[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4693":3,"related-4693":114},{"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":44,"search_phrases":89,"slug":92,"view_count":79,"doi":93,"paper":94,"created_at":113},4693,"Scalable, cloud-native decision support system for Variable Rate Application (VRA) based on multi-year NDRE time series in arable precision agriculture","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23282433","This paper presents an automated, cloud-native geospatial Decision Support System (DSS) that generates variable rate prescription maps directly from Sentinel-2 satellite observations. Designed as an end-to-end arable field VRA architecture, the system operates completely autonomously with minimal input, requiring only the field boundary polygon, the crop phenology class (winter or spring crop), and the multi-year observation window. The computation engine streams Cloud-Optimized GeoTIFF (COG) rasters via asynchronous cloud connectivity through the Microsoft Planetary Computer (MPC) SpatioTemporal Asset Catalog (STAC) Application Programming Interface (API), enforcing data quality through multi-stage Scene Classification Layer (SCL) filtering. Instead of the rapidly saturating Normalized Difference Vegetation Index (NDVI), the model relies on the Normalized Difference Red Edge (NDRE) index, which accurately captures canopy chlorophyll and green biomass dynamics. For each native 10×10 m grid cell, the system extracts the 95th percentile (P95) peak value within a crop-specific reproductive phenological window, eliminating single-day snapshot noise while validating winter or spring growth trajectories via automated phenological ratio calculations. The decision and prescription engine is built upon two core agronomic safety pillars: An exponential empirical Bayesian stability shrinkage that dampens unstable, high-variance zones toward the safe field average, preventing the over-application of inputs on drought-prone areas. A trigonometric Horn slope attenuation derived from digital elevation models that systematically reduces application rates on erosion-prone steep slopes. Cell-level rates are distributed within the target tolerance range using a rank-allocation algorithm coupled with a Hamilton quota (largest remainder method) correction, guaranteeing strict integer-based machine compatibility and zero-sum conservation of the total field input budget. The technology was validated on a 139.03 ha commercial production field across four reference seasons (2022, 2023, 2024, and 2026) for Variable Rate Seeding (VRS) in winter wheat (Triticum aestivum L., 230 kg\u002Fha baseline seed rate) in a rolling hilly landscape in Hungary, benchmarked against conventional uniform application and conservative multi-year VRS planning. The primary quantitative findings are summarized below: · Failure of conventional uniform application: Across the 14,250 cells, the flat 230 kg\u002Fha rate was optimal on only 1.1% of the field area. · Input reallocation: The model reallocated 1,633.7 kg of seed (and 1,167.1 kg compared to conservative VRS) from unproductive areas into high-capacity zones (Figure 1.). Across 98.9% of the field, conventional uniform management generated systematic under- or over-seeding, inducing yield losses or wasted operational expenditure. · Drought mitigation: In temporally unstable zones subject to extreme weather risk (covering 33.6% of the field, 46.69 ha), Bayesian stability shrinkage reduced seed allocation by 495 kg compared to uniform seeding. Establishing lower canopy densities in these vulnerable zones mitigates intra-crop water competition and prevents early-season forced