[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3167":3,"related-3167":120},{"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":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":48,"search_phrases":87,"slug":90,"view_count":82,"doi":91,"paper":92,"created_at":119},3167,"Temporal trend analysis and multi-temporal satellite feature integration for mango orchard acreage estimation using machine learning algorithms approach","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-65828-3","Accurate estimation of horticultural crop acreage is essential for agricultural planning, market forecasting and evidence-based policy formulation. The present study investigated long-term trends in mango cultivation and developed a multi-temporal remote sensing framework for mango orchard acreage estimation in Navsari District using integrated optical, SAR and machine learning approaches. Time-series (temporal) data spanning 23 years (2001–02 to 2023–24) were analyzed using polynomial regression models to evaluate trends in mango area and production. Linear regression best represented area expansion trends (Adj. R² = 0.969), whereas cubic regression better captured production variability (Adj. R² = 0.634), indicating climatic and seasonal influences on productivity. For orchard classification and acreage estimation, multi-temporal Sentinel-2 imagery acquired from November 2023 to March 2024 was processed within a phenology-guided framework. Monthly composites were generated and integrated with Sentinel-1 SAR backscatter data, vegetation indices (NDVI, GNDVI, NDRE, SAVI, EVI and NDMI) and texture metrics derived from Gray Level Co-occurrence Matrix (GLCM) analysis. A comprehensive 72-band feature stack was developed for classification. Four machine learning algorithms, namely Random Forest (RF), XGBoost (XGB), Support Vector Machine (SVM) and Multinomial Logistic Regression (MNLR) were evaluated for orchard discrimination. Among the tested models, RF achieved the highest classification performance with an Overall Accuracy of 99.80% and a Kappa coefficient of 0.997, followed by SVM (99.30%), MNLR (98.21%) and XGB (9.20%). The RF model estimated mango orchard area at 36,099.94 ha, showing the closest agreement with official horticultural statistics (34,363 ha) with only 5.05% estimation error. In contrast, SVM and MNLR overestimated orchard extent by 16.03% and 43.37%, respectively. The proposed framework provides a reliable and scalable methodology for operational horticultural monitoring, crop inventory generation and precision agricultural planning in tropical orchard ecosystems.","准确估算园艺作物种植面积对于农业规划、市场预测和循证政策制定至关重要。本研究探讨了芒果种植的长期趋势，并开发了一个多时相遥感框架，结合光学、合成孔径雷达（SAR）和机器学习方法，用于纳夫萨里县芒果园种植面积估算。利用多项式回归模型分析了跨越23年（2001—02年至2023—24年）的时间序列数据，以评估芒果面积和产量的变化趋势。线性回归最能表征面积扩张趋势（调整R² = 0.969），而三次回归更能捕捉产量变异性（调整R² = 0.634），表明气候和季节性因素对生产力具有影响。在果园分类和面积估算方面，基于物候指导框架处理了2023年11月至2024年3月获取的多时相Sentinel-2影像。生成了月度合成影像，并将其与Sentinel-1 SAR后向散射数据、植被指数（NDVI、GNDVI、NDRE、SAVI、EVI和NDMI）以及基于灰度共生矩阵（GLCM）分析提取的纹理指标进行整合。构建了一个包含72个波段的综合特征集用于分类。评估了四种机器学习算法，即随机森林（RF）、XGBoost（XGB）、支持向量机（SVM）和多项逻辑回归（MNLR），用于果园判别。在测试的模型中，RF取得了最高的分类性能，总体精度为99.80%，Kappa系数为0.997，其次是SVM（99.30%）、MNLR（98.21%）和XGB（9.20%）。RF模型估算的芒果园面积为36,099.94公顷，与官方园艺统计数据（34,363公顷）最为接近，估算误差仅为5.05%。相比之下，SVM和MNLR分别高估了果园面积16.03%和43.37%。所提出的框架为热带果园生态系统中的业务化园艺监测、作物清单生成和精准农业规划提供了一种可靠且可扩展的方法。",null,"Scientific Reports","2026-09-22T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":13,"relevant":20,"comment":21},14,22,18,1,"方法扎实、数据规模大且精度高，但属区域性作物遥感估产研究，产业影响有限，可作为技术方法类精选。",[23],{"name":10,"url":6},[25,26,27,28,29],"智慧农业","机器学习","芒果","遥感监测","作物估产",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[37,43],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"code":44,"slug":45,"name":46,"path":47,"parent_code":42},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":49,"locked":14,"version":50,"method":51,"evidence":52},"classified","2026-10-22.2","rules",{"candidates":53,"agricultural_evidence":56},[54,61,66,73,80],{"code":31,"score":55,"supported":56,"title":57,"abstract":59,"tags":60},15,true,[58],"orchard",[27,58],[27],{"code":38,"score":55,"supported":56,"title":62,"abstract":64,"tags":65},[63],"machine learning",[26,63],[26],{"code":44,"score":67,"supported":56,"title":68,"abstract":69,"tags":72},8,[],[70,71],"遥感","remote sensing",[29,28],{"code":74,"score":75,"supported":56,"title":76,"abstract":77,"tags":79},"I1",4,[],[78],"精准农业",[25],{"code":81,"score":82,"supported":56,"title":83,"abstract":84,"tags":86},"J1",3,[],[85],"农业规划",[],[88,89],"Navsari 芒果 遥感估产","Sentinel-2 芒果 果园面积","Navsari芒果遥感估产-3167","10.1038\u002Fs41598-026-65828-3",{"doi":91,"openalex_id":93,"authors":94,"venue":10,"cited_by_count":111,"oa_url":6,"card":112,"direction":116,"ingested_from":118},"W7213920056",[95,97,100,102,104,107,109],{"name":96,"orcid":9},"V. Raju",{"name":98,"orcid":99},"Yogesh