[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3168":3,"related-3168":58},{"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,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":57},3168,"Spatial and Temporal Analysis of Rice Yield Using the DSSAT Model in the Kommamuru Canal Command Area, Guntur (Dist), Andhra Pradesh, India","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjeai\u002F2026\u002Fv48i104524","Rice is the predominant crop cultivated in the Kommamuru Canal Command Area of Guntur district, Andhra Pradesh. Rice productivity is strongly influenced by irrigation water availability and climatic conditions. The present study aimed to analyse the spatial and temporal variability of rice productivity using the DSSAT-CERES Rice model integrated with Sentinel-2 remote sensing data during the kharif seasons of 2022, 2023 and 2024. Rice-cultivated areas were mapped using multi-temporal Sentinel-2 imagery, while weather, soil, crop management and cultivar data were used as inputs for DSSAT simulations. The model was calibrated and validated using field observations and yield data collected from representative locations within the command area. Simulated rice yields showed considerable variation among mandals and across years, ranging from 1,560.58 to 4,111.06 kg ha⁻¹. The highest productivity was observed in 2024 due to favourable rainfall distribution, adequate canal water supply and improved crop growth conditions, whereas relatively lower productivity was recorded in 2023 owing to moisture stress and irregular irrigation availability. Spatial yield maps generated through the integration of DSSAT outputs and GIS-based rice maps identified high- and low-productivity zones across the command area. The study demonstrated the usefulness of combining crop simulation modelling and remote sensing techniques for rice yield assessment, irrigation planning, yield forecasting and sustainable water resource management in canal command areas. The study confirmed that the DSSAT-CERES Rice model effectively simulated rice productivity in the Kommamuru Canal Command Area during 2022-2024. Overall, the study provides a reliable framework for sustainable rice production and efficient water management in canal command areas and can be applied for future agricultural planning and decision-making. The validation results showed low percentage deviation values ranging from 0.94% to 4.39%, indicating good agreement between simulated and actual farmer yields. Low percentage deviation values between simulated and actual yields indicated good model accuracy and reliability for yield prediction. The integration of DSSAT, GIS and remote sensing techniques was useful for sustainable rice production planning and efficient irrigation management.","水稻是安得拉邦贡土尔县科马穆鲁灌区种植的主要作物。水稻生产力受灌溉可用水量和气候条件的强烈影响。本研究旨在利用DSSAT-CERES水稻模型结合Sentinel-2遥感数据，分析2022年、2023年和2024年kharif季水稻生产力的时空变异性。利用多时相Sentinel-2影像绘制水稻种植区，同时将气象、土壤、作物管理和品种数据作为DSSAT模拟的输入。利用从灌区内代表性地点收集的田间观测数据和产量数据对模型进行校准和验证。模拟水稻产量在不同mandal和年份间表现出相当大的变异，范围为1,560.58至4,111.06 kg ha⁻¹。2024年由于有利的降雨分布、充足的渠水供应和改善的作物生长条件，生产力最高；而2023年由于水分胁迫和不规律的灌溉可用性，生产力相对较低。通过整合DSSAT输出和基于GIS的水稻分布图生成的空间产量图，识别出灌区内的高产区和低产区。研究表明，将作物模拟模型与遥感技术相结合，对于灌区水稻产量评估、灌溉规划、产量预测和可持续水资源管理具有实用价值。研究证实，DSSAT-CERES水稻模型有效模拟了2022-2024年科马穆鲁灌区的水稻生产力。总体而言，本研究为灌区可持续水稻生产和高效水资源管理提供了可靠的框架，可应用于未来的农业规划和决策。验证结果显示，百分比偏差值较低，范围为0.94%至4.39%，表明模拟产量与实际农民产量之间具有良好的一致性。模拟产量与实际产量之间较低的百分比偏差值表明模型在产量预测方面具有良好的准确性和可靠性。