ripening (Lollato et al., 2017). · Erosion control: On steep slopes exceeding 12% inclination (accounting for 9.75% of the field, 13.56 ha), the Horn topographic attenuation engine reduced seed input by 553 kg relative to the fixed 230 kg\u002Fha baseline. This intervention minimizes surface runoff damage and agrochemical leaching. · Economic return: Spatially optimizing plant density based on pedological and topographic constraints delivers substantial yield gains. Reallocating seed across the field—without any expenditure on supplemental fertilizer—produces a projected yield increase of +0.20 to +0.40 t\u002Fha (+28 to +56 tons of grain across the evaluated field) (Kindred et al., 2016). At a baseline wheat price of €192\u002Ft (70,000 HUF\u002Ft), this generates an estimated net value of €5,376 to €10,752 (1.96 to 3.92M HUF) on this single 139 ha field.","本文提出了一种自动化、云原生的地理空间决策支持系统（DSS），可直接从Sentinel-2卫星观测数据生成变量施肥处方图。该系统设计为端到端的耕地变量施肥（VRA）架构，完全自主运行且输入需求极低，仅需田块边界多边形、作物物候类别（冬作或春作）以及多年观测窗口。计算引擎通过Microsoft Planetary Computer（MPC）时空资产目录（STAC）应用程序接口（API）以异步云连接方式流式传输云优化GeoTIFF（COG）栅格数据，并通过多阶段场景分类层（SCL）滤波确保数据质量。该模型不采用易快速饱和的归一化差异植被指数（NDVI），而是依赖归一化差异红边指数（NDRE），后者能够准确捕捉冠层叶绿素和绿色生物量动态。对于每个原生10×10 m网格单元，系统提取作物特定生殖物候窗口内的第95百分位数（P95）峰值，消除单日快照噪声，同时通过自动化物候比率计算验证冬作或春作生长轨迹。决策与处方引擎建立在两个核心农艺安全支柱之上：指数经验贝叶斯稳定性收缩，将不稳定的高方差区域向安全的田块平均值收缩，防止在易旱区域过量施用投入品；基于数字高程模型推导的三角函数Horn坡度衰减，系统性地降低易侵蚀陡坡上的施用量。单元级施用量通过秩分配算法结合Hamilton配额（最大余数法）校正，在目标容差范围内分配，确保严格的基于整数的机器兼容性和田块总投入预算的零和守恒。该技术已在匈牙利起伏丘陵地貌的139.03 ha商业化生产田块上，针对冬小麦（Triticum aestivum L.，基准播种量230 kg\u002Fha）的变量播种（VRS），在四个参考季节（2022、2023、2024和2026年）进行了验证，并以常规均一施用和保守的多年VRS规划为基准进行比较。主要定量结果总结如下：· 常规均一施用的失败：在14,250个单元中，统一的230 kg\u002Fha施",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-10-10T00:00:00Z","论文",10,false,83,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":21,"comment":22},18,23,19,13,1,"基于多年度NDRE时序的云原生变量施肥决策系统，方法新颖、验证数据扎实，对精准农业信息化有较高参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"数字农业","智慧农业","变量施肥","精准农业","遥感",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",{"status":45,"locked":14,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":53},[50,58,63,69,77,84],{"code":51,"score":52,"supported":53,"title":54,"abstract":56,"tags":57},"I1",11,true,[55],"precision agriculture",[],[26,27,29],{"code":32,"score":52,"supported":53,"title":59,"abstract":61,"tags":62},[60],"decision support",[60],[],{"code":39,"score":64,"supported":53,"title":65,"abstract":66,"tags":68},9,[],[41,67,40],"winter wheat",[],{"code":70,"score":71,"supported":53,"title":72,"abstract":73,"tags":76},"F3",7,[],[74,75],"施肥","fertilizer",[28],{"code":78,"score":79,"supported":53,"title":80,"abstract":81,"tags":83},"H7",3,[],[82],"drought",[],{"code":85,"score":21,"supported":14,"title":86,"abstract":87,"tags":88},"I3",[],[],[30],[90,91],"Sentinel-2 NDRE 变量施肥","云原生 决策支持系统 精准农业","Sentinel-2NDRE变量施肥-4693","10.5281\u002Fzenodo.23282433",{"doi":93,"openalex_id":95,"authors":96,"venue":10,"cited_by_count":104,"oa_url":6,"card":105,"direction":111,"ingested_from":112},"W7221365319",[97,99,101],{"name":98,"orcid":9},"Zoltán Szenek",{"name":100,"orcid":9},"Donát Szám",{"name":102,"orcid":103},"Gábor Milics","https:\u002F\u002Forcid.org\u002F0000-0002-4499-8322",0,{"tldr":106,"method":107,"finding":108,"direction":109,"opportunity":110},"构建云原生决策支持系统，用多年NDRE时序自动生成精准农业变量施肥\u002F播种处方图。","Sentinel-2 