[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3280":3,"related-3280":88},{"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":52,"slug":55,"view_count":56,"doi":57,"paper":58,"created_at":87},3280,"Machine Learning for Surface Water Quality Management in Malaysia: Integrating Physicochemical Parameters, Nutrient Loads, Land Use, Hydrological Conditions, and Anthropogenic Pressure","https:\u002F\u002Fdoi.org\u002F10.64440\u002Fijes\u002Fengineeringx0021","Surface water quality in Malaysia is increasingly influenced by rapid urbanization, industrial expansion, agricultural activities, wastewater discharge, climatic variability, and changes in hydrological regimes. These interacting pressures create complex and nonlinear relationships among physicochemical water-quality parameters, watershed characteristics, and pollutant dynamics, making conventional monitoring approaches insufficient for comprehensive and timely assessment. This study develops a data-driven machine learning framework for surface water quality assessment, prediction, pollution-event detection, and management in Malaysian river systems. The proposed framework integrates physicochemical, hydrological, spatial, land-use, and anthropogenic variables to characterize the temporal and spatial variability of water-quality conditions. The investigated parameters include dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, pH, temperature, electrical conductivity, turbidity, total suspended solids, ammoniacal nitrogen, nitrate, phosphate, total nitrogen, and total phosphorus, together with rainfall, streamflow, water level, antecedent rainfall, urbanization, agricultural land use, industrial land use, population density, wastewater pressure, and road density. Seven machine learning approaches are incorporated, including Decision Trees (DT), Random Forest (RF), Support Vector Machines (SVM), Support Vector Regression (SVR), Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM) networks, and hybrid ensemble models. The Malaysian National Water Quality Standards and Water Quality Index (WQI) are incorporated to establish the regulatory and quantitative basis for water-quality classification and prediction. The framework further integrates explainable artificial intelligence techniques, including SHAP and LIME, to quantify predictor contributions and identify the dominant environmental and anthropogenic factors governing model outputs. The proposed methodology enables simultaneous water-quality classification, continuous prediction, anomaly and pollution-event detection, source-pressure identification, and management-oriented decision support. Particular emphasis is placed on temporal variability, nonlinear interactions, hydrological influence, land-use effects, and the integration of multiple environmental data sources. The study provides a comprehensive Malaysia-oriented machine learning framework that can enhance conventional surface-water monitoring by combining predictive modeling, explainability, and integrated watershed information. The findings establish a methodological basis for developing real-time and transferable water-quality prediction systems capable of supporting pollution mitigation, watershed management, and sustainable water-resource planning across Malaysian river basins.","马来西亚地表水水质日益受到快速城市化、工业扩张、农业活动、废水排放、气候变率以及水文情势变化的影响。这些相互作用压力在物理化学水质参数、流域特征与污染物动态之间形成了复杂的非线性关系，使传统监测方法难以实现全面且及时的评估。本研究开发了一个数据驱动的机器学习框架，用于马来西亚河流系统的地表水水质评估、预测、污染事件检测与管理。所提出的框架整合了物理化学、水文、空间、土地利用和人为变量，以刻画水质条件的时间与空间变异性。所研究的参数包括溶解氧、生化需氧量、化学需氧量、pH、温度、电导率、浊度、总悬浮固体、氨氮、硝酸盐、磷酸盐、总氮和总磷，以及降雨量、河流流量、水位、前期降雨、城市化、农业用地、工业用地、人口密度、废水压力和道路密度。