DSSAT、GIS和遥感技术的整合有助于可持续水稻生产规划和高效灌溉管理。",null,"Journal of Experimental Agriculture International","2026-09-22T00:00:00Z","论文",10,false,72,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":21,"comment":22},12,20,17,13,1,"印度区域尺度的DSSAT与Sentinel-2融合估产研究，方法成熟、验证可靠，对遥感估产与灌溉管理有参考价值，但属区域性案例，公共影响有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"水稻","产量预测","遥感","作物模型","灌溉管理",[32,33],"DSSAT CERES Rice 水稻","Sentinel-2 水稻 遥感估产","DSSATCERESRice水稻-3168",0,"10.9734\u002Fjeai\u002F2026\u002Fv48i104524",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":6,"card":50,"direction":54,"ingested_from":56},"W7213966772",[40,42,44,46,48],{"name":41,"orcid":9},"Rana Prathap",{"name":43,"orcid":9},"G. Ravi Babu",{"name":45,"orcid":9},"V. Muthayya Chowdary",{"name":47,"orcid":9},"K.Krupavathi",{"name":49,"orcid":9},"K. Chandrasekhar",{"tldr":51,"method":52,"finding":53,"direction":54,"opportunity":55},"用DSSAT-CERES水稻模型结合Sentinel-2遥感，分析印度Guntur灌区2022-20","DSSAT-CERES水稻模型、Sentinel-2多时相影像、GIS空间制图与","模拟产量1560-4111 kg\u002Fha，2024年最高、2023年因水分胁迫最低，验证偏差仅0.94","农业遥感与作物表型","可引入机器学习同化遥感与作物模型，提升灌区尺度产量预报精度并支撑灌溉决策。","openalex","2026-09-22T23:30:22.711640Z",{"total":59,"page":21,"page_size":59,"items":60},6,[61,114,156,194,229,268],{"id":62,"title":63,"url":64,"summary":65,"summary_zh":9,"content":9,"source_name":66,"source_url":64,"published_at":67,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":68,"score_detail":69,"sources":75,"tags":77,"search_phrases":80,"slug":83,"view_count":35,"doi":84,"paper":85,"created_at":113},1210,"Assessing rice genotype dependency in remote sensing: challenges in nitrogen discrimination and yield forecasting","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10432-8","Abstract Background Rice ( Oryza sativa L.) is a staple crop that accounts for 8% of the global primary crop production. This sector faces an environmental challenge driven by climate change, rising water scarcity, and the need for more resilient and adaptive production systems. Although Remote Sensing (RS) offers solutions for optimizing inputs, most current models are calibrated for specific varieties, limiting their application across diverse cultivars. This study evaluates the transferability of RS models for monitoring nitrogen (N) fertilization status and predicting yield across a highly heterogeneous dataset of rice genotypes, locations and seasons. Materials and methods Six field trials were conducted across three locations in Spain (Valencia and Tarragona) during seasons 2022 and 2023. The study analysed over 170 cultivars, including commercial Japonica and Indica varieties, and a selection of 170 non-commercialized-under development varieties, both subjected to low (100 kg N\u002Fha) and high (200 kg N\u002Fha) fertilization regimes. Multispectral UAV imagery (MAIA S2) was normalized using Accumulated Growing Degree Days (GDD) to align phenological stages across sites. Random Forest (RF) classifiers were employed to analyse the capacity of RS to identify whether rice paddies are under- or over-fertilized. The transferability of N models between rice genotypes was also assessed. Furthermore, the previously established MS3 + yield regression model, originally developed for the JSendra variety, was evaluated against a multi-variety dataset. Results Random Forest classifiers effectively discriminated between nitrogen application rates across diverse genotypes, with several sites exceeding an 85% validation accuracy. A consistent trend emerges when analysing spectral importance: visible (VIS) bands take importance during the early season stages, whereas near-infrared (NIR) and red-edge (RE) reflectance provide critical diagnostic information throughout the entire crop cycle. Notably, 82% of the evaluated varieties demonstrated a high compatibility with the global model. Conversely, the yield model showed limited