NDRE、COG\u002FSTAC云流式处理、贝叶斯收缩与坡度衰减","在匈牙利139公顷冬小麦田验证，系统优于传统均一施用与保守多年VRS规划。","农业遥感与作物表型","可探索将NDRE时序DSS扩展至多作物、多区域及实时物联网数据融合的通用处方框架。","数字乡村与农业信息化","openalex","2026-10-11T23:30:36.790037Z",{"total":115,"page":21,"page_size":115,"items":116},6,[117,209,293,362,423,502],{"id":118,"title":119,"url":120,"summary":121,"summary_zh":122,"content":9,"source_name":123,"source_url":120,"published_at":124,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":125,"score_detail":126,"sources":131,"tags":133,"primary_topic":136,"secondary_topics":137,"classification":143,"search_phrases":176,"slug":179,"view_count":21,"doi":180,"paper":181,"created_at":208},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":127,"substance":128,"depth":129,"authority":127,"freshness":64,"relevant":21,"comment":130},12,20,17,"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。",[132],{"name":123,"url":120},[27,134,41,135,30],"机器学习","杂草识别",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[138,139],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":85,"slug":140,"name":141,"path":142,"parent_code":36},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":45,"locked":14,"version":46,"method":47,"evidence":144},{"candidates":145,"agricultural_evidence":53},[146,151,156,161,165,171],{"code":39,"score":147,"supported":53,"title":148,"abstract":149,"tags":150},15,[40],[41,40],[41],{"code":32,"score":147,"supported":53,"title":152,"abstract":154,"tags":155},[153],"machine learning",[134,153],[134],{"code":85,"score":127,"supported":53,"title":157,"abstract":159,"tags":160},[158],"hyperspectral",[158],[30],{"code":51,"score":71,"supported":53,"title":162,"abstract":163,"tags":164},[],[29,55],[27],{"code":166,"score":79,"supported":53,"title":167,"abstract":168,"tags":170},"H6",[],[169],"农业生态",[],{"code":172,"score":21,"supported":14,"title":173,"abstract":174,"tags":175},"F4",[],[],[135],[177,178],"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":180,"openalex_id":182,"authors":183,"venue":123,"cited_by_count":104,"oa_url":9,"card":202,"direction":207,"ingested_from":112},"W7214771122",[184,187,190,193,196,199],{"name":185,"orcid":186},"Rimma Meyramovna Ualiyeva","https:\u002F\u002Forcid.org\u002F0000-0003-3551-5007",{"name":188,"orcid":189},"Mariya M. Kaverina","https:\u002F\u002Forcid.org\u002F0000-0002-3838-7656",{"name":191,"orcid":192},"Александра Викторовна Осипова","https:\u002F\u002Forcid.org\u002F0000-0002-3947-5252",{"name":194,"orcid":195},"E. Koppayev","https:\u002F\u002Forcid.org\u002F0009-0004-0476-9017",{"name":197,"orcid":198},"М. А. Чайзабекова","https:\u002F\u002Forcid.org\u002F0009-0004-0468-956X",{"name":200,"orcid":201},"Sayan Berikovich Zhangazin","https:\u002F\u002Forcid.org\u002F0000-0002-5813-8331",{"tldr":203,"method":204,"finding":205,"direction":109,"opportunity":206},"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。","农业人工智能与决策模型","2026-09-30T23:30:54.952093Z",{"id":210,"title":211,"url":212,"summary":213,"summary_zh":214,"content":9,"source_name":215,"source_url":212,"published_at":216,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":217,"score_detail":218,"sources":223,"tags":225,"primary_topic":228,"secondary_topics":229,"classification":232,"search_phrases":263,"slug":266,"view_count":79,"doi":267,"paper":268,"created_at":292},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。","Remote Sensing","2026-09-15T00:00:00Z",80,{"impact":17,"substance":219,"depth":17,"authority":220,"freshness":221,"relevant":21,"comment":222},22,14,8,"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。",[224],{"name":215,"url":212},[27,226,41,30,227],"农业人工智能","精准灌溉",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[230,231],{"code":85,"slug":140,"name":141,"path":142,"parent_code":36},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":233},{"candidates":234,"agricultural_evidence":53},[235,240,246,250,259],{"code":39,"score":236,"supported":53,"title":237,"abstract":238,"tags":239},26,[67,40],[41,67,40],[41],{"code":85,"score":17,"supported":53,"title":241,"abstract":243,"tags":245},[242],"remote