[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2165":3,"related-2165":100},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":63,"slug":66,"view_count":67,"doi":68,"paper":69,"created_at":99},2165,"Multi-grained image-text retrieval in farmland remote sensing via multimodal large language models","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.engappai.2026.116113","Low-altitude farmland remote sensing (FRS) has become an important tool for precision agricultural monitoring. However, existing FRS methods are mainly designed for segmentation with predefined categories, making them insufficient for semantic understanding and unable to support flexible natural-language queries. To address this limitation, we introduce an artificial intelligence-based task, FRS cross-modal retrieval, which aims to retrieve target farmland regions from large-scale remote sensing images using textual descriptions. To support this task, we construct the first Farmland Remote Sensing Cross-Modal Retrieval (FRS-CMR) dataset, containing 67,806 image-text pairs generated through multimodal large language models and expert refinement. Based on FRS-CMR, we propose a Farmland-oriented Multi-Grained Retrieval (FarmMGR) framework for image-text alignment. FarmMGR jointly exploits global scene semantics and local region-level cues to capture both large-scale landscape patterns and fine-grained farmland characteristics. To further improve semantic alignment, we design Prototype-Based Semantic Alignment (PBSA) to alleviate the soft-positive sample problem, Global Semantics Hierarchical Feature Integration (GSHFI) to adaptively fuse global and local representations, and Instance-Level Semantic Relation Modeling (ISRM) to capture relational dependencies among farmland instances. In addition, Memory-Enhanced Cross-Modal Contrastive Learning (MECCL) enlarges the negative sample pool and improves representation discrimination. Extensive experiments on FRS-CMR show that FarmMGR achieves a mean recall of 28.57%, outperforming existing image-text retrieval methods and demonstrating its effectiveness for language-driven farmland remote sensing retrieval.","低空农田遥感(FRS)已成为精准农业监测的重要工具。然而，现有农田遥感方法主要针对预定义类别的分割任务设计，难以进行语义理解，也无法支持灵活的自然语言查询。为解决这一局限，我们引入了一项基于人工智能的任务——农田遥感跨模态检索，旨在利用文本描述从大规模遥感图像中检索目标农田区域。为支撑该任务，我们构建了首个农田遥感跨模态检索(FRS-CMR)数据集，包含67,806个通过多模态大语言模型生成并经专家精炼的图像-文本对。基于FRS-CMR，我们提出了面向农田的多粒度检索(FarmMGR)框架以实现图像-文本对齐。FarmMGR联合利用全局场景语义和局部区域级线索，以同时捕获大尺度景观格局和细粒度农田特征。为进一步提升语义对齐效果，我们设计了基于原型的语义对齐(PBSA)以缓解软正样本问题，全局语义层次特征融合(GSHFI)以自适应融合全局与局部表示，以及实例级语义关系建模(ISRM)以捕获农田实例间的关系依赖。此外，记忆增强跨模态对比学习(MECCL)扩大了负样本池并提升了表示判别能力。在FRS-CMR上的大量实验表明，FarmMGR取得了28.57%的平均召回率，优于现有图像-文本检索方法，证明了其在语言驱动农田遥感检索中的有效性。",null,"Engineering Applications of Artificial Intelligence","2026-09-10T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,9,1,"构建首个农田遥感跨模态检索数据集并提出多粒度对齐框架，方法新颖、数据规模可观，对语言驱动的精准农业监测有实质推动。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","精准农业","遥感","多模态大模型",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51,57],{"code":32,"score":45,"supported":46,"title":47,"abstract":49,"tags":50},15,true,[48],"remote sensing",[29,48],[29],{"code":52,"score":53,"supported":46,"title":54,"abstract":55,"tags":56},"I1",5,[],[28],[26,28],{"code":58,"score":53,"supported":46,"title":59,"abstract":60,"tags":62},"I2",[],[61],"artificial intelligence",[27,30],[64,65],"农业人工智能 多模态大模型 智慧农业 精准农业","农业人工智能 多模态大模型","农业人工智能多模态大模型智慧农业精准农业-2165",2,"10.1016\u002Fj.engappai.2026.116113",{"doi":68,"openalex_id":70,"authors":71,"venue":10,"cited_by_count":91,"oa_url":6,"card":92,"direction":96,"ingested_from":98},"W7212180335",[72,74,77,79,82,84,86,88],{"name":73,"orcid":9},"KeJian Yu",{"name":75,"orcid":76},"Wentao Ma","https:\u002F\u002Forcid.org\u002F0000-0002-2781-1693",{"name":78,"orcid":9},"Shichao