A. Garde","https:\u002F\u002Forcid.org\u002F0000-0002-0297-316X",{"name":101,"orcid":9},"Dr. V. S. Thorat",{"name":103,"orcid":9},"V. T. Shinde",{"name":105,"orcid":106},"Nitin Varshney","https:\u002F\u002Forcid.org\u002F0000-0001-9144-5475",{"name":108,"orcid":9},"Alok Shrivastava",{"name":110,"orcid":9},"A. P. Chaudhary",0,{"tldr":113,"method":114,"finding":115,"direction":116,"opportunity":117},"融合多时相Sentinel-1\u002F2与机器学习，估算印度芒果园面积并分析23年种植趋势。","23年时序回归分析；Sentinel-2月合成+SAR+植被指数+GLCM纹理共","RF精度最高（总体精度99.80%，Kappa 0.997），面积估算误差仅5.05%，优于SVM和","农业遥感与作物表型","可迁移该多时相SAR-光学特征框架至其他热带果园，并探索深度学习与物候自适应特征优化。","openalex","2026-09-22T23:30:22.619289Z",{"total":121,"page":20,"page_size":121,"items":122},6,[123,204,299,390,463,546],{"id":124,"title":125,"url":126,"summary":127,"summary_zh":128,"content":9,"source_name":129,"source_url":126,"published_at":130,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":131,"score_detail":132,"sources":138,"tags":140,"primary_topic":144,"secondary_topics":145,"classification":148,"search_phrases":177,"slug":180,"view_count":20,"doi":181,"paper":182,"created_at":203},4808,"Early Detection of Apple Scab in Orchards and Mildew in Vineyards Using A Novel Deep Learning Model and Multispectral Imagery","https:\u002F\u002Fdoi.org\u002F10.15832\u002Fankutbd.1898342","Agricultural diseases are a significant problem for growers as they cause substantial damage to fruits and leaves, leading to yield losses. Therefore, developing systems for early detection of plant diseases is a crucial issue. In this study, a new deep learning-based hybrid model is presented for the detection and segmentation of plant diseases, specifically apple scab disease in apple orchards and mildew disease in vineyards. Multispectral imagery data collected in agricultural areas of Tekirdag province in Turkey via drones between May 2023 and December 2024 were used in developing the early disease detection system. The datasets contain 3625 images of apple orchards and 5625 images of vineyards. DJI Phantom 4 Multispectral drone, equipped with five spectral bands (Blue, Green, Red, Red Edge, and Near Infrared), was utilized to capture both spectral richness and temporal progression. This study introduces a novel deep learning architecture developed specifically for time series-based disease detection and segmentation using multispectral drone imagery. Changes in plants over time provide important information about the occurrence of plant diseases. For this reason, time series were used in this study to extract features from the changes in plants over time. The proposed model architecture was specifically designed to leverage time series information, enabling robust tracking of crop health changes over time. The model trained with the dataset collected for apple diseases achieved an average Intersection Over Unit (IoU) value of 82.05%, while the model trained with the dataset collected for vineyard diseases reached an IoU value of 79.67%. The findings highlight the effectiveness of integrating drone-acquired multispectral time-series data with advanced deep learning models for scalable, accurate, and timely plant disease detection in precision agriculture.","农业病害是种植者面临的重大问题，会对果实和叶片造成严重损害，导致产量损失。因此，开发植物病害早期检测系统是一个至关重要的问题。本研究提出了一种基于深度学习的新型混合模型，用于植物病害的检测与分割，具体针对苹果园中的苹果黑星病和葡萄园中的霜霉病。该早期病害检测系统的开发使用了2023年5月至2024年12月期间通过无人机在土耳其泰基尔达省农业区域采集的多光谱影像数据。数据集包含3625幅苹果园图像和5625幅葡萄园图像。所用DJI Phantom 4多光谱无人机配备五个光谱波段（蓝、绿、红、红边和近红外），用于捕捉光谱丰富度和时间变化过程。本研究提出了一种专门开发的深度学习架构，用于基于时间序列的多光谱无人机影像病害检测与分割。植物随时间的变化提供了关于植物病害发生的重要信息。因此，本研究使用时间序列从植物随时间的变化中提取特征。所提出的模型架构专门设计以利用时间序列信息，从而能够稳健地跟踪作物健康状况随时间的变化。使用苹果病害数据集训练的模型达到了82.05%的平均交并比（IoU）值，而使用葡萄园病害数据集训练的模型达到了79.67%的IoU值。研究结果凸显了将无人机获取的多光谱时间序列数据与先进深度学习模型相结合，在精准农业中实现可扩展、准确且及时的植物病害检测方面的有效性。","Tarım Bilimleri Dergisi","2026-10-12T00:00:00Z",77,{"impact":133,"substance":134,"depth":19,"authority":135,"freshness":136,"relevant":20,"comment":137},16,21,13,9,"该研究提出基于多光谱时序影像的新型深度学习模型，实现果园与葡萄园病害早期检测，数据规模大、方法新颖，对精准农业病害预警具有参考价值。",[139],{"name":129,"url":126},[25,141,142,28,143],"无人机","农业人工智能","病害预警",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[146,147],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"code":44,"slug":45,"name":46,"path":47,"parent_code":42},{"status":49,"locked":14,"version":50,"method":51,"evidence":149},{"candidates":150,"agricultural_evidence":56},[151,159,165,171],{"code":31,"score":152,"supported":56,"title":153,"abstract":155,"tags":158},17,[154],"apple",[156,157,154],"苹果","葡萄",[],{"code":38,"score":55,"supported":56,"title":160,"abstract":162,"tags":164},[161],"deep