transferability between varieties. While it performed poorly on the global dataset, it successfully predicted yields for 48.8% of commercial varieties (residues within ± 1 tons per hectare), specifically those with genetic and structural similarities to the training variety. Conclusion The study concludes that N-status monitoring via RS classifiers is robust across varying rice genetics, whereas yield prediction models exhibit strong genotype dependency.","Precision Agriculture","2026-08-29T00:00:00Z",75,{"impact":70,"substance":71,"depth":70,"authority":72,"freshness":73,"relevant":21,"comment":74},18,22,14,3,"研究揭示遥感氮素监测跨基因型稳健，但产量预测依赖品种，对精准农业实践有重要参考。",[76],{"name":66,"url":64},[78,26,27,28,79],"智慧农业","氮素监测",[81,82],"产量预测 智慧农业 氮素监测 水稻","产量预测 智慧农业","产量预测智慧农业氮素监测水稻-1210","10.1007\u002Fs11119-026-10432-8",{"doi":84,"openalex_id":86,"authors":87,"venue":66,"cited_by_count":35,"oa_url":106,"card":107,"direction":112,"ingested_from":56},"W7204667424",[88,90,93,95,97,99,102,104],{"name":89,"orcid":9},"Fàtima Della-Bellver",{"name":91,"orcid":92},"B. Franch","https:\u002F\u002Forcid.org\u002F0000-0003-0593-7874",{"name":94,"orcid":9},"Javier Tarín-Mestre",{"name":96,"orcid":9},"César José Guerrero-Benavent",{"name":98,"orcid":9},"Concha Domingo",{"name":100,"orcid":101},"Mar Català Forner","https:\u002F\u002Forcid.org\u002F0000-0002-1026-7097",{"name":103,"orcid":9},"Karen Marti-Jerez",{"name":105,"orcid":9},"Luis Marqués","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs11119-026-10432-8.pdf",{"tldr":108,"method":109,"finding":110,"direction":54,"opportunity":111},"评估遥感模型在不同水稻基因型间的可转移性，用于氮肥监测和产量预测。","多站点田间试验，无人机多光谱影像，GDD归一化，随机森林分类器，产量回归模型。","氮监测模型跨基因型有效，但产量模型转移性有限，仅对相似品种有效。","可研究开发基于基因型特征的通用产量预测模型，或结合机器学习提升跨品种适应性。","智慧农业 \u002F 农业物联网","2026-09-01T04:03:03.696050Z",{"id":115,"title":116,"url":117,"summary":118,"summary_zh":119,"content":9,"source_name":120,"source_url":117,"published_at":121,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":122,"score_detail":123,"sources":127,"tags":129,"search_phrases":132,"slug":135,"view_count":35,"doi":136,"paper":137,"created_at":155},3177,"A comparative analysis on maize yield prediction using sentinel 2A and Landsat 8 satellite image in Sundarganj, Gaibandha, Bangladesh","https:\u002F\u002Fdoi.org\u002F10.3329\u002Fbjar.v51i1.92530","Maize is an important cereal crops in Bangladesh. Over the last two decades, its cultivation has increased promisingly, especially in the Northern part of Bangladesh. The effective estimation of crop yields at a regional scale holds significant importance in facilitating decision-making within the agricultural sector, thereby ensuring grain security. The traditional ground-based measurement techniques suffer from inefficiencies, and there exists a need for a reliable, precise, and effective method for estimating regional crop yields. This study used remote sensing (RS) techniques for forecasting pre-harvest maize yield to improve the management system. Currently, the normalized difference vegetation index (NDVI) is widely used to predict crop yield including maize. However, the present study used Landsat 8 (~ 30 m) and Sentinel 2A (~ 10 m) high resolution data for 2018-2019 and 2019-2020 to predict maize yield based on the year 2020-2021 at Sundarganj Upazila in Gaibandha district. The single cloud free image acquisition date based on maximum NDVI for both satellite images was used for each maize growing period to develop a yield prediction model. A regression model was performed between NDVI values and 20 farmers field-level maize yields. The absolute mean error of prediction was about 10.30% for Landsat 8 and 