该框架纳入了七种机器学习方法，包括决策树（DT）、随机森林（RF）、支持向量机（SVM）、支持向量回归（SVR）、人工神经网络（ANN）、长短期记忆（LSTM）网络以及混合集成模型。研究纳入马来西亚国家水质标准和水质指数（WQI），以建立水质分类与预测的监管和定量基础。该框架进一步整合了可解释人工智能技术，包括SHAP和LIME，以量化预测因子的贡献并识别主导模型输出的主要环境与人为因素。所提出的方法能够同时实现水质分类、连续预测、异常与污染事件检测、源压力识别以及面向管理的决策支持。研究特别强调时间变异性、非线性相互作用、水文影响、土地利用效应以及多环境数据源的整合。该研究提供了一个面向马来西亚的综合机器学习框架，可增强传统",null,"The International Journal of Engineering Sciences","2026-09-22T00:00:00Z","论文",10,false,68,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":20,"relevant":21,"comment":22},12,20,17,9,1,"马来西亚河流水质机器学习框架，方法体系完整但属区域性研究，对国内农业信息化参考价值有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业人工智能","机器学习","可解释AI","水质监测","流域管理",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":45},[44],{"code":32,"score":18,"supported":45,"title":46,"abstract":48,"tags":51},true,[47],"machine learning",[27,47,49,50],"artificial intelligence","decision support",[26,28,27],[53,54],"马来西亚 地表水 机器学习","水质指数 机器学习 预测","马来西亚地表水机器学习-3280",3,"10.64440\u002Fijes\u002Fengineeringx0021",{"doi":57,"openalex_id":59,"authors":60,"venue":10,"cited_by_count":79,"oa_url":6,"card":80,"direction":84,"ingested_from":86},"W7214031821",[61,63,65,67,69,71,73,75,77],{"name":62,"orcid":9},"Iskandar M. Wahyu",{"name":64,"orcid":9},"Gusti E. Rosyadi",{"name":66,"orcid":9},"May Abdul Hafed Abdul kader",{"name":68,"orcid":9},"Nuryani U. Humaira",{"name":70,"orcid":9},"Safaruddin P. Nasyita",{"name":72,"orcid":9},"Sudarijati Sudarijati;",{"name":74,"orcid":9},"Ujang Asmil Zuwariah",{"name":76,"orcid":9},"Anisa Ricardi",{"name":78,"orcid":9},"Achuo Azmaine",0,{"tldr":81,"method":82,"finding":83,"direction":84,"opportunity":85},"构建马来西亚地表水质机器学习框架，融合多源环境数据实现分类、预测与污染事件检测。","用DT、RF、SVM、SVR、ANN、LSTM及混合集成模型，结合SHAP\u002FLI","多源变量与可解释AI能有效刻画水质非线性时空变化并识别主要人为压力源。","农业人工智能与决策模型","可迁移至中国流域，探索农业面源污染与水文气候耦合下的可解释水质预警模型。","openalex","2026-09-23T23:30:32.639861Z",{"total":89,"page":21,"page_size":89,"items":90},6,[91,179,270,337,416,492],{"id":92,"title":93,"url":94,"summary":95,"summary_zh":96,"content":9,"source_name":97,"source_url":94,"published_at":98,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":99,"score_detail":100,"sources":105,"tags":107,"primary_topic":110,"secondary_topics":111,"classification":112,"search_phrases":159,"slug":162,"view_count":163,"doi":164,"paper":165,"created_at":178},3626,"A Hybrid Model for Crop Yield Prediction Using Recurrent Neural Networks and Explainable Artificial Intelligence","https:\u002F\u002Fdoi.org\u002F10.22194\u002Fjgias\u002F27.2061","Accurate maize yield prediction is essential for ensuring food security and supporting agricultural planning in Kenya. However, the changes in climate and severe weather are posing more challenges to the stability of yield and food security. While advanced machine learning models, such as Long Short–Term Memory (LSTM) networks, offer strong predictive performance, their inherent \"black–box\" nature limits their practical adoption by farmers and policymakers who require interpretable and trustworthy insights. This study developed a transparent predictive model by integrating LSTM with Explainable Artificial Intelligence (XAI). Using county–level maize yield data (2012–2023), the hybrid model was compared against Random Forest and Gradient Boosting baselines. The LSTM model achieved superior performance (R² = 0.8551, RMSE = 0.3715, MAE = 0.2705), compared to Random Forest (R2 = 0.8441, RMSE= 0.3851 and MAE= 0.2856) and Gradient Boosting (R2 = 0.7925, RMSE = 0.4443 and MAE = 0.3234) baselines. XAI analysis, using SHapley Additive explanations (SHAP) and Local Interpretable Model–agnostic Explanations (LIME), identified longitude, latitude, and annual rainfall as key predictors. The model maintained high accuracy while improving interpretability, increasing transparency and trust. This provides actionable insights for farmers and policymakers, supporting evidence–based planning and enhancing resilience in smallholder systems. Keywords: Crop yield prediction, long short–term memory (LSTM), explainable artificial intelligence, precision agriculture, machine learning.","准确的玉米产量预测对于保障肯尼亚粮食安全和支持农业规划至关重要。