Jin",{"name":80,"orcid":81},"Lu Liu","https:\u002F\u002Forcid.org\u002F0000-0002-3170-9376",{"name":83,"orcid":9},"Yuwei Wang",{"name":85,"orcid":9},"Weiwei Wang",{"name":87,"orcid":9},"Longzhe Quan",{"name":89,"orcid":90},"Lichuan Gu","https:\u002F\u002Forcid.org\u002F0000-0002-3768-8203",0,{"tldr":93,"method":94,"finding":95,"direction":96,"opportunity":97},"提出农田遥感图文跨模态检索任务，构建数据集并提出FarmMGR框架实现语言驱动检索。","构建67,806图文对FRS-CMR数据集，用多粒度对齐与记忆增强对比学习。","FarmMGR平均召回率28.57%，优于现有图文检索方法，验证语言驱动农田遥感检索可行。","农业遥感与作物表型","可探索多模态大模型生成数据的噪声校正、跨区域泛化及细粒度农田语义检索基准。","openalex","2026-09-11T23:30:30.005240Z",{"total":101,"page":21,"page_size":101,"items":102},6,[103,209,262,335,418,489],{"id":104,"title":105,"url":106,"summary":107,"summary_zh":108,"content":9,"source_name":109,"source_url":106,"published_at":110,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":111,"score_detail":112,"sources":118,"tags":120,"primary_topic":122,"secondary_topics":128,"classification":134,"search_phrases":172,"slug":175,"view_count":101,"doi":176,"paper":177,"created_at":208},2947,"AI-enabled UAV-based Soil Organic Carbon Mapping in Arid Environments: A Pilot Study Protocol","https:\u002F\u002Fdoi.org\u002F10.2174\u002F0118743315495282260915110324","Introduction Soil organic carbon (SOC) is an important indicator of soil health, agricultural productivity, and carbon sequestration potential. However, accurate and scalable SOC mapping in arid environments is constrained by high spatial heterogeneity and the limitations of conventional soil sampling. This study aims to develop a standardized UAV-enabled framework for high-resolution SOC mapping in arid agricultural environments. Methods A pilot-study protocol integrating UAV-based hyperspectral remote sensing with artificial intelligence and machine learning was developed. The workflow encompasses study-site selection, ground-reference sampling, UAV hyperspectral data acquisition, radiometric and geometric preprocessing, spectral feature extraction and selection, machine-learning model development, validation, uncertainty assessment, and performance evaluation using R 2 , RMSE, and MAE. The protocol also incorporates assessment of environmental confounders, including soil moisture, surface roughness, and crop residues. Results The resulting framework provides a systematic and reproducible workflow for UAV-based SOC estimation, integrating field observations, hyperspectral features, predictive modelling, and uncertainty assessment. It establishes defined procedures for evaluating model robustness and transferability across varying field conditions. Discussion The framework addresses an important methodological gap in UAV-enabled SOC mapping by integrating remote sensing and AI within a standardized pilot-study design. Its emphasis on environmental confounders and uncertainty assessment can improve the reliability and comparability of SOC mapping studies. However, field validation across diverse arid environments remains necessary. Conclusion The proposed protocol provides a practical foundation for reproducible SOC mapping and subsequent field validation, supporting precision agriculture, sustainable soil management, and carbon monitoring, reporting, and verification (MRV) in arid regions.","引言 土壤有机碳（SOC）是衡量土壤健康、农业生产力及碳固存潜力的重要指标。然而，干旱环境中高空间异质性和传统土壤采样的局限性制约了准确且可扩展的SOC制图。本研究旨在开发一个标准化的无人机（UAV）框架，用于干旱农业环境中的高分辨率SOC制图。方法 开发了一套整合无人机高光谱遥感与人工智能及机器学习的试点研究方案。该工作流程涵盖研究地点选择、地面参考采样、无人机高光谱数据采集、辐射与几何预处理、光谱特征提取与选择、机器学习模型开发、验证、不确定性评估，以及使用R²、RMSE和MAE进行的性能评价。该方案还包括对环境混杂因素的评估，包括土壤水分、地表粗糙度和作物残茬。结果 所构建的框架为基于无人机的SOC估算提供了系统且可重复的工作流程，整合了野外观测、高光谱特征、预测建模和不确定性评估。它建立了明确的程序，用于评估模型在不同田间条件下的稳健性和可迁移性。讨论 该框架通过将遥感与人工智能整合于标准化的试点研究设计中，填补了无人机SOC制图领域的重要方法学空白。其对环境混杂因素和不确定性评估的重视，可提高SOC制图研究的可靠性和可比性。