6.70% for Sentinel 2A compared to the actual maize yield during 2020-2021. The study revealed that NDVI data extracted from Sentinel 2A high resolution satellite images can be successfully used to predict the maize yield with appreciable accuracy. Finally, this study has demonstrated the efficacy of combining multi-temporal remote sensing data for accurate maize yield estimation, aiding agricultural authorities and production enterprises in the timely formulation and refinement of cropping strategies and management policies for the ongoing season. Bangladesh J. Agril. Res. 51(1): 501-521, March 2026","玉米是孟加拉国重要的谷类作物。过去二十年间，其种植面积增长显著，尤其是在孟加拉国北部地区。在区域尺度上有效估算作物产量对于促进农业部门决策、进而保障粮食安全具有重要意义。传统的地面测量技术效率低下，亟需一种可靠、精确且有效的区域作物产量估算方法。本研究采用遥感（RS）技术预测收获前玉米产量，以改进管理体系。目前，归一化植被指数（NDVI）被广泛用于预测包括玉米在内的作物产量。然而，本研究利用Landsat 8（约30 m）和Sentinel 2A（约10 m）高分辨率数据，基于2018—2019年和2019—2020年的数据，对盖班达县孙达尔甘杰乌帕齐拉2020—2021年的玉米产量进行预测。在每个玉米生长期，选取两颗卫星影像中NDVI最大值对应的单幅无云影像获取日期，用于建立产量预测模型。对NDVI值与20户农民田块级玉米产量进行回归建模。与2020—2021年实际玉米产量相比，Landsat 8的绝对平均预测误差约为10.30%，Sentinel 2A约为6.70%。研究表明，利用Sentinel 2A高分辨率卫星影像提取的NDVI数据可成功用于预测玉米产量，且精度令人满意。最后，本研究证明了结合多时相遥感数据进行准确玉米产量估算的有效性，有助于农业主管部门和生产企业在当季及时制定和完善种植策略与管理政策。Bangladesh J. Agril. Res. 51(1): 501-521, March 2026","Bangladesh Journal of Agricultural Research","2026-09-21T00:00:00Z",65,{"impact":124,"substance":18,"depth":125,"authority":20,"freshness":124,"relevant":21,"comment":126},8,16,"基于Sentinel 2A与Landsat 8的玉米遥感估产对比研究，方法清晰、误差数据具体，对遥感估产有参考价值，但属区域小尺度研究，公共影响有限。",[128],{"name":120,"url":117},[78,27,130,28,131],"玉米","NDVI",[133,134],"Sentinel 2A Landsat 8 玉米产量预测","孟加拉国 Sundarganj 玉米遥感估产","Sentinel2ALandsat8玉米产量预测-3177","10.3329\u002Fbjar.v51i1.92530",{"doi":136,"openalex_id":138,"authors":139,"venue":120,"cited_by_count":35,"oa_url":117,"card":150,"direction":54,"ingested_from":56},"W7213918280",[140,142,144,146,148],{"name":141,"orcid":9},"N Mohammad",{"name":143,"orcid":9},"MA Islam",{"name":145,"orcid":9},"MG Mahboob",{"name":147,"orcid":9},"MM Rahman",{"name":149,"orcid":9},"I Ahmed",{"tldr":151,"method":152,"finding":153,"direction":54,"opportunity":154},"用Sentinel 2A与Landsat 8的NDVI回归模型预测孟加拉国玉米产量并比较精度。","基于最大NDVI单期影像与20个农户地块产量做回归，比较两种卫星。","Sentinel 2A预测绝对平均误差6.70%，优于Landsat 8的10.30%。","可探索多时相NDVI与机器学习融合，提升小农户尺度玉米估产精度与迁移性。","2026-09-22T23:30:23.551878Z",{"id":157,"title":158,"url":159,"summary":160,"summary_zh":161,"content":9,"source_name":162,"source_url":159,"published_at":121,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":163,"score_detail":164,"sources":167,"tags":169,"search_phrases":172,"slug":175,"view_count":35,"doi":176,"paper":177,"created_at":193},3164,"Spatiotemporal Deep Learning for Rice Plant Height Estimation from Multi-Temporal UAV RGB Imagery","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16182034","Accurate plant height estimation is important for monitoring crop growth and supporting precision agricultural management. Manual measurements are labor-intensive, while LiDAR-based methods are expensive and require complex processing. UAV photogrammetry provides a lower-cost alternative but remains challenging in flooded rice paddies because of canopy deformation and difficulties in terrain extraction. This study proposes Rice-STNet, a spatiotemporal deep learning framework for end-to-end rice plant height estimation using multi-temporal UAV RGB imagery. Rice-STNet integrates a convolutional neural network for spatial feature extraction, Time2Vec for temporal encoding, and a gated recurrent unit network for modeling temporal dependencies across observation dates. The framework was evaluated using field data collected from rice paddies over two growing