然而，气候变化和极端天气对产量稳定性和粮食安全构成了更多挑战。尽管长短期记忆（LSTM）网络等先进机器学习模型具有强大的预测性能，但其固有的“黑箱”特性限制了农民和政策制定者的实际采用，因为他们需要可解释且可信的洞见。本研究通过将LSTM与可解释人工智能（XAI）相结合，开发了一个透明的预测模型。利用县级玉米产量数据（2012—2023年），将该混合模型与随机森林和梯度提升基线模型进行了比较。LSTM模型取得了更优的性能（R² = 0.8551，RMSE = 0.3715，MAE = 0.2705），优于随机森林（R2 = 0.8441，RMSE= 0.3851，MAE= 0.2856）和梯度提升（R2 = 0.7925，RMSE = 0.4443，MAE = 0.3234）基线模型。使用SHapley加法解释（SHAP）和局部可解释模型无关解释（LIME）进行的XAI分析，确定了经度、纬度和年降雨量是关键预测因子。该模型在保持高精度的同时提高了可解释性，增强了透明度和信任。这为农民和政策制定者提供了可操作的洞见，支持基于证据的规划，并增强小农系统的韧性。关键词：作物产量预测，长短期记忆（LSTM），可解释人工智能，精准农业，机器学习。","Journal of Global Innovations in Agricultural Sciences","2026-09-25T00:00:00Z",71,{"impact":17,"substance":101,"depth":19,"authority":102,"freshness":103,"relevant":21,"comment":104},21,13,8,"将LSTM与可解释AI结合用于肯尼亚县级玉米产量预测，方法新颖、指标详实，对智慧农业有参考价值，但属境外区域研究，公共影响有限。",[106],{"name":97,"url":94},[108,26,27,28,109],"智慧农业","玉米产量预测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":113},{"candidates":114,"agricultural_evidence":45},[115,119,127,134,141,147,153],{"code":32,"score":18,"supported":45,"title":116,"abstract":117,"tags":118},[49],[27,47,49],[26,28,27],{"code":120,"score":121,"supported":45,"title":122,"abstract":123,"tags":126},"I1",7,[],[124,125],"精准农业","precision agriculture",[108],{"code":128,"score":89,"supported":45,"title":129,"abstract":130,"tags":133},"A3",[],[131,132],"玉米","maize",[],{"code":135,"score":89,"supported":45,"title":136,"abstract":137,"tags":140},"A8",[],[138,139],"粮食安全","food security",[],{"code":142,"score":56,"supported":45,"title":143,"abstract":144,"tags":146},"G2",[],[145],"smallholder",[],{"code":148,"score":56,"supported":45,"title":149,"abstract":150,"tags":152},"H7",[],[151],"气候变化",[],{"code":154,"score":56,"supported":45,"title":155,"abstract":156,"tags":158},"J1",[],[157],"农业规划",[],[160,161],"LSTM 玉米产量预测","SHAP LIME 农业模型","LSTM玉米产量预测-3626",5,"10.22194\u002Fjgias\u002F27.2061",{"doi":164,"openalex_id":166,"authors":167,"venue":97,"cited_by_count":79,"oa_url":94,"card":173,"direction":84,"ingested_from":86},"W7214446524",[168,170],{"name":169,"orcid":9},"Stephen Gitau Ndung’u",{"name":171,"orcid":172},"Consolata Gakii","https:\u002F\u002Forcid.org\u002F0000-0003-2783-9992",{"tldr":174,"method":175,"finding":176,"direction":84,"opportunity":177},"用LSTM结合可解释AI预测肯尼亚玉米产量，兼顾精度与透明度。","基于2012-2023县级玉米产量数据，LSTM融合SHAP与LIME，对比随机","LSTM精度最高（R²=0.8551），经度、纬度和年降雨量是主要预测因子。","可探索将可解释AI与遥感、气象多源数据融合，提升小农户区域产量预测的可信度与推广性。","2026-09-27T23:31:22.771605Z",{"id":180,"title":181,"url":182,"summary":183,"summary_zh":184,"content":9,"source_name":185,"source_url":182,"published_at":186,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":187,"score_detail":188,"sources":193,"tags":195,"primary_topic":198,"secondary_topics":199,"classification":206,"search_phrases":243,"slug":246,"view_count":79,"doi":247,"paper":248,"created_at":269},5400,"Artificial intelligence in food safety: a bibliometric and empirical synthesis of emerging technologies and research frontiers","https:\u002F\u002Fdoi.org\u002F10.1108\u002Fbfj-06-2025-0819","Purpose This study aimed to identify recent research trends in the integration of artificial intelligence (AI) with food safety