然而，仍需在不同干旱环境中进行田间验证。结论 所提出的方案为可重复的SOC制图及后续田间验证提供了实用基础，支持干旱地区的精准农业、可持续土壤管理以及碳监测、报告与核查（MRV）。","The Open Agriculture Journal","2026-09-18T00:00:00Z",67,{"impact":113,"substance":17,"depth":114,"authority":115,"freshness":116,"relevant":21,"comment":117},12,16,13,8,"提出无人机高光谱结合AI的干旱区土壤有机碳制图标准化方案，方法框架清晰但尚属试点协议、缺乏实地验证，具备一定参考价值。",[119],{"name":109,"url":106},[26,27,28,29,121],"土壤碳汇",{"code":123,"slug":124,"name":125,"path":126,"parent_code":127},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[129,133],{"code":58,"slug":130,"name":131,"path":132,"parent_code":36},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":135},{"candidates":136,"agricultural_evidence":46},[137,142,148,153,158,165],{"code":123,"score":19,"supported":46,"title":138,"abstract":139,"tags":141},[124],[140,124],"土壤",[],{"code":58,"score":13,"supported":46,"title":143,"abstract":144,"tags":147},[],[145,146,61],"机器学习","machine learning",[27],{"code":32,"score":13,"supported":46,"title":149,"abstract":150,"tags":152},[],[29,48,151],"hyperspectral",[29],{"code":52,"score":116,"supported":46,"title":154,"abstract":155,"tags":157},[],[28,156],"precision agriculture",[26,28],{"code":159,"score":160,"supported":46,"title":161,"abstract":162,"tags":164},"H4",4,[],[163],"carbon sequestration",[121],{"code":166,"score":167,"supported":46,"title":168,"abstract":169,"tags":171},"H7",3,[],[170],"干旱",[],[173,174],"无人机 土壤有机碳 制图","AI 高光谱 干旱农业","无人机土壤有机碳制图-2947","10.2174\u002F0118743315495282260915110324",{"doi":176,"openalex_id":178,"authors":179,"venue":109,"cited_by_count":91,"oa_url":106,"card":203,"direction":96,"ingested_from":98},"W7213561504",[180,183,186,189,192,195,197,199,201],{"name":181,"orcid":182},"Moath Awawdeh","https:\u002F\u002Forcid.org\u002F0000-0003-1404-6782",{"name":184,"orcid":185},"Irfan Ahmed","https:\u002F\u002Forcid.org\u002F0000-0002-2172-4177",{"name":187,"orcid":188},"Anees Bashir","https:\u002F\u002Forcid.org\u002F0000-0002-4668-6592",{"name":190,"orcid":191},"Tarig Faisal","https:\u002F\u002Forcid.org\u002F0000-0001-6451-7576",{"name":193,"orcid":194},"Nicky Rahmana Putra","https:\u002F\u002Forcid.org\u002F0000-0003-4886-496X",{"name":196,"orcid":9},"Almaha Jamal",{"name":198,"orcid":9},"Afra Rashed",{"name":200,"orcid":9},"Hamda Yousif",{"name":202,"orcid":9},"Sarah Sadeq",{"tldr":204,"method":205,"finding":206,"direction":96,"opportunity":207},"提出一套无人机高光谱结合AI的干旱区土壤有机碳制图标准化试点方案。","无人机高光谱遥感、地面采样、光谱特征选择与机器学习建模，用R²、RMSE、MAE","构建了可复现的SOC估算流程，并纳入环境混杂因素与不确定性评估。","可在多干旱区开展跨区域验证，探索模型迁移性与不确定性量化方法。","2026-09-19T23:30:33.273156Z",{"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":111,"score_detail":217,"sources":221,"tags":223,"primary_topic":226,"secondary_topics":227,"classification":229,"search_phrases":246,"slug":249,"view_count":91,"doi":250,"paper":251,"created_at":261},5570,"Evaluation of Counterfactual Explanation Robustness for Computer Vision Agriculture Yield-Forecast Models Under Satellite Cloud-Mask Gaps: An Interpretability Reliability Bound for Cooperative Storage Decisions","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545079","Counterfactual explanations are increasingly proposed as the interface through which cooperative storage managers interrogate computer-vision yield-forecast models, yet their reliability under the satellite cloud-mask gaps that routinely afflict agricultural remote sensing has not been bounded. The bound is derived from a Lipschitz continuity assumption on the forecast model and a masking-operator perturbation analysis, and it is expressed as a function of the cloud-mask gap fraction, the model's local Lipschitz constant, and the counterfactual search tolerance. A replication-based evaluation of the before-after-control-impact estimator was conducted to