026feb3\u002F137-151",{"doi":439,"openalex_id":441,"authors":442,"venue":392,"cited_by_count":107,"oa_url":9,"card":461,"direction":112,"ingested_from":115},"W7214771122",[443,446,449,452,455,458],{"name":444,"orcid":445},"Rimma Meyramovna Ualiyeva","https:\u002F\u002Forcid.org\u002F0000-0003-3551-5007",{"name":447,"orcid":448},"Mariya M. Kaverina","https:\u002F\u002Forcid.org\u002F0000-0002-3838-7656",{"name":450,"orcid":451},"Александра Викторовна Осипова","https:\u002F\u002Forcid.org\u002F0000-0002-3947-5252",{"name":453,"orcid":454},"E. Koppayev","https:\u002F\u002Forcid.org\u002F0009-0004-0476-9017",{"name":456,"orcid":457},"М. А. Чайзабекова","https:\u002F\u002Forcid.org\u002F0009-0004-0468-956X",{"name":459,"orcid":460},"Sayan Berikovich Zhangazin","https:\u002F\u002Forcid.org\u002F0000-0002-5813-8331",{"tldr":462,"method":463,"finding":464,"direction":114,"opportunity":465},"利用高光谱数据与随机森林识别哈萨克斯坦东北部麦田作物与杂草，并分析误判的生物学原因。","VIS-NIR高光谱传感器采集数据，PCA分析光谱，随机森林分类春小麦与五种杂草","作物-杂草总体分类精度72.94%，小麦识别精度高（MCC=0.994），杂草种间分离受解剖结构影响","可探索融合形态解剖特征与深度学习，提升杂草种间分类精度，服务精准施药。","2026-09-30T23:30:54.952093Z",{"id":468,"title":469,"url":470,"summary":471,"summary_zh":9,"content":9,"source_name":472,"source_url":9,"published_at":473,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":264,"score_detail":474,"sources":476,"tags":478,"primary_topic":479,"secondary_topics":480,"classification":482,"search_phrases":504,"slug":506,"view_count":21,"doi":507,"paper":508,"created_at":515},2212,"《基于遥感的农作物产量估算综述:机器学习技术及环境、算法和硬件限制》","https:\u002F\u002Fwww.frontiersin.org\u002Farticles\u002F10.3389\u002Ffpls.2026.1742689\u002Ffull","Muhammad等系统综述遥感技术在农作物和植物产量估算中的应用进展,提出将遥感方法学系统分类为:传感器方法、平台方法、分析与建模方法、机器学习方法。基于多项研究结果发现,基于深度学习的架构在精度、查准率、查全率和F1分数等关键评估指标上一致实现优越性能,这种性能优势源于其学习分层表示、捕捉复杂非线性关系、高效扩展大规模数据集的能力。论文还将局限性系统归纳为环境、算法、硬件操作和无线传感器网络四大类别。","Frontiers in Plant Science 2026年9月","2026-09-07T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":190,"freshness":85,"relevant":21,"comment":475},"系统综述遥感产量估算方法学并归纳环境、算法与硬件限制，方法分类清晰、结论可靠，对农业遥感与AI应用有较高参考价值。",[477],{"name":472,"url":470},[26,27,67,28,29],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[481],{"code":45,"slug":46,"name":47,"path":48,"parent_code":43},{"status":50,"locked":14,"version":51,"method":52,"evidence":483},{"candidates":484,"agricultural_evidence":57},[485,490,494,500],{"code":39,"score":486,"supported":57,"title":487,"abstract":488,"tags":489},16,[67],[66,67],[27,67],{"code":45,"score":19,"supported":57,"title":491,"abstract":492,"tags":493},[28],[28],[29,28],{"code":495,"score":95,"supported":57,"title":496,"abstract":497,"tags":499},"I5",[],[498],"无线传感",[],{"code":76,"score":21,"supported":14,"title":501,"abstract":502,"tags":503},[],[],[26],[505,93],"农业人工智能 产量估算 智慧农业 机器学习","农业人工智能产量估算智慧农业机器学习-2212","10.3389\u002Ffpls.2026.1742689\u002Ffull",{"doi":507,"openalex_id":9,"authors":509,"venue":9,"cited_by_count":107,"oa_url":9,"card":510,"direction":114,"ingested_from":180},[],{"tldr":511,"method":512,"finding":513,"direction":114,"opportunity":514},"系统综述遥感估产方法，分类传感器、平台、建模与机器学习，指出深度学习性能最优。","文献综述，按传感器、平台、分析建模、机器学习四类归纳遥感估产方法。","深度学习在精度、查准率、召回率和F1上一致优于其他方法，局限归为环境、算法、硬件与无线传感器网络四类","可针对综述指出的环境与硬件限制，研究轻量化深度学习模型在边缘设备上的实时估产。","2026-09-12T00:06:40.290409Z"]