and security through a comprehensive bibliometric analysis. Design\u002Fmethodology\u002Fapproach A total of 2,782 documents published between 2021 and 2025 were retrieved from the Web of Science Core Collection. The dataset was refined by removing duplicates, filtering document types and excluding irrelevant records. VOSviewer was then used to conduct co-authorship, keyword co-occurrence, citation, co-citation and bibliographic coupling analyses. Findings Machine learning and deep learning emerged as core technologies associated with food security, contamination detection and quality prediction. Keyword clustering demonstrated interdisciplinary connections with agriculture, climate change and public health. Citation and co-citation analyses identified three major intellectual foundations: traditional machine learning, computer vision-based deep learning and time-series modeling. China, India and the United States were the most influential contributors in terms of research output and technological development. Bibliographic coupling and source co-citation analyses further indicated increasing convergence between food science and computational science. Originality\u002Fvalue This study provides a comprehensive overview of the intellectual structure, collaborative networks and thematic evolution of AI-driven food safety research. Unlike previous studies focusing on specific technologies or applications, it maps global research patterns and interdisciplinary linkages. The findings provide strategic insights for researchers, policymakers and industry stakeholders. Future studies should prioritize explainable AI integration into regulatory frameworks and the development of larger, more diverse datasets to improve model generalizability across food systems.","目的 本研究旨在通过全面的文献计量分析，识别人工智能（AI）与食品安全及保障融合领域的最新研究趋势。设计与方法 从Web of Science核心合集中检索了2021年至2025年间发表的2，782篇文献。通过去除重复记录、筛选文献类型并排除不相关记录对数据集进行精炼。随后使用VOSviewer进行合著、关键词共现、引文、共被引及文献耦合分析。研究发现 机器学习和深度学习成为与食品安全、污染检测和质量预测相关的核心技术。关键词聚类显示了与农业、气候变化和公共卫生的跨学科联系。引文和共被引分析识别出三大主要知识基础：传统机器学习、基于计算机视觉的深度学习和时间序列建模。中国、印度和美国在研究产出和技术发展方面是最具影响力的贡献者。文献耦合和来源共被引分析进一步表明食品科学与计算科学之间的融合日益加深。原创性\u002F价值 本研究全面概述了AI驱动的食品安全研究的知识结构、合作网络和主题演变。与以往聚焦特定技术或应用的研究不同，本研究绘制了全球研究模式和跨学科联系。研究结果为研究人员、政策制定者和行业利益相关者提供了战略性见解。未来研究应优先关注可解释AI融入监管框架以及开发更大、更多样化的数据集，以提高模型在食品系统中的泛化能力。","British Food Journal","2026-10-19T00:00:00Z",80,{"impact":189,"substance":190,"depth":189,"authority":191,"freshness":103,"relevant":21,"comment":192},18,22,14,"基于2782篇文献的AI食品安全计量分析，方法规范、结论有参考价值，但属综述性研究，产业落地影响有限。",[194],{"name":185,"url":182},[108,26,27,196,197],"食品安全","文献计量",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[200],{"code":201,"slug":202,"name":203,"path":204,"parent_code":205},"E4","food-safety","质量安全与溯源","\u002Ftopic\u002Ffood-supply\u002Ffood-safety","E",{"status":39,"locked":14,"version":40,"method":41,"evidence":207},{"candidates":208,"agricultural_evidence":45},[209,218,224,230,235,239],{"code":32,"score":210,"supported":45,"title":211,"abstract":212,"tags":217},19,[49],[213,27,214,47,215,49,216],"深度学习","计算机视觉","deep learning","computer vision",[26,27],{"code":201,"score":219,"supported":45,"title":220,"abstract":222,"tags":223},15,[221],"food safety",[196,221],[196],{"code":225,"score":219,"supported":45,"title":226,"abstract":228,"tags":229},"I7",[227],"bibliometric",[197,227],[197],{"code":148,"score":89,"supported":45,"title":231,"abstract":232,"tags":234},[],[151,233],"climate change",[],{"code":135,"score":56,"supported":45,"title":236,"abstract":237,"tags":238},[],[139],[],{"code":120,"score":21,"supported":14,"title":240,"abstract":241,"tags":242},[],[],[108],[244,245],"AI 