learning",[163,161],"深度学习",[142],{"code":44,"score":166,"supported":56,"title":167,"abstract":169,"tags":170},12,[168],"multispectral",[168],[28],{"code":74,"score":172,"supported":56,"title":173,"abstract":174,"tags":176},7,[],[78,175],"precision agriculture",[25],[178,179],"苹果黑星病 无人机 多光谱","葡萄园 霜霉病 深度学习","苹果黑星病无人机多光谱-4808","10.15832\u002Fankutbd.1898342",{"doi":181,"openalex_id":183,"authors":184,"venue":129,"cited_by_count":111,"oa_url":196,"card":197,"direction":202,"ingested_from":118},"W7221558726",[185,188,191,193],{"name":186,"orcid":187},"Batuhan Aşıroğlu","https:\u002F\u002Forcid.org\u002F0000-0003-0767-6348",{"name":189,"orcid":190},"Aydın Kağan Turkoglu","https:\u002F\u002Forcid.org\u002F0009-0001-8383-8807",{"name":192,"orcid":9},"Fatih Sinan Bir",{"name":194,"orcid":195},"Sibel Senan","https:\u002F\u002Forcid.org\u002F0000-0001-6773-0428","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5761152",{"tldr":198,"method":199,"finding":200,"direction":116,"opportunity":201},"提出一种基于无人机多光谱时序影像的深度学习混合模型，实现苹果黑星病和葡萄霉病的早期检测与分割。","DJI Phantom 4多光谱无人机采集时序影像，构建时序深度学习分割模型。","苹果病IoU达82.05%，葡萄病达79.67%，验证多光谱时序数据有效性。","可探索多作物、多病害的时序多光谱通用模型，并融合气象与地面数据提升早期预警能力。","智慧农业 \u002F 农业物联网","2026-10-13T23:30:10.242467Z",{"id":205,"title":206,"url":207,"summary":208,"summary_zh":209,"content":9,"source_name":210,"source_url":207,"published_at":211,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":212,"score_detail":213,"sources":215,"tags":217,"primary_topic":220,"secondary_topics":221,"classification":224,"search_phrases":271,"slug":274,"view_count":20,"doi":275,"paper":276,"created_at":298},4988,"Hierarchical cross-modal spatiotemporal attention network for crop yield prediction in complex Himalayan agroecosystems","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1933033","Introduction Accurate crop yield prediction is essential for precision agriculture, climate-resilient farm management, and timely decision-making. However, yield prediction remains challenging because of complex, nonlinear, and multi-scale interactions among crop physiology, soil conditions, weather variability, and management practices. Conventional statistical, machine learning, and deep learning approaches, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks, often have limitations in capturing long-range temporal dependencies, hierarchical spatial relationships, heterogeneous data modalities, and predictive uncertainty. Methods This study proposes a Hierarchical Cross-Modal Spatio-Temporal Attention Network (HCM-STAN) for crop yield prediction using a unified attention-based framework for integrating heterogeneous agro-environmental data. The architecture incorporates four principal components: (i) hierarchical spatio-temporal attention operating across tree, block, orchard, and regional scales; (ii) a cross-modal attention module for adaptive fusion of IoT-based soil sensor observations, weather data, plant physiological measurements, and drone-based multispectral imagery; (iii) a soil-aware attention head that explicitly models soil memory, hysteresis effects, and root-zone dynamics; and (iv) an uncertainty-aware prediction module combining Monte Carlo dropout and quantile regression to generate calibrated prediction intervals. The framework was evaluated using a multi-source dataset collected from Himalayan apple orchards during 2023–2026, comprising 1,247 monitored trees across three commercial orchards. Results The proposed HCM-STAN achieved an R 2 of 0.89 for four-week-ahead crop yield prediction, representing a 15.6% improvement over the CNN-LSTM baseline. Furthermore, zero-shot transfer to mango orchards achieved an R 2 of 0.67, corresponding to approximately 75% of the performance obtained under full training. Discussion The results demonstrate that integrating hierarchical spatio-temporal representation learning, cross-modal attention, soil-aware modelling, and uncertainty estimation can effectively address the heterogeneous and dynamic nature of crop yield prediction in complex mountain agroecosystems. The observed zero-shot transfer performance further indicates the potential of HCM-STAN to generalize across crop systems without crop-specific retraining. Overall, the proposed framework provides a promising foundation for reliable, uncertainty-aware yield forecasting and can support precision agriculture, climate-smart farm management, and data-driven decision-making in heterogeneous agroecosystems.","引言 准确的作物产量预测对于精准农业、气候适应性农场管理以及及时决策至关重要。然而，由于作物生理、土壤条件、天气变异和管理措施之间复杂、非线性且多尺度的相互作用，产量预测仍然具有挑战性。传统的统计方法、机器学习以及深度学习方法，包括卷积神经网络（CNN）、循环神经网络（RNN）和长短期记忆（LSTM）网络，在捕捉长程时间依赖关系、层次化空间关系、异构数据模态以及预测不确定性方面往往存在局限。方法 本研究提出了一种用于作物产量预测的层次化跨模态时空注意力网络（HCM-STAN），采用统一的基于注意力的框架来整合异构农业环境数据。