seasons. Rice-STNet achieved an R2 of 0.97, a root mean squared error of 1.97 cm, and a mean absolute error of 1.14 cm. It outperformed random forest, support vector regression, a CNN-only baseline, and a UAV photogrammetry-based point-cloud approach. In addition, the framework generated high-resolution plant height maps for field-scale analysis of spatial growth variability. These results underscore the importance of jointly modeling spatial and temporal characteristics for continuously evolving crop traits. The proposed framework offers an accurate, scalable, and non-destructive solution for large-scale crop phenotyping and precision agriculture.","准确的株高估算对于监测作物生长和支持精准农业管理具有重要意义。人工测量劳动强度大，而基于激光雷达（LiDAR）的方法成本高昂且需要复杂的处理。无人机摄影测量提供了一种成本较低的替代方案，但在淹水稻田中仍面临挑战，原因在于冠层变形和地形提取困难。本研究提出了Rice-STNet，一种时空深度学习框架，用于利用多时相无人机RGB影像进行端到端水稻株高估算。Rice-STNet集成了用于空间特征提取的卷积神经网络、用于时间编码的Time2Vec，以及用于建模观测日期之间时间依赖关系的门控循环单元网络。该框架利用两个生长季从稻田采集的田间数据进行了评估。Rice-STNet取得了R²为0.97、均方根误差为1.97 cm、平均绝对误差为1.14 cm的结果。其性能优于随机森林、支持向量回归、仅使用CNN的基线方法以及基于无人机摄影测量的点云方法。此外，该框架生成了高分辨率株高图，用于田块尺度空间生长变异性分析。这些结果凸显了联合建模空间与时间特征对于持续变化的作物性状的重要性。所提出的框架为大规模作物表型分析和精准农业提供了一种准确、可扩展且非破坏性的解决方案。","Agriculture",80,{"impact":70,"substance":71,"depth":70,"authority":20,"freshness":165,"relevant":21,"comment":166},9,"提出时空深度学习框架Rice-STNet，用多时相无人机RGB影像实现水稻株高高精度估算，方法新颖、数据跨两个生长季，对作物表型与精准农业有实用价值。",[168],{"name":162,"url":159},[78,170,26,28,171],"农业人工智能","作物表型",[173,174],"无人机 RGB 水稻株高","Rice-STNet 水稻表型","无人机RGB水稻株高-3164","10.3390\u002Fagriculture16182034",{"doi":176,"openalex_id":178,"authors":179,"venue":162,"cited_by_count":35,"oa_url":159,"card":188,"direction":54,"ingested_from":56},"W7213887432",[180,183,185],{"name":181,"orcid":182},"Weiguo Wang","https:\u002F\u002Forcid.org\u002F0009-0003-4028-9363",{"name":184,"orcid":9},"Noboru Noguchi",{"name":186,"orcid":187},"Liangliang Yang","https:\u002F\u002Forcid.org\u002F0000-0002-5055-3987",{"tldr":189,"method":190,"finding":191,"direction":54,"opportunity":192},"提出Rice-STNet时空深度学习框架，用多时相无人机RGB影像估算水稻株高。","CNN提取空间特征，Time2Vec编码时间，GRU建模时序依赖，两季稻田数据验","R²达0.97、RMSE 1.97cm，优于随机森林、SVR、纯CNN及点云方法。","可迁移至其他作物与多源遥感融合，探索轻量化模型及实时田间部署。","2026-09-22T23:30:18.545958Z",{"id":195,"title":196,"url":197,"summary":198,"summary_zh":199,"content":9,"source_name":200,"source_url":197,"published_at":201,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":202,"sources":205,"tags":207,"search_phrases":209,"slug":212,"view_count":35,"doi":213,"paper":214,"created_at":228},3009,"AgriMAC: An Attention Based Multimodal Deep Clustering Framework for Rice Health Assessment","https:\u002F\u002Fdoi.org\u002F10.22266\u002Fijies2026.1031.06","Rice is Indonesia's staple crop, yet its productivity has declined in recent years because pest and disease outbreaks remain difficult to detect at an early stage.Existing precision agriculture approaches commonly process Internet of Things (IoT) sensor data and remote sensing imagery independently and often rely on supervised learning, requiring large amounts of labeled data.Meanwhile, multispectral drone imagery producing the Normalized Difference Vegetation Index (NDVI) provides richer information on crop physiological conditions than RGB-based vegetation indices.This study proposes Agricultural Multimodal Attention Clustering (AgriMAC), an unsupervised framework that integrates UAV derived NDVI imagery, 7-in-1 IoT soil sensor measurements, and historical weather data from the Open-Meteo API for rice field condition monitoring.Each modality is encoded using a dedicated autoencoder and fused through an entropy-regularized attention mechanism before Deep Embedded Clustering is performed.To reduce the influence