食品安全 文献计量","机器学习 食品质量预测","AI食品安全文献计量-5400","10.1108\u002Fbfj-06-2025-0819",{"doi":247,"openalex_id":249,"authors":250,"venue":185,"cited_by_count":79,"oa_url":9,"card":264,"direction":84,"ingested_from":86},"W7222920742",[251,253,255,258,261],{"name":252,"orcid":9},"Harang Lee",{"name":254,"orcid":9},"지은서",{"name":256,"orcid":257},"Yoonbin Kim","https:\u002F\u002Forcid.org\u002F0000-0002-5625-2560",{"name":259,"orcid":260},"Joon-Goo Lee","https:\u002F\u002Forcid.org\u002F0000-0003-3617-5518",{"name":262,"orcid":263},"Woo‐Ju Kim","https:\u002F\u002Forcid.org\u002F0000-0002-6954-1580",{"tldr":265,"method":266,"finding":267,"direction":84,"opportunity":268},"对2021-2025年2782篇文献做文献计量，梳理AI与食品安全交叉研究趋势。","Web of Science文献计量，VOSviewer共被引、关键词共现与耦合","机器学习与深度学习是核心，形成传统ML、计算机视觉、时序建模三大知识基础。","可解释AI嵌入监管框架、构建更大更多样数据集以提升跨食物系统泛化能力。","2026-10-20T23:30:31.567802Z",{"id":271,"title":272,"url":273,"summary":274,"summary_zh":275,"content":9,"source_name":276,"source_url":273,"published_at":277,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":99,"score_detail":278,"sources":280,"tags":282,"primary_topic":286,"secondary_topics":287,"classification":293,"search_phrases":313,"slug":316,"view_count":79,"doi":317,"paper":318,"created_at":336},5320,"Machine Learning for Drought Analysis and Forecasting: A Critical Review of Data Fusion, Extreme-Event Sensitivity, Explainability and Generalization","https:\u002F\u002Fdoi.org\u002F10.17485\u002Fijst\u002Fv19i36.1263","Background: Machine learning is increasingly applied to drought monitoring and forecasting, but studies differ widely in how drought is defined, what data are used, and how models are validated, which makes reported performance difficult to compare directly. Objectives: To explain why reported machine-learning drought-model performance varies across definitions, data sources and validation schemes, and to assess whether recent studies adequately address rare severe events and cross-domain transferability. Method: Journal articles published January 2018 to August 2026 were reviewed, starting from a 70-record high-relevance subset of a 359-record discovery export and extended through citation chaining. Publisher and DOI verification produced a final evidence base of 55 articles: 48 primary studies and 7 reviews. Target definition, input data, model family, validation design, metrics, class-imbalance treatment, explainability and external testing were compared across studies. Findings: Reported performance varied with the modelling task rather than with any single algorithm: Random Forest reached 97.7% AUC-ROC for agricultural-drought mapping in Queensland, SVM reached average R²=0.90 for hydrological drought in Algeria, and a CatBoost stacking model reached R²=0.9065 for remote-sensing-based drought monitoring. These scores are not directly comparable because targets, data and validation protocols differ. Recent studies increasingly test cross-product and cross-region transfer, use SHAP\u002FLIME explainability, and adopt extreme-aware losses, yet class-wise reporting of severe and extreme drought, ordinal-aware evaluation, and leakage-resistant temporal validation remain inconsistently applied across the 48 primary studies. Novelty: The review jointly evaluates predictive accuracy alongside rare-event sensitivity, ordinal drought severity, leakage-resistant validation, explainability, uncertainty and cross-domain generalization, criteria largely absent from earlier machine-learning drought reviews. Keywords: Drought Forecasting, Machine Learning, Remote Sensing, Explainable AI, Extreme Drought","背景：机器学习正日益应用于干旱监测与预报，但各研究在干旱定义、所用数据和模型验证方式上差异很大，导致所报告的模型性能难以直接比较。