该架构包含四个主要组成部分：（i）在树、地块、果园和区域尺度上运行的层次化时空注意力；（ii）用于自适应融合基于物联网的土壤传感器观测、天气数据、植物生理测量和无人机多光谱影像的跨模态注意力模块；（iii）显式建模土壤记忆、滞后效应和根区动态的土壤感知注意力头；（iv）结合蒙特卡洛 dropout 和分位数回归以生成校准预测区间的不确定性感知预测模块。该框架使用2023—2026年在喜马拉雅苹果园采集的多源数据集进行了评估，该数据集包含三个商业果园中1，247棵监测树木。结果 所提出的 HCM-STAN 在提前四周作物产量预测中达到了0.89的 R²，相较于 CNN-LSTM 基线提升了15.6%。此外，零样本迁移至芒果园达到了0.67的 R²，相当于完整训练下所获性能的约75%。讨论 结果表明，整合层次化时空表征学习、跨模态注意力、土壤感知建模和不确定性估计，能够有效应对复杂山地农业生态系统中作物产量预测的异构性和动态性。观察到的零样本迁移性能进一步表明，HCM-STAN 具有在无需作物特定再训练的情况下跨作物系统泛化的潜力。总体而言，该 p","Frontiers in Plant Science","2026-10-14T00:00:00Z",81,{"impact":19,"substance":18,"depth":19,"authority":17,"freshness":136,"relevant":20,"comment":214},"提出分层跨模态时空注意力网络，融合物联网土壤传感、气象、生理与无人机多光谱数据，苹果园四周产量预测R²达0.89并实现芒果园零样本迁移，方法新颖、数据扎实，对智慧农业产量预报有参考价值。",[216],{"name":210,"url":207},[25,142,218,219,70],"产量预测","多模态融合",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},[222,223],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"code":44,"slug":45,"name":46,"path":47,"parent_code":42},{"status":49,"locked":14,"version":50,"method":51,"evidence":225},{"candidates":226,"agricultural_evidence":56},[227,232,236,241,245,252,259,265],{"code":38,"score":228,"supported":56,"title":229,"abstract":230,"tags":231},11,[],[163,26,63,161],[142,219],{"code":31,"score":136,"supported":56,"title":233,"abstract":234,"tags":235},[],[156,27,58,154],[],{"code":44,"score":67,"supported":56,"title":237,"abstract":238,"tags":240},[],[239,168],"无人机多光谱",[218,70],{"code":74,"score":172,"supported":56,"title":242,"abstract":243,"tags":244},[],[78,175],[25],{"code":246,"score":121,"supported":56,"title":247,"abstract":248,"tags":251},"H2",[],[249,250],"土壤","soil",[],{"code":253,"score":121,"supported":56,"title":254,"abstract":255,"tags":258},"I5",[],[256,257],"物联网","IoT",[],{"code":260,"score":82,"supported":56,"title":261,"abstract":262,"tags":264},"H6",[],[263],"农业生态",[],{"code":266,"score":82,"supported":56,"title":267,"abstract":268,"tags":270},"H7",[],[269],"气候适应",[],[272,273],"喜马拉雅 苹果园 产量预测","HCM-STAN 作物产量预测","喜马拉雅苹果园产量预测-4988","10.3389\u002Ffpls.2026.1933033",{"doi":275,"openalex_id":277,"authors":278,"venue":210,"cited_by_count":111,"oa_url":207,"card":292,"direction":202,"ingested_from":118},"W7221795850",[279,282,284,286,288,290],{"name":280,"orcid":281},"Shabir Ahmad Mir","https:\u002F\u002Forcid.org\u002F0000-0003-2311-218X",{"name":283,"orcid":9},"Umarfarooque Momin",{"name":285,"orcid":9},"Sudeep Kumar E",{"name":287,"orcid":9},"Itfaq Ahmad Mir",{"name":289,"orcid":9},"Ab Rouf Khan",{"name":291,"orcid":9},"Ghulam Mohammad Mir",{"tldr":293,"method":294,"finding":295,"direction":296,"opportunity":297},"提出分层跨模态时空注意力网络HCM-STAN，融合多源农业数据预测喜马拉雅山区苹果产量。","用IoT土壤、气象、生理和无人机多光谱数据，结合分层时空与跨模态注意力及不确定性","四周前产量预测R²达0.89，比CNN-LSTM提升15.6%，零样本迁移芒果R²为0.67。","农业人工智能与决策模型","可探索跨作物零样本迁移的领域自适应机制，并验证模型在更多山地农业生态系统的泛化能力。","2026-10-15T23:30:08.750140Z",{"id":300,"title":301,"url":302,"summary":303,"summary_zh":304,"content":9,"source_name":305,"source_url":302,"published_at":306,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":212,"score_detail":307,"sources":310,"tags":312,"primary_topic":315,"secondary_topics":316,"classification":323,"search_phrases":365,"slug":368,"view_count":369,"doi":370,"paper":371,"created_at":389},4575,"A review of remote sensing for agricultural drought: technologies, indices, modelling advances and future prospects","https:\u002F\u002Fdoi.org\u002F10.1080\u002F15481603.2026.2738981","Agricultural drought is a complex hazard that jeopardises food security and progress toward the Sustainable Development Goals. We systematically reviewed peer-reviewed English-language literature published between 2015 and 2025, focusing on Remote Sensing (RS)-driven agricultural drought monitoring and prediction. Eligible studies were identified from the Web of Science and Scopus databases (last searched in August 2025); review articles, duplicates, and irrelevant studies were excluded, resulting in 181 articles. This review critically examined the evolution of sensors, drought indices, modelling approaches, and emerging technologies used to assess agricultural drought across diverse agro-ecological regions. Specifically, we (1) systematically evaluated the use of optical, thermal, and microwave RS platforms, along with satellite-based, model-based, and reanalysis products for agricultural drought monitoring; (2) reviewed widely used RS-based drought indices, including single indices grouped into nine categories, as well as composite indices; (3) reviewed software or programming languages applied; (4) assessed the progression of modelling approaches from traditional statistical and time-series methods to machine learning (ML) and deep learning (DL) frameworks; and (5) examined validation strategies applied to agricultural drought assessment. The analysis reveals