of crop growth stage, the IoT representation is residualized using growth-phase statistics estimated exclusively from the training fold, enabling the discovered clusters to represent within-phase agronomic conditions rather than crop age.Experiments conducted under a grouped leave-one-field-out protocol produced a Silhouette Score of 0.465 ± 0.048, a Davies Bouldin Index of 0.807 ± 0.036, and a Calinski Harabasz Index of 195 ± 27.The learned groups also showed low normalized mutual information with growth phase (0.079 ± 0.043) and near chance phase decodability (balanced accuracy = 0.554 ± 0.042), indicating that they are only weakly associated with crop growth stage.The learned attention weights identified IoT soil measurements (0.570 ± 0.024) as the dominant modality, while NDVI imagery (0.210 ± 0.014) and weather information (0.220 ± 0.014) provided complementary spatial and temporal context.Overall, AgriMAC provides an interpretable and leakage-aware framework for multimodal clustering of rice field conditions.Although its clustering performance is comparable to that of a capacity-matched IoT-only model, it additionally quantifies the contribution of each sensing modality through attention weights and explicitly mitigates the growth-phase confound, making it suitable for field level agronomic condition monitoring and spatial decision support in precision agriculture.","水稻是印度尼西亚的主要作物，但近年来其生产力有所下降，因为病虫害暴发在早期阶段仍难以检测。现有的精准农业方法通常独立处理物联网（IoT）传感器数据和遥感影像，且往往依赖监督学习，需要大量标注数据。与此同时，生成归一化植被指数（NDVI）的多光谱无人机影像比基于RGB的植被指数能提供更丰富的作物生理状况信息。本研究提出农业多模态注意力聚类（AgriMAC），这是一个无监督框架，整合了无人机获取的NDVI影像、七合一IoT土壤传感器测量数据以及来自Open-Meteo API的历史天气数据，用于稻田状况监测。每种模态均使用专用自编码器进行编码，并通过熵正则化注意力机制进行融合，随后执行深度嵌入聚类。为减少作物生长阶段的影响，IoT表征利用仅从训练折估计的生长阶段统计量进行残差化处理，使发现的聚类能够表征阶段内的农艺状况而非作物年龄。在分组留一田块协议下进行的实验产生了0.465 ± 0.048的轮廓系数、0.807 ± 0.036的Davies-Bouldin指数和195 ± 27的Calinski-Harabasz指数。学习到的分组还显示出与生长阶段的低归一化互信息（0.079 ± 0.043）以及接近随机的阶段可解码性（平衡准确率 = 0.554 ± 0.042），表明它们与作物生长阶段仅存在弱关联。学习到的注意力权重将IoT土壤测量（0.570 ± 0.024）识别为主导模态，而NDVI影像（0.210 ± 0.014）和天气信息（0.220 ± 0.014）则提供了互补的空间和时间背景。总体而言，AgriMAC为稻田状况的多模态聚类提供了一个可解释且感知数据泄漏的框架。尽管其聚类性能与容量匹配的仅IoT模型相当，但它还通过注意力权重量化了每种传感模态的贡献，并明确减轻了生长阶段混杂因素，使其适用于田块级农艺状况监测和精准农业中的空间决策支持。","International journal of intelligent engineering and systems","2026-09-19T00:00:00Z",{"impact":17,"substance":203,"depth":19,"authority":20,"freshness":165,"relevant":21,"comment":204},21,"提出无监督多模态注意力聚类框架，融合无人机NDVI、IoT土壤与气象数据评估水稻健康，方法新颖且实验严谨，对精准农业田间监测有参考价值。",[206],{"name":200,"url":197},[78,170,26,208,28],"多模态融合",[210,211],"AgriMAC 水稻 多模态聚类","无人机 NDVI 水稻 病害监测","AgriMAC水稻多模态聚类-3009","10.22266\u002Fijies2026.1031.06",{"doi":213,"openalex_id":215,"authors":216,"venue":200,"cited_by_count":35,"oa_url":197,"card":223,"direction":112,"ingested_from":56},"W7213619014",[217,219,221],{"name":218,"orcid":9},"Nurfadhilah Mardianti Andini",{"name":220,"orcid":9},"Mike Yuliana",{"name":222,"orcid":9},"Moch. Zen Samsono Hadi",{"tldr":224,"method":225,"finding":226,"direction":112,"opportunity":227},"提出无监督多模态聚类框架AgriMAC，融合无人机NDVI、IoT土壤与气象数据评估水稻健康。","各模态自编码器编码，熵正则注意力融合，深度嵌入聚类，按生长阶段残差化。","聚类性能与仅IoT模型相当，但注意力权重可解释模态贡献并弱化生长阶段混淆。","可探索注意力融合机制在更多作物与传感器组合下的泛化性，并引入时序动态聚类。","2026-09-20T23:30:08.419613Z",{"id":230,"title":231,"url":232,"summary":233,"summary_zh":234,"content":9,"source_name":235,"source_url":232,"published_at":236,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":237,"score_detail":238,"sources":240,"tags":242,"search_phrases":246,"slug":249,"view_count":35,"doi":250,"paper":251,"created_at":267},2951,"Comparative Analysis of Geographical Factors Affecting Paddy (Oryza sativa L.) Yields in Türkiye Using Random Forest and ANOVA: The Case of