目标：解释为何已报告的机器学习干旱模型性能会因定义、数据来源和验证方案的不同而变化，并评估近期研究是否充分处理了罕见的严重事件和跨域可迁移性问题。方法：综述了2018年1月至2026年8月发表的期刊论文，起始于359条发现导出记录中70条高相关性记录的子集，并通过引文追溯加以扩展。经出版商和DOI核验，最终证据基础为55篇文章：48篇原始研究和7篇综述。比较了各研究的目标定义、输入数据、模型族、验证设计、指标、类别不平衡处理、可解释性和外部测试。发现：所报告的性能随建模任务而非任何单一算法而变化：随机森林在昆士兰农业干旱制图中达到97.7%的AUC-ROC，SVM在阿尔及利亚水文干旱中达到平均R²=0.90，CatBoost堆叠模型在基于遥感的干旱监测中达到R²=0.9065。这些分数不能直接比较，因为目标、数据和验证方案不同。近期研究越来越多地测试跨产品和跨区域迁移，使用SHAP\u002FLIME可解释性，并采用极端事件感知损失函数，然而在48篇原始研究中，严重和极端干旱的分类别报告、序数感知评估以及防泄漏的时间验证仍应用不一致。新颖性：本综述将预测准确性与罕见事件敏感性、序数干旱严重程度、防泄漏验证、可解释性、不确定性和跨域泛化等标准共同评估，这些标准在以往的机器学习干旱综述中大多缺失。关键词：干旱预报，机器学习，遥感，可解释人工智能，极端干旱","Indian Journal of Science and Technology","2026-10-18T00:00:00Z",{"impact":17,"substance":101,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":279},"系统综述机器学习干旱分析与预测，指出评估口径不一、极端事件敏感性与可解释性不足，对农业旱情监测信息化有参考价值，但属学术综述、影响面偏细分领域。",[281],{"name":276,"url":273},[26,28,283,284,285],"极端天气","遥感","干旱监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[288],{"code":148,"slug":289,"name":290,"path":291,"parent_code":292},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":39,"locked":14,"version":40,"method":41,"evidence":294},{"candidates":295,"agricultural_evidence":45},[296,301,307],{"code":32,"score":297,"supported":45,"title":298,"abstract":299,"tags":300},16,[47],[27,47],[26,28],{"code":148,"score":219,"supported":45,"title":302,"abstract":304,"tags":306},[303],"drought",[305,303],"干旱",[285],{"code":308,"score":121,"supported":45,"title":309,"abstract":310,"tags":312},"I3",[],[284,311],"remote sensing",[284],[314,315],"机器学习 干旱预测","遥感 干旱监测","机器学习干旱预测-5320","10.17485\u002Fijst\u002Fv19i36.1263",{"doi":317,"openalex_id":319,"authors":320,"venue":276,"cited_by_count":79,"oa_url":273,"card":330,"direction":335,"ingested_from":86},"W7222882764",[321,324,327],{"name":322,"orcid":323},"Ritik Mallik","https:\u002F\u002Forcid.org\u002F0009-0000-2785-0265",{"name":325,"orcid":326},"Dayal Kumar Behera","https:\u002F\u002Forcid.org\u002F0000-0002-9740-8066",{"name":328,"orcid":329},"Satarupa Mohanty","https:\u002F\u002Forcid.org\u002F0000-0002-1434-7158",{"tldr":331,"method":332,"finding":333,"direction":84,"opportunity":334},"综述55篇文献，比较机器学习干旱监测预报在定义、数据与验证上的差异。","系统综述2018-2026年55篇文献，比较目标定义、数据、模型、验证与可解释性","模型性能差异主要源于任务与验证设计而非算法，极端事件与泄漏防护评估仍不足。","可构建面向极端干旱的序数感知评估与抗泄漏时序验证框架，提升跨区域泛化。","农业遥感与作物表型","2026-10-19T23:30:22.840943Z",{"id":338,"title":339,"url":340,"summary":341,"summary_zh":342,"content":9,"source_name":343,"source_url":340,"published_at":277,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":344,"score_detail":345,"sources":347,"tags":349,"primary_topic":351,"secondary_topics":352,"classification":353,"search_phrases":373,"slug":376,"view_count":79,"doi":377,"paper":378,"created_at":415},5306,"A growing challenge: stakeholder perceptions of AI and ML applications in U.S. agriculture","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10459-x","Abstract Context The global population is expected to exceed ten billion by 2050 and food demand is projected to increase by up to 100% in the same timeframe. Agricultural management must adapt to meet the increasing global demands. Artificial intelligence (AI) and machine learning (ML) technologies can help the agricultural community respond to these needs. Aims An online survey and follow-up interviews were conducted to examine patterns of use, perceptions, and barriers among early adopting stakeholders regarding AI and ML. Methods The online survey with agricultural stakeholders (n=129) included questions regarding the participants’ views on incorporating AI and ML techniques into farming