characterise the operating characteristics of the design under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64), with power estimated across 2000 design cells. The reliability bound degrades non-linearly as the gap fraction rises, and that the intraclass correlation of 0.64 implies that cooperative-level aggregation of counterfactual recourse is necessary to preserve explanation stability. The contribution is a decision-relevant reliability bound that storage cooperatives can use to set a maximum tolerable cloud-mask gap before counterfactual recourse is suspended.","反事实解释日益被提出作为合作式仓储管理者质询计算机视觉产量预测模型的接口，然而在农业遥感中经常出现的卫星云掩膜缺口条件下，其可靠性尚未得到界定。该界基于预测模型上的Lipschitz连续性假设和掩膜算子扰动分析推导得出，并表示为云掩膜缺口比例、模型局部Lipschitz常数和反事实搜索容差的函数。对前后对照影响估计量进行了基于重复的评估，以表征该设计在给定方差结构（组间标准差=6.0，残差标准差=4.5，组内相关系数=0.64）下的运行特征，并在2000个设计单元中估计了检验效能。可靠性界随缺口比例上升呈非线性退化，且组内相关系数0.64意味着需要对反事实救济进行合作社层面的聚合，以保持解释的稳定性。本文的贡献在于提供了一个与决策相关的可靠性界，仓储合作社可据此设定在暂停反事实救济之前可容忍的最大云掩膜缺口。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",{"impact":116,"substance":218,"depth":219,"authority":113,"freshness":13,"relevant":21,"comment":220},20,17,"该论文提出反事实解释在卫星云掩膜缺口下的可靠性边界，方法新颖且结论可用于合作社决策，但属细分方法学研究，产业影响有限。",[222],{"name":215,"url":212},[26,27,224,29,225],"产量预测","可解释性",{"code":58,"slug":130,"name":131,"path":132,"parent_code":36},[228],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":230},{"candidates":231,"agricultural_evidence":46},[232,238,242],{"code":58,"score":113,"supported":46,"title":233,"abstract":235,"tags":237},[234],"computer vision",[236],"计算机视觉",[27],{"code":32,"score":116,"supported":46,"title":239,"abstract":240,"tags":241},[],[29,48],[224,29],{"code":52,"score":21,"supported":14,"title":243,"abstract":244,"tags":245},[],[],[26],[247,248],"农业人工智能 产量预测 可解释性 智慧农业","农业人工智能 产量预测","农业人工智能产量预测可解释性智慧农业-5570","10.5281\u002Fzenodo.23545079",{"doi":250,"openalex_id":252,"authors":253,"venue":215,"cited_by_count":91,"oa_url":212,"card":256,"direction":96,"ingested_from":98},"W7223114002",[254],{"name":255,"orcid":9},"Kadiatou Barry",{"tldr":257,"method":258,"finding":259,"direction":96,"opportunity":260},"推导卫星云掩膜缺口下反事实解释可靠性的理论界，并评估其对合作社储粮决策的影响。","基于Lipschitz连续性假设与掩膜扰动分析推导可靠性界，并用2000个设计单","缺口比例上升使可靠性非线性下降；ICC=0.64表明需合作社层级聚合以保持解释稳定。","可研究不同作物区云掩膜统计特性下的自适应解释暂停阈值与聚合策略。","2026-10-22T11:20:39.240791Z",{"id":263,"title":264,"url":265,"summary":266,"summary_zh":267,"content":9,"source_name":268,"source_url":265,"published_at":269,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":270,"score_detail":271,"sources":273,"tags":275,"primary_topic":277,"secondary_topics":282,"classification":284,"search_phrases":316,"slug":319,"view_count":91,"doi":320,"paper":321,"created_at":334},5394,"Rice Yield Prediction Using Multi-Modal Regression Learning with Optimized Ensemble Models","https:\u002F\u002Fdoi.org\u002F10.3390\u002Feng7100572","Accurate rice yield estimation is important for agricultural planning, resource allocation, and food security assessment, but yield depends on interacting climatic, vegetation, remote sensing, seasonal, and crop growth factors. This study presents a Multi-Modal Regression Learning (MMRL) framework for continuous rice yield prediction. The framework integrates rainfall, temperature, humidity, normalized difference vegetation index (NDVI), VH synthetic aperture radar (SAR) backscatter, season, rice cultivation area, and leaf wetness information. Predictor relevance is assessed before model development, and multiple regression learners are trained using a common feature set. Their validation predictions are combined through non-negative ensemble weights constrained to sum to one, thereby producing a single optimized prediction. Experiments were performed on a modeled spatio-temporal agricultural dataset representing Karnataka