sensing",[30,242,244],"multispectral",[30],{"code":32,"score":147,"supported":53,"title":247,"abstract":248,"tags":249},[153],[134,153],[226],{"code":251,"score":13,"supported":53,"title":252,"abstract":253,"tags":258},"H3",[],[254,255,256,257],"灌溉","水资源","irrigation","water resource",[227],{"code":51,"score":71,"supported":53,"title":260,"abstract":261,"tags":262},[],[29,55],[27],[264,265],"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":267,"openalex_id":269,"authors":270,"venue":215,"cited_by_count":104,"oa_url":212,"card":287,"direction":109,"ingested_from":112},"W7213246708",[271,274,276,278,281,284],{"name":272,"orcid":273},"Fan Ding","https:\u002F\u002Forcid.org\u002F0000-0001-5482-8290",{"name":275,"orcid":9},"Qian Cheng",{"name":277,"orcid":9},"Fuyi Duan",{"name":279,"orcid":280},"Shuaipeng Fei","https:\u002F\u002Forcid.org\u002F0000-0002-8774-7929",{"name":282,"orcid":283},"Junjie Feng","https:\u002F\u002Forcid.org\u002F0000-0001-8900-2691",{"name":285,"orcid":286},"Zhen Chen","https:\u002F\u002Forcid.org\u002F0000-0002-2847-0042",{"tldr":288,"method":289,"finding":290,"direction":109,"opportunity":291},"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":294,"title":295,"url":296,"summary":297,"summary_zh":298,"content":9,"source_name":215,"source_url":296,"published_at":299,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":300,"score_detail":301,"sources":304,"tags":306,"primary_topic":309,"secondary_topics":310,"classification":313,"search_phrases":341,"slug":344,"view_count":79,"doi":345,"paper":346,"created_at":361},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。","2026-09-08T00:00:00Z",73,{"impact":17,"substance":219,"depth":17,"authority":20,"freshness":302,"relevant":21,"comment":303},2,"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。",[305],{"name":215,"url":296},[27,226,30,307,308],"产量估算","冬小麦",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[311,312],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":85,"slug":140,"name":141,"path":142,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":314},{"candidates":315,"agricultural_evidence":53},[316,320,326,330,334],{"code":39,"score":236,"supported":53,"title":317,"abstract":318,"tags":319},[67,40],[41,67,40],[308],{"code":32,"score":17,"supported":53,"title":321,"abstract":323,"tags":325},[322],"deep learning",[324,134,153,322],"深度学习",[226],{"code":85,"score":13,"supported":53,"title":327,"abstract":328,"tags":329},[242],[],[307,30],{"code":51,"score":71,"supported":53,"title":331,"abstract":332,"tags":333},[],[29,55],[27],{"code":335,"score":115,"supported":53,"title":336,"abstract":337,"tags":340},"A8",[],[338,339],"粮食安全","food security",[],[342,343],"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","10.3390\u002Frs18183061",{"doi":345,"openalex_id":347,"authors":348,"venue":215,"cited_by_count":104,"oa_url":296,"card":356,"direction":109,"ingested_from":112},"W7211938917",[349,351,353],{"name":350,"orcid":9},"Anqi Xue",{"name":352,"orcid":9},"Shufang Tian",{"name":354,"orcid":355},"Tingyan Fu","https:\u002F\u002Forcid.org\u002F0000-0003-4207-4211",{"tldr":357,"method":358,"finding":359,"direction":207,"opportunity":360},"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。","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。","2026-09-09T23:30:22.411579Z",{"id":363,"title":364,"url":365,"summary":366,"summary_zh":9,"content":9,"source_name":367,"source_url":9,"published_at":368,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":369,"score_detail":370,"sources":372,"tags":374,"primary_topic":376,"secondary_topics":377,"classification":380,"search