practices and individual follow-up interviews with agricultural stakeholders (n=10) were conducted to better understand their concerns toward technology use and adoption of AI-enabled tools and technologies. Key results Using a mixed-methods approach, four challenges to establishing AI-\u002FML-enabled technology in agriculture were identified. Namely, challenges related to (1) systems of systems (SoS), (2) sub-seasonal to seasonal weather (S2S) forecasts, (3) precision soil management, and (4) policy and economics. Each challenge was discussed in the context of recent literature. Conclusion Concerns were voiced clustered around four broad research themes. This brief communication brings the perspective of agricultural stakeholders to the discussion of the potential of AI\u002FML-enabled technology and the direction of their development in the agricultural sector. Implications Data collected in 2021, establish an important baseline for the early phase of AI\u002FML use in U.S. agriculture, enabling comparison of future perceptions and developments in the sector. Impact This brief communication describes a survey and follow-up interviews conducted with agricultural stakeholders on factors influencing their willingness and reluctance to incorporate AI and ML techniques into farming practices. The findings, summarized and discussed as four key challenges for AI and ML in the agricultural sector, have the potential to streamline research activities.","摘要\n\n背景 预计到2050年全球人口将超过100亿，同期粮食需求预计将增加多达100%。农业管理必须作出调整以满足不断增长的全球需求。人工智能（AI）和机器学习（ML）技术可帮助农业界应对这些需求。\n\n目的 通过开展在线调查和后续访谈，考察早期采用相关技术的利益相关者在AI和ML方面的使用模式、认知及障碍。\n\n方法 针对农业利益相关者（n=129）的在线调查包含有关参与者对将AI和ML技术纳入农业实践看法的问题，并对农业利益相关者（n=10）进行个体后续访谈，以更好地了解他们对技术使用及采用AI赋能工具和技术的顾虑。\n\n主要结果 采用混合方法，识别出在农业中建立AI\u002FML赋能技术面临的四项挑战，即与以下方面相关的挑战：（1）系统之系统（SoS），（2）次季节至季节天气（S2S）预报，（3）精准土壤管理，以及（4）政策与经济。每项挑战均结合近期文献进行了讨论。\n\n结论 所表达的顾虑集中于四个广泛的研究主题。本简报将农业利益相关者的视角引入关于AI\u002FML赋能技术潜力及其在农业部门发展方向讨论中。\n\n意义 2021年收集的数据为美国农业中AI\u002FML应用的早期阶段建立了重要基线，有助于比较该部门未来的认知和发展。\n\n影响 本简报描述了针对农业利益相关者开展的一项调查和后续访谈，内容涉及影响其愿意和不愿将AI和ML技术纳入农业实践的因素。研究结果总结并讨论为农业部门AI和ML的四项关键挑战，有望优化研究活动。","Precision Agriculture",77,{"impact":189,"substance":18,"depth":19,"authority":191,"freshness":103,"relevant":21,"comment":346},"基于129份问卷与10次访谈的混合方法研究，系统梳理美国农业AI\u002FML应用的四大挑战，为早期采用者认知提供基线数据，对智慧农业研究有参考价值。",[348],{"name":343,"url":340},[108,26,27,124,350],"农户认知",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":354},{"candidates":355,"agricultural_evidence":45},[356,361,368],{"code":32,"score":357,"supported":45,"title":358,"abstract":359,"tags":360},11,[],[27,47,49],[26,27],{"code":362,"score":89,"supported":45,"title":363,"abstract":364,"tags":367},"H2",[],[365,366],"土壤","soil",[],{"code":120,"score":369,"supported":14,"title":370,"abstract":371,"tags":372},2,[],[],[108,124],[374,375],"美国农业 AI ML 调查","农业人工智能 应用障碍","美国农业AIML调查-5306","10.1007\u002Fs11119-026-10459-x",{"doi":377,"openalex_id":379,"authors":380,"venue":343,"cited_by_count":79,"oa_url":340,"card":410,"direction":84,"ingested_from":86},"W7222866367",[381,384,387,390,393,396,399,402,404,407],{"name":382,"orcid":383},"Jennifer Koch","https:\u002F\u002Forcid.org\u002F0000-0002-7067-2705",{"name":385,"orcid":386},"Andrew Fox","https:\u002F\u002Forcid.org\u002F0000-0001-6719-8902",{"name":388,"orcid":389},"Richa Bhattarai","https:\u002F\u002Forcid.org\u002F0000-0003-3827-7761",{"name":391,"orcid":392},"Hank C. Jenkins‐Smith","https:\u002F\u002Forcid.org\u002F0000-0002-5204-2726",{"name":394,"orcid":395},"Jonathan McFadden","https:\u002F\u002Forcid.org\u002F0000-0003-3762-1428",{"name":397,"orcid":398},"Audrey