conditions, with 10,000 observations used for the regression comparison. The proposed MMRL ensemble achieved an R2 of 0.9515, an RMSE of 0.6334 tons\u002Fha, an MAE of 0.5075 tons\u002Fha, and an MAPE of 9.81%, outperforming the evaluated individual regression models. Diagnostic plots further indicated close agreement between predicted and reference yields across most of the evaluated range. The MMRL workflow was also evaluated on a separately reconstructed synthetic dataset generated with fully specified distributions, sampling rules, noise terms, a yield generation equation, and fixed random seeds.","准确的水稻产量估算对农业规划、资源配置和粮食安全评估具有重要意义，但产量取决于气候、植被、遥感、季节和作物生长等多种因素的相互作用。本研究提出了一种用于连续水稻产量预测的多模态回归学习（Multi-Modal Regression Learning，MMRL）框架。该框架整合了降雨量、温度、湿度、归一化植被指数（NDVI）、VH合成孔径雷达（SAR）后向散射、季节、水稻种植面积和叶片湿润信息。在模型开发之前评估预测变量的相关性，并使用共同特征集训练多个回归学习器。其验证预测结果通过约束为和等于1的非负集成权重进行组合，从而生成单一优化预测。实验在代表卡纳塔克邦条件的建模时空农业数据集上进行，使用10,000个观测值进行回归比较。所提出的MMRL集成模型实现了R²为0.9515、RMSE为0.6334吨\u002F公顷、MAE为0.5075吨\u002F公顷、MAPE为9.81%，优于所评估的各个回归模型。诊断图进一步表明，在大多数评估范围内，预测产量与参考产量高度一致。MMRL工作流程还在一个单独重建的合成数据集上进行了评估，该数据集使用完全指定的分布、采样规则、噪声项、产量生成方程和固定随机种子生成。","Eng—Advances in Engineering","2026-10-18T00:00:00Z",78,{"impact":17,"substance":18,"depth":17,"authority":113,"freshness":116,"relevant":21,"comment":272},"多模态回归集成方法在水稻估产上取得较高精度，方法新颖、数据规模明确，对农业信息化有参考价值，但属学术论文且数据集为模拟数据，产业影响有限。",[274],{"name":268,"url":265},[26,27,276,224,29],"水稻",{"code":278,"slug":279,"name":276,"path":280,"parent_code":281},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[283],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":285},{"candidates":286,"agricultural_evidence":46},[287,291,295,302,308,312],{"code":278,"score":45,"supported":46,"title":288,"abstract":289,"tags":290},[279],[276,279],[276],{"code":32,"score":116,"supported":46,"title":292,"abstract":293,"tags":294},[],[29,48],[224,29],{"code":296,"score":101,"supported":46,"title":297,"abstract":298,"tags":301},"A8",[],[299,300],"粮食安全","food security",[],{"code":303,"score":167,"supported":46,"title":304,"abstract":305,"tags":307},"J1",[],[306],"农业规划",[],{"code":52,"score":21,"supported":14,"title":309,"abstract":310,"tags":311},[],[],[26],{"code":58,"score":21,"supported":14,"title":313,"abstract":314,"tags":315},[],[],[27],[317,318],"水稻 产量预测 多模态","NDVI SAR 水稻估产","水稻产量预测多模态-5394","10.3390\u002Feng7100572",{"doi":320,"openalex_id":322,"authors":323,"venue":268,"cited_by_count":91,"oa_url":265,"card":329,"direction":96,"ingested_from":98},"W7222942064",[324,326],{"name":325,"orcid":9},"Nayana Venkatesh",{"name":327,"orcid":328},"Evangelin Geetha Devaraj","https:\u002F\u002Forcid.org\u002F0000-0002-7253-4314",{"tldr":330,"method":331,"finding":332,"direction":96,"opportunity":333},"构建多模态回归学习框架，融合气候、遥感与作物因子，实现水稻产量连续预测。","融合降雨、温度、NDVI、SAR后向散射等多源特征，用非负权重集成多个回归器。","集成模型R²达0.9515，RMSE为0.6334吨\u002F公顷，优于各单一回归模型。","可引入真实田间多时相遥感与深度时序模型，验证合成数据结论在真实产区的泛化性。","2026-10-20T23:30:19.292634Z",{"id":336,"title":337,"url":338,"summary":339,"summary_zh":340,"content":9,"source_name":341,"source_url":338,"published_at":342,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":343,"score_detail":344,"sources":346,"tags":348,"primary_topic":351,"secondary_topics":352,"classification":359,"search_phrases":403,"slug":406,"view_count":67,"doi":9,"paper":407,"created_at":417},5013,"REMOTE SENSING AND GIS-BASED ASSESSMENT OF SOIL SALINITY IN IRRIGATED LANDS: METHODS, CHALLENGES AND PROSPECTS FOR PRECISION AGRICULTURE","http:\u002F\u002Fwww.arabianjournalofscience.