_phrases":410,"slug":413,"view_count":79,"doi":9,"paper":414,"created_at":422},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":219,"depth":17,"authority":127,"freshness":221,"relevant":21,"comment":371},"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。",[373],{"name":367,"url":365},[27,226,375,41,30],"多光谱无人机",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[378,379],{"code":85,"slug":140,"name":141,"path":142,"parent_code":36},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":381},{"candidates":382,"agricultural_evidence":53},[383,387,391,395,399,404],{"code":39,"score":127,"supported":53,"title":384,"abstract":385,"tags":386},[40],[41],[41],{"code":85,"score":13,"supported":53,"title":388,"abstract":389,"tags":390},[244],[],[375,30],{"code":32,"score":64,"supported":53,"title":392,"abstract":393,"tags":394},[322],[],[226],{"code":70,"score":221,"supported":53,"title":396,"abstract":397,"tags":398},[75],[],[],{"code":78,"score":221,"supported":53,"title":400,"abstract":402,"tags":403},[401],"waterlogging",[],[],{"code":51,"score":405,"supported":53,"title":406,"abstract":407,"tags":409},4,[],[408],"smart agriculture",[27],[411,412],"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":415,"venue":9,"cited_by_count":104,"oa_url":9,"card":416,"direction":109,"ingested_from":421},[],{"tldr":417,"method":418,"finding":419,"direction":109,"opportunity":420},"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。","agent","2026-09-05T00:04:40.858350Z",{"id":424,"title":425,"url":426,"summary":427,"summary_zh":428,"content":9,"source_name":429,"source_url":426,"published_at":430,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":431,"score_detail":432,"sources":436,"tags":438,"primary_topic":441,"secondary_topics":442,"classification":450,"search_phrases":478,"slug":481,"view_count":104,"doi":482,"paper":483,"created_at":501},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":433,"substance":434,"depth":129,"authority":20,"freshness":13,"relevant":21,"comment":435},16,21,"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。",[437],{"name":429,"url":426},[27,134,439,440,308],"遥感监测","土壤墒情",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[443,449],{"code":444,"slug":445,"name":446,"path":447,"parent_code":448},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":451},{"candidates":452,"agricultural_evidence":53},[453,457,462,466,470,474],{"code":39,"score":236,"supported":53,"title":454,"abstract":455,"tags":456},[67,40],[41,67,40],[308],{"code":444,"score":147,"supported":53,"title":458,"abstract":459,"tags":461},[445],[460,445],"土壤",[440],{"code":32,"score":147,"supported":53,"title":463,"abstract":464,"tags":465},[153],[134,153],[134],{"code":85,"score":13,"supported":53,"title":467,"abstract":468,"tags":469},[],[30,242,244],[439],{"code":251,"score":115,"supported":53,"title":471,"abstract":472,"tags":473},[],[254,256],[],{"code":51,"score":405,"supported":53,"title":475,"abstract":476,"tags":477},[],[27],[27],[479,480],"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":482,"openalex_id":484,"authors":485,"venue":429,"cited_by_count":104,"oa_url":426,"card":495,"direction":500,"ingested_from":112},"W7223156204",[486,489,491,493],{"name":487,"orcid":488},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":490,"orcid":9},"Yi Zhang",{"name":492,"orcid":9},"WEI Lei",{"name":494,"orcid":9},"Chengcai Zhang",{"tldr":496,"method":497,"finding":498,"direction":109,"opportunity":499},"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":503,"title":504,"url":505,"summary":506,"summary_zh":507,"content":9,"source_name":508,"source_url":505,"published_at":509,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":510,"score_detail":511,"sources":513,"tags":515,"primary_topic":519,"secondary_topics":520,"classification":522,"search_phrases":537,"slug":540,"view_count":21,"doi":541,"paper":542,"created_at":569},4806,"A