Kırıkkale, Balıkesir, Diyarbakır and Şanlıurfa","https:\u002F\u002Fdoi.org\u002F10.24925\u002Fturjaf.v14i9.2678-2694.8977","Rice (Oryza sativa L.) is a staple food for nearly half of the global population and a strategic crop for Turkey, where inter-provincial yield disparities remain pronounced. This study aims to classify provincial rice yield levels in Turkey for the 2004–2024 period using TurkStat data and to quantify the relative contribution of 14 environmental, edaphic and agronomic parameters driving these differences. Preliminary analyses identified Kırıkkale (21-year mean 908.3 kg\u002Fda) as the high-yield province, Balıkesir (747.7 kg\u002Fda) as the medium-yield province, and Diyarbakır (448.1 kg\u002Fda) and Şanlıurfa (440.5 kg\u002Fda) as the low- and lowest-yield provinces, respectively. A 14-parameter dataset compiled from field measurements and published province-level studies was analysed using Principal Component Analysis (PCA), Random Forest (RF) classification and one-way Analysis of Variance (ANOVA).With a 70\u002F30 train\u002Ftest split, the RF model achieved 93.47% accuracy, 0.9764 ROC-AUC and a mean variance of 0.0145. Gini-based variable importance ranked soil moisture, organic matter, soil pH, rainfall and temperature as the most influential drivers of yield, and ANOVA confirmed statistically significant differences across yield classes for these variables (all p \u003C 0.001). Findings indicate that low yields in south-eastern Anatolia are largely driven by inadequate soil moisture management, low organic matter content, elevated soil pH and summer heat stress, whereas Kırıkkale’s high yields are associated with more balanced soil–water relations. Results provide evidence-based guidance for region-specific rice production policies and data-driven decision support in Türkiye.","水稻（Oryza sativa L.）是全球近半数人口的主粮，也是土耳其的战略性作物，但该国各省之间的产量差异依然显著。本研究旨在利用土耳其统计局（TurkStat）数据，对2004—2024年期间土耳其各省水稻产量水平进行分类，并量化14项环境、土壤和农艺参数对上述差异的相对贡献。初步分析确定，Kırıkkale省（21年均值908.3 kg\u002Fda）为高产区，Balıkesir省（747.7 kg\u002Fda）为中产区，Diyarbakır省（448.1 kg\u002Fda）和Şanlıurfa省（440.5 kg\u002Fda）分别为低产区和最低产区。基于田间实测数据和已发表的省级研究，构建了包含14项参数的数据集，并采用主成分分析（PCA）、随机森林（RF）分类和单因素方差分析（ANOVA）进行分析。在70\u002F30的训练\u002F测试集划分下，RF模型达到了93.47%的准确率、0.9764的ROC-AUC值以及0.0145的平均方差。基于基尼系数的变量重要性排序显示，土壤水分、有机质、土壤pH、降雨量和温度是影响产量最重要的驱动因素，ANOVA证实这些变量在不同产量类别间均存在统计学显著差异（均p \u003C 0.001）。研究结果表明，安纳托利亚东南部地区的低产主要归因于土壤水分管理不足、有机质含量低、土壤pH偏高以及夏季高温胁迫，而Kırıkkale省的高产则与更为均衡的土壤—水分关系有关。研究结果为土耳其制定区域特异性水稻生产政策和数据驱动的决策支持提供了循证依据。","Turkish Journal of Agriculture - Food Science and Technology","2026-09-17T00:00:00Z",74,{"impact":17,"substance":71,"depth":70,"authority":20,"freshness":165,"relevant":21,"comment":239},"基于21年省级数据与随机森林、ANOVA量化水稻产量驱动因子，方法规范、结论可靠，但属土耳其区域研究，对国内三农实践参考价值有限。",[241],{"name":235,"url":232},[26,27,243,244,245],"农业大数据","精准农业","土壤墒情",[247,248],"土耳其 水稻 产量 随机森林","Kırıkkale Balıkesir 水稻 产量","土耳其水稻产量随机森林-2951","10.24925\u002Fturjaf.v14i9.2678-2694.8977",{"doi":250,"openalex_id":252,"authors":253,"venue":235,"cited_by_count":35,"oa_url":260,"card":261,"direction":54,"ingested_from":56},"W7213465585",[254,257],{"name":255,"orcid":256},"Mehmet ÖZCANLI","https:\u002F\u002Forcid.org\u002F0000-0003-2228-8298",{"name":258,"orcid":259},"Kerim Karadağ","https:\u002F\u002Forcid.org\u002F0000-0001-5167-4054","https:\u002F\u002Fwww.agrifoodscience.com\u002Findex.php\u002FTURJAF\u002Farticle\u002Fdownload\u002F8977\u002F4317",{"tldr":262,"method":263,"finding":264,"direction":265,"opportunity":266},"用随机森林和方差分析比较土耳其四省水稻产量差异，识别关键地理驱动因子。","基于2004–2024年TurkStat数据，用PCA、随机森林分类和单因素AN","土壤水分、有机质、pH、降雨和温度是产量主因；东南部低产源于土壤水分不足、有机质低、pH高和夏季热胁","农业人工智能与决策模型","可引入时序遥感与土壤传感器数据，构建跨区域可迁移的产量预测与精准水肥管理模型。","2026-09-19T23:30:34.632573Z",{"id":269,"title":270,"url":271,"summary":272,"summary_zh":273,"content":9,"source_name":274,"source_url":271,"published_at":275,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":237,"score_detail":276,"sources":278,"tags":280,"search_phrases":283,"slug":286,"view_count":35,"doi":287,"paper":288,"created_at":299},2667,"A