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F1223","Soil salinization is a major land degradation process affecting the sustainability of irrigated agriculture, particularly in arid and semi-arid regions. Conventional soil sampling provides essential information about salinity levels but may be insufficient for continuous monitoring of large agricultural territories because of spatial variability, sampling costs, and temporal limitations. Remote sensing (RS) and geographic information systems (GIS) offer complementary approaches for identifying spatial patterns of salinity, monitoring land conditions, and supporting agricultural management. This article examines the scientific and methodological foundations of integrating multispectral satellite imagery, spectral indices, geospatial analysis, and machine learning for soil salinity assessment in irrigated lands. Particular attention is given to Landsat and Sentinel-2 imagery, the interpretation of vegetation and soil reflectance indicators, the role of electrical conductivity measurements in model calibration, and the limitations associated with vegetation cover, soil moisture, and seasonal variability. The analysis indicates that reliable salinity assessment requires the integration of satellite observations with field measurements and environmental variables rather than the isolated application of spectral indices. The article proposes a methodological framework for developing spatially explicit salinity assessment systems applicable to irrigated agricultural landscapes, including those of Central Asia. The framework provides a basis for improving monitoring strategies and supporting evidence-based land management without assuming unverified local measurement results.","土壤盐渍化是影响灌溉农业可持续性的主要土地退化过程，在干旱和半干旱地区尤为突出。传统土壤采样可提供盐分水平的基本信息，但由于空间变异性、采样成本和时间限制，可能不足以对大面积农业区域进行持续监测。遥感（RS）和地理信息系统（GIS）为识别盐渍化空间格局、监测土地状况和支持农业管理提供了互补性方法。本文探讨了整合多光谱卫星影像、光谱指数、地理空间分析和机器学习进行灌溉土地土壤盐渍化评估的科学与方法学基础。文章特别关注Landsat和Sentinel-2影像、植被与土壤反射率指标的解译、电导率测量在模型校准中的作用，以及与植被覆盖、土壤湿度和季节变异性相关的局限性。分析表明，可靠的盐渍化评估需要将卫星观测与野外测量和环境变量相结合，而非孤立地应用光谱指数。本文提出了一个方法学框架，用于开发适用于灌溉农业景观（包括中亚地区）的空间显式盐渍化评估系统。该框架为改进监测策略和支持基于证据的土地管理提供了基础，且不以未经核实的当地测量结果为假设前提。","Arabian journal of science.","2026-10-13T00:00:00Z",74,{"impact":114,"substance":218,"depth":219,"authority":115,"freshness":116,"relevant":21,"comment":345},"系统综述遥感与GIS在灌溉农田盐渍化评估中的方法框架与挑战，对盐碱地治理和精准农业有参考价值，但属综述性论文，非突破性成果。",[347],{"name":341,"url":338},[26,28,29,349,350],"盐碱地治理","土壤监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[353,358],{"code":354,"slug":355,"name":356,"path":357,"parent_code":36},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":123,"slug":124,"name":125,"path":126,"parent_code":127},{"status":39,"locked":14,"version":40,"method":41,"evidence":360},{"candidates":361,"agricultural_evidence":46},[362,367,375,379,383,387,393,397],{"code":32,"score":17,"supported":46,"title":363,"abstract":364,"tags":366},[48],[29,48,365],"multispectral",[29],{"code":354,"score":219,"supported":46,"title":368,"abstract":370,"tags":374},[369],"GIS",[371,372,369,373],"地理信息","空间分析","geographic information",[],{"code":123,"score":45,"supported":46,"title":376,"abstract":377,"tags":378},[124],[140,124],[349],{"code":52,"score":13,"supported":46,"title":380,"abstract":381,"tags":382},[156],[],[26,28],{"code":58,"score":101,"supported":46,"title":384,"abstract":385,"tags":386},[],[145,146],[],{"code":388,"score":167,"supported":46,"title":389,"abstract":390,"tags":392},"H3",[],[391],"灌溉",[],{"code":166,"score":167,"supported":46,"title":394,"abstract":395,"tags":396},[],[170],[],{"code":398,"score":167,"supported":46,"title":399,"abstract":400,"tags":402},"I7",[],[401],"methodological framework",[],[404,405],"遥感 GIS 土壤盐渍化","Landsat Sentinel-2 盐分监测","遥感GIS土壤盐渍化-5013",{"doi":9,"openalex_id":408,"authors":409,"venue":341,"cited_by_count":91,"oa_url":338,"card":412,"direction":96,"ingested_from":98},"W7221843179",[410],{"name":411,"orcid":9},"Sadullayev Sardor Zokirjon