study on the identification of wheat powdery mildew using chlorophyll fluorescence imaging","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1837843","Introduction Wheat powdery mildew poses a serious threat to global wheat production, and its accurate identification is key to smart agriculture. Methods This study combines chlorophyll fluorescence imaging with deep learning algorithms to investigate the temporal dynamics of key fluorescence parameters (such as maximum photochemical efficiency, QYmax) during the progression of powderymildew. A QYmax-based chlorophyll fluorescence image dataset for wheat powdery mildew was constructed. Building on this, the LCRNet model was proposed, which effectively processes fluorescence image data through a collaborative mechanism integrating the Large Selective Adaptive Fusion (LSAF) and Coordinated Attention (CA) modules. Results On an independent test set, the model achieved an accuracy of 94% and an F1 score of 94.1%, outperforming comparison models. Discussion This study proposes a method for classifying the severity of powdery mildew in wheat that reduces the computational cost of fluorescence data processing, providing important methodological support and technical insights for advancing the intelligent, precise, and green transformation of crop disease prevention and control systems.","引言 小麦白粉病对全球小麦生产构成严重威胁，其准确识别是实现智慧农业的关键。方法 本研究将叶绿素荧光成像与深度学习算法相结合，探究白粉病发展过程中关键荧光参数（如最大光化学效率QYmax）的时间动态变化，构建了基于QYmax的小麦白粉病叶绿素荧光图像数据集。在此基础上，提出了LCRNet模型，该模型通过融合大选择性自适应融合（LSAF）模块与协调注意力（CA）模块的协同机制，有效处理荧光图像数据。结果 在独立测试集上，该模型达到了94%的准确率和94.1%的F1分数，优于对比模型。讨论 本研究提出了一种小麦白粉病严重程度分类方法，降低了荧光数据处理的计算成本，为推动作物病害防控体系向智能化、精准化和绿色化转型提供了重要的方法学支撑和技术启示。","Frontiers in Plant Science","2026-10-12T00:00:00Z",81,{"impact":17,"substance":219,"depth":17,"authority":220,"freshness":64,"relevant":21,"comment":512},"将叶绿素荧光成像与深度学习结合用于小麦白粉病严重度识别，方法新颖、数据可靠，对智慧植保有实质参考价值。",[514],{"name":508,"url":505},[27,226,516,517,518],"病害识别","叶绿素荧光","小麦白粉病",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[521],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":523},{"candidates":524,"agricultural_evidence":53},[525,529,533],{"code":39,"score":220,"supported":53,"title":526,"abstract":527,"tags":528},[40],[41,40],[],{"code":32,"score":221,"supported":53,"title":530,"abstract":531,"tags":532},[],[324,322],[226,516],{"code":51,"score":71,"supported":53,"title":534,"abstract":535,"tags":536},[],[27,408],[27],[538,539],"小麦白粉病 叶绿素荧光 深度学习","LCRNet 小麦病害识别","小麦白粉病叶绿素荧光深度学习-4806","10.3389\u002Ffpls.2026.1837843",{"doi":541,"openalex_id":543,"authors":544,"venue":508,"cited_by_count":104,"oa_url":563,"card":564,"direction":500,"ingested_from":112},"W7221549180",[545,547,550,553,555,557,559,561],{"name":546,"orcid":9},"Yijia Chen",{"name":548,"orcid":549},"Chengyuan Hao","https:\u002F\u002Forcid.org\u002F0000-0002-6002-3963",{"name":551,"orcid":552},"Zhiyu Li","https:\u002F\u002Forcid.org\u002F0000-0003-3771-0199",{"name":554,"orcid":9},"Xupeng An",{"name":556,"orcid":9},"Jingxiang Huang",{"name":558,"orcid":9},"Xinchang Chen",{"name":560,"orcid":9},"Shidong Wang",{"name":562,"orcid":9},"ZhiChao Chen","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1837843\u002Fpdf",{"tldr":565,"method":566,"finding":567,"direction":207,"opportunity":568},"该研究利用叶绿素荧光成像与深度学习识别小麦白粉病并分级。","构建QYmax荧光图像数据集，提出融合LSAF与CA模块的LCRNet模型。","模型在独立测试集上准确率达94%、F1为94.1%，优于对比模型。","可探索多病害、多作物荧光图像通用模型，并融合田间时序数据实现早期预警。","2026-10-13T23:30:09.060156Z"]