a growing research interest in this topic, with a strong geographic concentration in Asia, particularly China. Optical and thermal platforms (e.g. MODIS and Landsat) dominate the literature (~54%) due to their accessibility, followed by satellite-derived precipitation products (17%), while microwave soil-moisture missions remain underutilized (~7%). Regarding RS-based indices, most studies employed more than one index: 44% analysed multiple individual indices, 51% used composite indices (either alone or in combination with individual indices), and only 5% relied on a single index. Vegetation indices, such as NDVI, remain workhorses; however, their sensitivity to soil background effects and canopy saturation has driven increased adoption of thermal composites (e.g. VHI), as well as adaptive weighting schemes and principal component analysis-based approaches. Moreover, computational tools used in drought research can be broadly grouped into geospatial and RS platforms for large-scale data processing and analysis (e.g. QGIS, GEE), and general-purpose programming languages (e.g. R, Python) for model development and advanced analytics. Regarding the methodology, statistical and time-series approaches account for 64% of the reviewed studies, followed by ML models (20%) and DL methods (15%), while explainable artificial intelligence (XAI) remains nascent. Techniques, such as random forests, support vector machines, and CNN and LSTM architectures, generally outperform traditional models but require large training datasets and raise concerns related to interpretability and ethics. In terms of validation, hydro-climatic indices such as SPI and SPEI are most commonly used (32%), followed by crop yield and yield anomaly data (19%). This review further identifies key research gaps across sensors, indices, and modelling approaches and proposes a roadmap for developing equitable, interpretable, and resilient agricultural drought monitoring systems. Emerging opportunities include context-aware and interpretable AI, multi-sensor and multi-scale data fusion across optical, thermal, and microwave domains, integration of physiologically meaningful indicators such as sun-induced chlorophyll fluorescence (SIF), robust uncertainty quantification, and the development of hybrid and physics-informed modelling frameworks.","农业干旱是一种复杂的灾害，威胁粮食安全和可持续发展目标的实现。我们系统综述了2015年至2025年间发表的同行评议英文文献，聚焦于遥感（RS）驱动的农业干旱监测与预测。符合条件的研究通过Web of Science和Scopus数据库检索确定（最后检索时间为2025年8月）；综述文章、重复文献及不相关研究被排除，最终纳入181篇文献。本综述批判性地审视了用于评估不同农业生态区农业干旱的传感器、干旱指数、建模方法及新兴技术的演变。具体而言，我们（1）系统评估了光学、热红外和微波遥感平台，以及基于卫星、基于模型和再分析产品在农业干旱监测中的应用；（2）综述了广泛使用的基于遥感的干旱指数，包括归为九类的单一指数以及复合指数；（3）综述了所应用的软件或编程语言；（4）评估了建模方法从传统统计和时间序列方法向机器学习（ML）和深度学习（DL）框架的演进；（5）考察了应用于农业干旱评估的验证策略。分析显示，该主题的研究兴趣日益增长，地理上高度集中于亚洲，尤其是中国。光学和热红外平台（如MODIS和Landsat）因其可获取性在文献中占主导地位（约54%），其次是卫星降水产品（17%），而微波土壤水分任务仍未得到充分利用（约7%）。在基于遥感的指数方面，大多数研究采用了不止一种指数：44%分析了多个单一指数，51%使用了复合指数（单独使用或与单一指数结合使用），仅5%依赖单一指数。植被指数（如NDVI）仍是主力工具；然而，其对土壤背景效应和冠层饱和的敏感性推动了热红外复合指数（如VHI）以及自适应权重方案和基于主成分分析的方法得到更多采用。此外，干旱研究中使用的计算工具可大致分为用于大规模数据处理和分析的地理空间与遥感平台（如QGIS、GEE），以及用于模型开发的通用编程语言（如R、Python）。","GIScience & Remote Sensing","2026-10-08T00:00:00Z",{"impact":19,"substance":18,"depth":308,"authority":17,"freshness":67,"relevant":20,"comment":309},19,"系统综述181篇文献，梳理遥感干旱监测的传感器、指数与建模进展，方法学价值高，但属学术综述，产业落地影响有限。",[311],{"name":305,"url":302},[25,313,26,28,314],"粮食安全","农业干旱",{"code":44,"slug":45,"name":46,"path":47,"parent_code":42},[317,322],{"code":266,"slug":318,"name":319,"path":320,"parent_code":321},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":49,"locked":14,"version":50,"method":51,"evidence":324},{"candidates":325,"agricultural_evidence":56},[326,330,336,341,347,351,357,361],{"code":44,"score":55,"supported":56,"title":327,"abstract":328,"tags":329},[71],[70,71],[28],{"code":266,"score":17,"supported":56,"title":331,"abstract":333,"tags":335},[332],"drought",[334,332],"干旱",[],{"code":38,"score":13,"supported":56,"title":337,"abstract":338,"tags":340},[],[163,26,63,161,339],"artificial intelligence",[26],{"code":342,"score":172,"supported":56,"title":343,"abstract":344,"tags":346},"A8",[],[313,345],"food security",[313],{"code":246,"score":121,"supported":56,"title":348,"abstract":349,"tags":350},[],[249,250],[],{"code":352,"score":82,"supported":56,"title":353,"abstract":354,"tags":356},"D",[],[355],"水产",[],{"code":260,"score":82,"supported":56,"title":358,"abstract":359,"tags":360},[],[263],[],{"code":74,"score":20,"supported":14,"title":362,"abstract":363,"tags":364},[],[],[25],[366,367],"遥感 农业干旱 监测","GIScience Remote Sensing 干旱指数","遥感农业干旱监测-4575",2,"10.1080\u002F15481603.2026.2738981",{"doi":370,"openalex_id":372,"authors":373,"venue":305,"cited_by_count":111,"oa_url":383,"card":384,"direction":202,"ingested_from":118},"W7221081979",[374,377,380],{"name":375,"orcid":376},"Fatemeh