systematic review of remote sensing applications for agricultural water management in a water-stressed South Africa","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43621-026-04728-y","Abstract Recent advancements in earth observation and computational technologies have enabled the prudent use of remote sensing (RS) for monitoring and managing water resources. RS is a spatially explicit and effective approach for field-level to large-scale monitoring through data-driven insights on key agricultural water management (AWM) aspects of water consumption, productivity, agricultural drought and irrigated area extent mapping. However, the progress made in the South African context is not well documented. Hence, this review aimed to assess how RS is utilized for AWM in South Africa. Article search was conducted in the Scopus and Web of Science databases using keywords remote sensing, earth observation, agricultural water management, irrigation, evapotranspiration, water use, water productivity, agricultural drought, and South Africa. The bibliometric analysis from 165 articles revealed that number of publications have a 8.3% annual growth over 26 years. RS data have been utilized to assess evapotranspiration (ET), crop water productivity (CWP), agricultural drought, scheme performance, and irrigation water quality. Data limitations, insufficient technical capacity, and limited computational infrastructure were the notable challenges. Future research should focus on integrated use of multi-temporal and multi-source data to minimize gaps across sensors and improve accuracy. Overall, RS has substantial potential to address water management issues in the agricultural sector of South Africa. Addressing the challenges through continued innovation, data integration, and capacity building is key to utilizing RS for sustainable agriculture and water security.","摘要 地球观测与计算技术的最新进展使得遥感（RS）能够被审慎地应用于水资源监测与管理。遥感是一种空间显式且有效的方法，可通过数据驱动的洞察，从田块尺度到大规模监测农业水资源管理（AWM）的关键方面，包括耗水量、生产力、农业干旱及灌溉面积范围制图。然而，南非在这一领域的进展尚未得到充分记录。因此，本综述旨在评估遥感在南非农业水资源管理中的应用情况。研究在Scopus和Web of Science数据库中使用遥感、地球观测、农业水资源管理、灌溉、蒸散发、用水量、水分生产力、农业干旱和南非等关键词进行文献检索。对165篇文章的文献计量分析显示，过去26年间发表数量年增长率为8.3%。遥感数据已被用于评估蒸散发（ET）、作物水分生产力（CWP）、农业干旱、灌区性能及灌溉水质。数据局限性、技术能力不足和计算基础设施有限是显著挑战。未来研究应侧重于多时相和多源数据的集成使用，以缩小传感器之间的差距并提高精度。总体而言，遥感在解决南非农业部门水资源管理问题方面具有巨大潜力。通过持续创新、数据集成和能力建设来应对这些挑战，是利用遥感实现可持续农业和水安全的关键。","Discover Sustainability","2026-09-15T00:00:00Z",{"impact":125,"substance":18,"depth":19,"authority":20,"freshness":124,"relevant":21,"comment":277},"以南非为对象的遥感农业水资源管理系统性综述，方法规范、数据规模明确，对干旱区灌溉与遥感应用有参考价值，但属区域案例研究，公共影响层级有限。",[279],{"name":274,"url":271},[78,281,28,30,282],"农业水资源","农业干旱",[284,285],"农业水资源 农业干旱 智慧农业 灌溉管理","农业水资源 农业干旱","农业水资源农业干旱智慧农业灌溉管理-2667","10.1007\u002Fs43621-026-04728-y",{"doi":287,"openalex_id":289,"authors":290,"venue":274,"cited_by_count":35,"oa_url":271,"card":294,"direction":54,"ingested_from":56},"W7213266434",[291],{"name":292,"orcid":293},"Yilkal Gebeyehu Mekonnen","https:\u002F\u002Forcid.org\u002F0000-0001-9450-1081",{"tldr":295,"method":296,"finding":297,"direction":54,"opportunity":298},"系统综述遥感在南非农业水资源管理中的应用进展与挑战。","Scopus与Web of Science检索165篇文献，做文献计量分析。","遥感用于蒸散、水分生产力、干旱与灌溉监测，但受数据与能力限制。","多源多时相遥感数据融合以填补传感器空缺、提升精度，是值得切入的方向。","2026-09-16T23:30:29.241938Z"]