o'g'li",{"tldr":413,"method":414,"finding":415,"direction":96,"opportunity":416},"综述遥感与GIS评估灌溉地土壤盐渍化的方法、挑战与前景，并提出空间显式评估框架。","整合多光谱卫星影像、光谱指数、GIS空间分析与机器学习，结合电导率校准。","可靠盐渍化评估需卫星观测与实地测量及环境变量融合，而非孤立使用光谱指数。","可探索多源遥感与机器学习融合的盐渍化动态监测，并验证于中亚等干旱灌区。","2026-10-15T23:30:32.401626Z",{"id":419,"title":420,"url":421,"summary":422,"summary_zh":423,"content":9,"source_name":424,"source_url":421,"published_at":425,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":426,"score_detail":427,"sources":430,"tags":432,"primary_topic":435,"secondary_topics":436,"classification":439,"search_phrases":463,"slug":466,"view_count":91,"doi":467,"paper":468,"created_at":488},5006,"Mapping Soil Salinity in Arid Agricultural Regions Using Multi-Source Remote Sensing and a Long-Range Dependency-Enhanced Deep Learning Framework","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15101966","Soil salinity estimation across arid agricultural regions is challenging due to pronounced environmental heterogeneity and complex dependencies among multi-source variables. This study proposes a Long-Range Dependency-Enhanced Network (LRD-Net) for regional-scale soil salinity estimation in multiple agricultural oasis regions using multi-source remote sensing and environmental covariates. A dataset comprising 180 field samples from three agricultural oasis regions in Xinjiang, China, was constructed with 25 covariates derived from spectral, topographic, climatic, and soil property data. LRD-Net consists of three stages: a multi-scale feature fusion backbone based on depthwise separable convolutions and cross-scale interaction; a Long-Range Dependency Enhancement Module (LDEM) with an asymmetric dual-branch architecture that captures forward and reverse-aware dependencies through reverse alignment and cross-directional fusion; and multi-scale attention refinement with a regression head for continuous salinity estimation. Under the region-stratified random-split evaluation, LRD-Net achieved an R2 of 0.905, an RMSE of 1.207 g·kg−1, and an MAE of 0.868 g·kg−1. Performance decreased under more spatially independent validation, with an R2 of 0.874 under spatial cross-validation and LORO test R2 values ranging from 0.739 to 0.801, indicating that prediction across spatially and geographically independent samples is more challenging than region-stratified random-split evaluation. The proposed framework effectively integrates multi-source information and captures complementary long-range dependencies, providing a potential approach for regional-scale soil salinity estimation within the studied arid agricultural oasis environments and supporting soil salinization monitoring and sustainable land management in the study region.","干旱农业区域土壤盐分估算面临显著的环境异质性和多源变量间复杂依赖关系的挑战。本研究提出了一种长程依赖增强网络（LRD-Net），利用多源遥感与环境协变量对多个农业绿洲区域进行区域尺度土壤盐分估算。研究构建了包含中国新疆三个农业绿洲区域180个野外样本的数据集，并衍生出25个协变量，涵盖光谱、地形、气候和土壤属性数据。LRD-Net由三个阶段组成：基于深度可分离卷积和跨尺度交互的多尺度特征融合骨干网络；具有非对称双分支架构的长程依赖增强模块（LDEM），通过反向对齐和跨方向融合捕获前向与反向感知依赖关系；以及多尺度注意力细化与回归头，用于连续盐分估算。在区域分层随机划分评估下，LRD-Net取得了R²为0.905、RMSE为1.207 g·kg⁻¹、MAE为0.868 g·kg⁻¹的性能。在空间独立性更强的验证下性能有所下降，空间交叉验证R²为0.874，留一区域测试R²范围为0.739至0.801，表明跨空间和地理独立样本的预测比区域分层随机划分评估更具挑战性。所提出的框架有效整合了多源信息并捕获了互补的长程依赖关系，为研究区干旱农业绿洲环境下的区域尺度土壤盐分估算提供了潜在方法，并支持研究区土壤盐渍化监测与可持续土地管理。","Land","2026-10-14T00:00:00Z",72,{"impact":113,"substance":428,"depth":219,"authority":115,"freshness":20,"relevant":21,"comment":429},21,"方法新颖、数据扎实的遥感土壤盐渍化估算研究，对干旱区盐碱地监测与治理有参考价值，但属细分领域学术进展，公共影响有限。",[431],{"name":424,"url":421},[26,27,29,433,434],"土壤盐渍化","新疆农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[437,438],{"code":123,"slug":124,"name":125,"path":126,"parent_code":127},{"code":58,"slug":130,"name":131,"path":132,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":440},{"candidates":441,"agricultural_evidence":46},[442,446,450,455,459],{"code":32,"score":45,"supported":46,"title":443,"abstract":444,"tags":445},[48],[29,48],[29],{"code":123,"score":19,"supported":46,"title":447,"abstract":448,"tags":449},[124],[140,124],[],{"code":58,"score":20,"supported":46,"title":451,"abstract":453,"tags":454},[452],"deep