Saba","https:\u002F\u002Forcid.org\u002F0000-0001-6171-155X",{"name":378,"orcid":379},"Nima Ahmadian","https:\u002F\u002Forcid.org\u002F0000-0003-3676-1840",{"name":381,"orcid":382},"Olena Dubovyk","https:\u002F\u002Forcid.org\u002F0000-0002-7338-3167","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F15481603.2026.2738981?needAccess=true",{"tldr":385,"method":386,"finding":387,"direction":116,"opportunity":388},"系统综述2015-2025年遥感农业干旱监测与预测的传感器、指数、建模及验证进展。","基于Web of Science与Scopus的181篇文献系统综述，涵盖光学\u002F","光学热红外平台占主导，微波土壤水分利用不足；复合指数与机器学习建模成为趋势。","微波土壤水分数据融合与深度学习干旱预测模型在数据稀缺区验证是潜在研究空白。","2026-10-10T23:30:14.933078Z",{"id":391,"title":392,"url":393,"summary":394,"summary_zh":395,"content":9,"source_name":396,"source_url":393,"published_at":397,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":398,"score_detail":399,"sources":402,"tags":404,"primary_topic":407,"secondary_topics":408,"classification":413,"search_phrases":439,"slug":442,"view_count":369,"doi":443,"paper":444,"created_at":462},3916,"Hyperspectral inversion of soil total nitrogen content using spectral and machine learning","https:\u002F\u002Fdoi.org\u002F10.3329\u002Fbjb.v55i3.93850","The Agro-pastoral Transitional Ecotone of Northern Shaanxi is an ecologically fragile region. Soil nitrogen is a critical factor influencing crop productivity and regional ecological balance. Rapid and accurate monitoring of farmland soil total nitrogen (TN) content is therefore essential for implementing precision agriculture and ensuring sustainable land use in this area. This study focused on the cultivated soils of Dingbian County, a representative area within this ecotone. Field-collected soil samples underwent TN content determination and laboratory-based hyper spectral reflectance measurement. The original spectral reflectance (R) was first transformed using mathematical methods, including first-derivative (FDR), second-derivative (SDR), and logarithm of the reciprocal's first-derivative (LRFD) analysis, to enhance spectral information and suppress background noise. Subsequently, the successive projections algorithm (SPA) and correlation analysis (CA) were integrated to identify feature wavelengths highly correlated with soil TN from the transformed spectral data. Partial Least Squares Regression (PLSR), Decision Tree (DT), and Random Forest (RF) were developed to estimate soil TN content. Model performance was evaluated and compared using the coefficient of determination (R²), root mean square error (RMSE), and ratio of performance to deviation (RPD). The results indicated that: (1) Spectral derivative transformations effectively highlighted subtle information, with LRFD showing the greatest sensitivity to TN. Models built using LRFD-derived features consistently outperformed those based on original reflectance. (2) Among the machine learning algorithms, the Random Forest (RF) model demonstrated superior predictive performance on the validation set (R² = 0.85, RMSE = 0.11 g\u002Fkg, RPD = 2.15), indicating the highest stability and generalization capability. The PLSR model performed moderately well, while the DT model was prone to over fitting and yielded the lowest accuracy. (3) This study confirms that the integration of hyper spectral technology and machine learning algorithms enables efficient and precise estimation of soil TN content in farmland within the Northern Shaanxi agro-pastoral ecotone, providing a reliable technical framework for soil fertility assessment and precision nutrient management in the region. Bangladesh J. Bot. 55(3): 605-614, 2026 (September)","陕北农牧交错带是生态脆弱区，土壤氮素是影响作物生产力和区域生态平衡的关键因子。快速、准确地监测农田土壤全氮（TN）含量，对于该区实施精准农业和保障土地可持续利用至关重要。本研究以该交错带典型区域定边县的耕作土壤为研究对象，对野外采集的土壤样品进行TN含量测定和实验室高光谱反射率测量。首先采用数学方法对原始光谱反射率（R）进行变换，包括一阶微分（FDR）、二阶微分（SDR）和倒数对数一阶微分（LRFD）分析，以增强光谱信息并抑制背景噪声。随后，将连续投影算法（SPA）与相关分析（CA）相结合，从变换后的光谱数据中筛选出与土壤TN高度相关的特征波长。构建偏最小二乘回归（PLSR）、决策树（DT）和随机森林（RF）模型估算土壤TN含量。采用决定系数（R²）、均方根误差（RMSE）和相对分析误差（RPD）对模型性能进行评价与比较。结果表明：（1）光谱微分变换有效凸显了细微信息，其中LRFD对TN的敏感性最强，基于LRFD衍生特征构建的模型始终优于基于原始反射率的模型。（2）在机器学习算法中，随机森林（RF）模型在验证集上表现出优越的预测性能（R² = 0.85，RMSE = 0.11 g\u002Fkg，RPD = 2.15），具有最高的稳定性和泛化能力。PLSR模型表现中等，而DT模型易于过拟合，精度最低。（3）本研究证实，高光谱技术与机器学习算法相结合能够高效、精确地估算陕北农牧交错带农田土壤TN含量，为该区域土壤肥力评估和精准养分管理提供了可靠的技术框架。Bangladesh J. Bot. 55(3): 605-614, 2026 (September)","Bangladesh Journal of Botany","2026-09-30T00:00:00Z",70,{"impact":166,"substance":400,"depth":152,"authority":135,"freshness":67,"relevant":20,"comment":401},20,"高光谱结合机器学习反演土壤全氮，方法对比扎实、结论可靠，对精准农业养分管理有参考价值，但属区域性田间研究，影响范围有限。",[403],{"name":396,"url":393},[25,405,26,70,406],"精准施肥","土壤氮素",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},[409,412],{"code":246,"slug":250,"name":410,"path":411,"parent_code":321},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":44,"slug":45,"name":46,"path":47,"parent_code":42},{"status":49,"locked":14,"version":50,"method":51,"evidence":414},{"candidates":415,"agricultural_evidence":56},[416,420,424,429,433],{"code":38,"score":55,"supported":56,"title":417,"abstract":418,"tags":419},[63],[26,63],[26],{"code":246,"score":17,"supported":56,"title":421,"abstract":422,"tags":423},[250],[249,250],[],{"code":44,"score":136,"supported":56,"title":425,"abstract":427,"tags":428},[426],"hyperspectral",[],[70],{"code":74,"score":172,"supported":56,"title":430,"abstract":431,"tags":432},[],[78,175],[25],{"code":434,"score":75,"supported":56,"title":435,"abstract":436,"tags":438},"F3",[],[437],"养分管理",[405],[440,441],"定边县 土壤全氮 高光谱","陕北农牧交错带 土壤氮素 反演","定边县土壤全氮高光谱-3916","10.3329\u002Fbjb.v55i3.93850",{"doi":443,"openalex_id":445,"authors":446,"venue":396,"cited_by_count":111,"oa_url":393,"card":457,"direction":296,"ingested_from":118},"W7214885868",[447,450,452,454],{"name":448,"orcid":449},"Zhenyu Dong","https:\u002F\u002Forcid.org\u002F0000-0003-0028-3293",{"name":451,"orcid":9},"Ni Wang",{"name":453,"orcid":9},"Jiancang Xie",{"name":455,"orcid":456},"Jungang Luo","https:\u002F\u002Forcid.org\u002F0009-0009-0285-4839",{"tldr":458,"method":459,"finding":460,"direction":116,"opportunity":461},"用高光谱与机器学习反演陕北农牧交错带农田土壤全氮含量。","野外采样测光谱，经FDR\u002FSDR\u002FLRFD变换，SPA+CA选特征波长，建PLS","LRFD变换最敏感，RF模型验证集R²=0.85、RPD=2.15，精度与泛化最优。","可探索多源遥感与深度学习融合，提升生态脆弱区土壤氮素时空反演与制图能力。","2026-10-01T23:30:53.960449Z",{"id":464,"title":465,"url":466,"summary":467,"summary_zh":468,"content":9,"source_name":469,"source_url":466,"published_at":470,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":398,"score_detail":471,"sources":473,"tags":475,"primary_topic":478,"secondary_topics":483,"classification":486,"search_phrases":514,"slug":517,"view_count":20,"doi":518,"paper":519,"created_at":545},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",{"impact":166,"substance":400,"depth":152,"authority":166,"freshness":136,"relevant":20,"comment":472},"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。",[474],{"name":469,"url":466},[25,26,476,477,70],"小麦","杂草识别",{"code":479,"slug":480,"name":476,"path":481,"parent_code":482},"A2","wheat","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[484,485],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"code":44,"slug":45,"name":46,"path":47,"parent_code":42},{"status":49,"locked":14,"version":50,"method":51,"evidence":487},{"candidates":488,"agricultural_evidence":56},[489,493,497,501,505,509],{"code":479,"score":55,"supported":56,"title":490,"abstract":491,"tags":492},[480],[476,480],[476],{"code":38,"score":55,"supported":56,"title":494,"abstract":495,"tags":496},[63],[26,63],[26],{"code":44,"score":166,"supported":56,"title":498,"abstract":499,"tags":500},[426],[426],[70],{"code":74,"score":172,"supported":56,"title":502,"abstract":503,"tags":504},[],[78,175],[25],{"code":260,"score":82,"supported":56,"title":506,"abstract":507,"tags":508},[],[263],[],{"code":510,"score":20,"supported":14,"title":511,"abstract":512,"tags":513},"F4",[],[],[477],[515,516],"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":518,"openalex_id":520,"authors":521,"venue":469,"cited_by_count":111,"oa_url":9,"card":540,"direction":296,"ingested_from":118},"W7214771122",[522,525,528,531,534,537],{"name":523,"orcid":524},"Rimma Meyramovna Ualiyeva","https:\u002F\u002Forcid.org\u002F0000-0003-3551-5007",{"name":526,"orcid":527},"Mariya M. Kaverina","https:\u002F\u002Forcid.org\u002F0000-0002-3838-7656",{"name":529,"orcid":530},"Александра Викторовна Осипова","https:\u002F\u002Forcid.org\u002F0000-0002-3947-5252",{"name":532,"orcid":533},"E. Koppayev","https:\u002F\u002Forcid.org\u002F0009-0004-0476-9017",{"name":535,"orcid":536},"М. А. Чайзабекова","https:\u002F\u002Forcid.org\u002F0009-0004-0468-956X",{"name":538,"orcid":539},"Sayan Berikovich Zhangazin","https:\u002F\u002Forcid.org\u002F0000-0002-5813-8331",{"tldr":541,"method":542,"finding":543,"direction":116,"opportunity":544},"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":547,"title":548,"url":549,"summary":550,"summary_zh":551,"content":9,"source_name":552,"source_url":549,"published_at":553,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":554,"score_detail":555,"sources":557,"tags":559,"primary_topic":564,"secondary_topics":565,"classification":567,"search_phrases":600,"slug":603,"view_count":20,"doi":604,"paper":605,"created_at":632},3830,"Current breakthroughs in biomass and carbon modelling of rubber plantations: A bibliometric analysis and literature review","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.15821","Rubber