learning",[],[27],{"code":166,"score":167,"supported":46,"title":456,"abstract":457,"tags":458},[],[170],[],{"code":52,"score":21,"supported":14,"title":460,"abstract":461,"tags":462},[],[],[26],[464,465],"新疆 农业绿洲 土壤盐渍化 遥感","LRD-Net 土壤盐分 估算","新疆农业绿洲土壤盐渍化遥感-5006","10.3390\u002Fland15101966",{"doi":467,"openalex_id":469,"authors":470,"venue":424,"cited_by_count":91,"oa_url":421,"card":483,"direction":96,"ingested_from":98},"W7221816183",[471,473,476,478,480],{"name":472,"orcid":9},"Mingjie Qian",{"name":474,"orcid":475},"Liangjun Wang","https:\u002F\u002Forcid.org\u002F0009-0006-8718-8326",{"name":477,"orcid":9},"Peng Wang",{"name":479,"orcid":9},"Hangyuan Liu",{"name":481,"orcid":482},"Weitao Chen","https:\u002F\u002Forcid.org\u002F0000-0002-6272-1618",{"tldr":484,"method":485,"finding":486,"direction":96,"opportunity":487},"提出LRD-Net深度学习框架，融合多源遥感与环境协变量，估算新疆干旱绿洲区土壤盐分。","构建180个样点、25个协变量的数据集，设计含长程依赖增强模块的多尺度网络。","区域分层随机划分下R²达0.905，但空间独立验证R²降至0.739-0.874，跨区域预测更难。","可探索跨区域迁移学习与不确定性量化，提升空间独立场景下盐分制图泛化能力。","2026-10-15T23:30:29.667342Z",{"id":490,"title":491,"url":492,"summary":493,"summary_zh":494,"content":9,"source_name":495,"source_url":492,"published_at":425,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":496,"sources":498,"tags":500,"primary_topic":502,"secondary_topics":503,"classification":511,"search_phrases":560,"slug":563,"view_count":21,"doi":564,"paper":565,"created_at":588},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",{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":497},"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。",[499],{"name":495,"url":492},[26,27,224,501,29],"多模态融合",{"code":58,"slug":130,"name":131,"path":132,"parent_code":36},[504,510],{"code":505,"slug":506,"name":507,"path":508,"parent_code":509},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":512},{"candidates":513,"agricultural_evidence":46},[514,520,528,533,538,542,549,555],{"code":58,"score":515,"supported":46,"title":516,"abstract":517,"tags":519},11,[],[518,145,146,452],"深度学习",[27,501],{"code":505,"score":20,"supported":46,"title":521,"abstract":522,"tags":527},[],[523,524,525,526],"苹果","芒果","orchard","apple",[],{"code":32,"score":116,"supported":46,"title":529,"abstract":530,"tags":532},[],[531,365],"无人机多光谱",[224,29],{"code":52,"score":534,"supported":46,"title":535,"abstract":536,"tags":537},7,[],[28,156],[26],{"code":123,"score":101,"supported":46,"title":539,"abstract":540,"tags":541},[],[140,124],[],{"code":543,"score":101,"supported":46,"title":544,"abstract":545,"tags":548},"I5",[],[546,547],"物联网","IoT",[],{"code":550,"score":167,"supported":46,"title":551,"abstract":552,"tags":554},"H6",[],[553],"农业生态",[],{"code":166,"score":167,"supported":46,"title":556,"abstract":557,"tags":559},[],[558],"气候适应",[],[561,562],"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":564,"openalex_id":566,"authors":567,"venue":495,"cited_by_count":91,"oa_url":492,"card":581,"direction":587,"ingested_from":98},"W7221795850",[568,571,573,575,577,579],{"name":569,"orcid":570},"Shabir Ahmad Mir","https:\u002F\u002Forcid.org\u002F0000-0003-2311-218X",{"name":572,"orcid":9},"Umarfarooque Momin",{"name":574,"orcid":9},"Sudeep Kumar E",{"name":576,"orcid":9},"Itfaq Ahmad Mir",{"name":578,"orcid":9},"Ab Rouf Khan",{"name":580,"orcid":9},"Ghulam Mohammad Mir",{"tldr":582,"method":583,"finding":584,"direction":585,"opportunity":586},"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。","智慧农业 \u002F 农业物联网","2026-10-15T23:30:08.750140Z"]