plantations (Hevea brasiliensis (Willd. ex A.Juss.) Müll.Arg.) are expanding tropical carbon (C) stores, storing an estimated 160–300 mg C ha-1 of total ecosystem C at maturity, but precise biomass and soil organic carbon (SOC) assessment remains difficult due to age disparities, historical land-use variations and data gaps. This review systematically analyses trends and research gaps in biomass accumulation of rubber systems. PRISMA 2020 guidelines were used to examine 104 studies in Scopus published between 1999 and 2025. The literature revealed a marked increase in research studies in China and Thailand post-2013 using a bibliometric approach presented by Biblioshiny. The most important methods are species-specific allometry (coefficient of determination (R2) > 0.9), age-based growth curves, remote sensing (RS) regressions (R2 0.2–0.7) and machine learning (ML) models like Random Forest\u002FXGBoost (R2 0.8–0.97, root mean square error (RMSE) 7–40 mg\u002Fha), which outperform simpler parametric models. Deep learning (DL) yields the highest accuracies (RMSE ~6 mg ha-1). Gaps persist in standardised protocols, South Indian datasets (e.g., Tamil Nadu\u002FWestern Ghats) and integrated lifecycle carbon accounting, including latex\u002FSOC. Forest-to-rubber conversion incurs net losses, while cropland-to-rubber\u002Fagroforestry offers gains. The authors recommend adopting harmonised Tier 2\u002F3 IPCC models, multi-decadal RS archives for reducing emissions from deforestation and forest degradation (REDD+) and ML extension to Indian smallholders. Policy integration with offset markets requires avoiding deforestation to realise climate-smart benefits.","橡胶林（Hevea brasiliensis (Willd. ex A.Juss.) Müll.Arg.）正在扩大热带碳（C）储量，据估计，成熟期生态系统总碳储量可达160–300 mg C ha⁻¹，但由于林龄差异、历史土地利用变化及数据缺口，精确评估生物量和土壤有机碳（SOC）仍然困难。本综述系统分析了橡胶系统生物量积累的趋势与研究空白。采用PRISMA 2020指南，对Scopus数据库中1999年至2025年间发表的104篇研究进行了审查。通过Biblioshiny文献计量方法分析发现，2013年后中国和泰国的研究数量显著增加。最重要的方法包括物种特异性异速生长方程（决定系数（R²）> 0.9）、基于林龄的生长曲线、遥感（RS）回归（R² 0.2–0.7）以及机器学习（ML）模型如随机森林\u002FXGBoost（R² 0.8–0.97，均方根误差（RMSE）7–40 mg\u002Fha），后者优于简单的参数模型。深度学习（DL）精度最高（RMSE约6 mg ha⁻¹）。在标准化方案、南印度数据集（如泰米尔纳德邦\u002F西高止山脉）以及涵盖乳胶\u002FSOC的综合生命周期碳核算方面仍存在空白。森林转橡胶林会导致净碳损失，而农田转橡胶林\u002F农林复合经营则带来碳增益。作者建议采用统一的IPCC第2\u002F3层级模型、多年代际遥感档案以支持减少毁林和森林退化所致排放（REDD+），并将机器学习应用扩展至印度小农户。与抵消市场的政策整合需要避免毁林，以实现气候智慧型效益。","Plant Science Today","2026-09-28T00:00:00Z",80,{"impact":19,"substance":18,"depth":19,"authority":135,"freshness":136,"relevant":20,"comment":556},"系统综述橡胶林生物量与碳建模研究进展，方法学与数据缺口梳理扎实，对农业碳汇与遥感监测领域有参考价值。",[558],{"name":552,"url":549},[26,28,560,561,562,563],"农业碳汇","生物量估算","橡胶林","碳汇计量",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},[566],{"code":44,"slug":45,"name":46,"path":47,"parent_code":42},{"status":49,"locked":14,"version":50,"method":51,"evidence":568},{"candidates":569,"agricultural_evidence":56},[570,577,581,585,589,595],{"code":571,"score":17,"supported":56,"title":572,"abstract":574,"tags":576},"I7",[573],"bibliometric",[575,573],"文献计量",[],{"code":38,"score":13,"supported":56,"title":578,"abstract":579,"tags":580},[],[163,26,63,161],[26],{"code":44,"score":67,"supported":56,"title":582,"abstract":583,"tags":584},[],[70,71],[561,28],{"code":246,"score":121,"supported":56,"title":586,"abstract":587,"tags":588},[],[249,250],[],{"code":590,"score":82,"supported":56,"title":591,"abstract":592,"tags":594},"G2",[],[593],"小农户",[],{"code":596,"score":20,"supported":14,"title":597,"abstract":598,"tags":599},"H4",[],[],[560],[601,602],"橡胶林 生物量 碳储量","橡胶园 遥感 机器学习","橡胶林生物量碳储量-3830","10.14719\u002Fpst.15821",{"doi":604,"openalex_id":606,"authors":607,"venue":552,"cited_by_count":111,"oa_url":626,"card":627,"direction":202,"ingested_from":118},"W7214623646",[608,611,614,617,620,623],{"name":609,"orcid":610},"R M Dhinu","https:\u002F\u002Forcid.org\u002F0009-0008-3471-1093",{"name":612,"orcid":613},"M. Vijayabhama","https:\u002F\u002Forcid.org\u002F0000-0001-9437-9477",{"name":615,"orcid":616},"Balaji Kannan","https:\u002F\u002Forcid.org\u002F0000-0003-2330-0893",{"name":618,"orcid":619},"K Baranidharan","https:\u002F\u002Forcid.org\u002F0000-0001-9371-8588",{"name":621,"orcid":622},"M Kalpana","https:\u002F\u002Forcid.org\u002F0000-0002-0201-6968",{"name":624,"orcid":625},"M Sankavi","https:\u002F\u002Forcid.org\u002F0009-0006-1493-1123","https:\u002F\u002Fhorizonepublishing.com\u002Fjournals\u002Findex.php\u002FPST\u002Farticle\u002Fdownload\u002F15821\u002F16343",{"tldr":628,"method":629,"finding":630,"direction":116,"opportunity":631},"综述橡胶林生物量与碳建模研究进展，指出机器学习与深度学习精度最高但标准化和区域数据仍不足。","PRISMA 2020 文献计量分析 104 篇 Scopus 文献，比较异速生","机器学习\u002F深度学习精度优于传统模型，但标准协议、印度南部数据和全生命周期碳核算仍存空白。","可面向印度小农户构建多年代遥感与机器学习融合的橡胶林碳核算与REDD+监测体系。","2026-09-30T23:30:12.585164Z"]