Reinert","https:\u002F\u002Forcid.org\u002F0000-0001-8951-702X",{"name":400,"orcid":401},"Juan Sesmero","https:\u002F\u002Forcid.org\u002F0000-0002-2669-4536",{"name":403,"orcid":9},"M. Z. Walkup",{"name":405,"orcid":406},"Wolfgang Jentner","https:\u002F\u002Forcid.org\u002F0000-0003-1045-6020",{"name":408,"orcid":409},"David S. Ebert","https:\u002F\u002Forcid.org\u002F0000-0001-6177-1296",{"tldr":411,"method":412,"finding":413,"direction":84,"opportunity":414},"通过问卷与访谈调查美国农业利益相关者对AI\u002FML应用的认知与障碍。","在线问卷129人加10人深度访谈的混合方法。","识别出系统之系统、次季节天气预报、精准土壤管理和政策经济四大挑战。","可针对四类障碍开展分区域、分作物类型的采纳意愿追踪与干预研究。","2026-10-19T23:30:02.345893Z",{"id":417,"title":418,"url":419,"summary":420,"summary_zh":421,"content":9,"source_name":422,"source_url":419,"published_at":423,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":424,"score_detail":425,"sources":427,"tags":429,"primary_topic":431,"secondary_topics":432,"classification":433,"search_phrases":457,"slug":460,"view_count":79,"doi":461,"paper":462,"created_at":491},5080,"Explainable deep learning for Mango leaf disease recognition using AlexNet and grad-CAM","https:\u002F\u002Fdoi.org\u002F10.1177\u002F1088467x261495708","Early and accurate detection of plant diseases is essential for improving crop productivity and supporting sustainable agricultural practices. Mango (Mangifera indica), one of the most economically important tropical fruit crops worldwide, is highly susceptible to several leaf diseases that can significantly reduce yield and fruit quality. This study presents an explainable deep learning framework for automated ten-class mango leaf disease classification using a publicly available dataset containing approximately 5000 images distributed across Anthracnose, Bacterial Canker, Cutting Weevil, Dag Disease, Die Back, Galls, Healthy, Powdery Mildew, Red Rust, and Sooty Mould classes. Unlike conventional AlexNet-based approaches, the proposed framework integrates Bayesian hyperparameter optimization, two-stage fine-tuning, test-time augmentation (TTA), and Grad-CAM-based explainability to improve both predictive performance and interpretability. Experimental results demonstrate that the proposed framework achieves a final test accuracy of 99.2% using eight-view test-time augmentation (TTA), with corresponding precision, recall, and F1-score of approximately 99.1%. The model also achieves micro-average and macro-average ROC–AUC scores of 1.00 on the curated test set, indicating strong discriminative capability across the considered disease categories. Comparative evaluations against MobileNetV2, DenseNet201, ConvNeXt, and LeafNet demonstrate competitive or superior classification performance while maintaining moderate computational complexity and architectural simplicity. Furthermore, Grad-CAM visualizations qualitatively highlight biologically relevant disease symptoms, including lesions, necrotic regions, rust spots, fungal growth, and tissue discoloration, thereby enhancing model interpretability. Although field-scale validation and quantitative explainability assessment remain subjects for future investigation, the proposed framework provides an accurate, computationally efficient, and interpretable approach for automated mango leaf disease diagnosis with strong potential for smart agriculture and precision farming applications.","植物病害的早期准确检测对于提高作物生产力和支持可持续农业实践至关重要。芒果（Mangifera indica）是全球最具经济重要性的热带水果作物之一，极易感染多种叶部病害，这些病害可显著降低产量和果实品质。本研究提出了一种可解释的深度学习框架，用于自动化十类芒果叶部病害分类，所使用的公开数据集包含约5000张图像，分布于炭疽病（Anthracnose）、细菌性溃疡病（Bacterial Canker）、切叶象甲（Cutting Weevil）、枯枝病（Dag Disease）、回枯病（Die Back）、虫瘿（Galls）、健康（Healthy）、白粉病（Powdery Mildew）、红锈病（Red Rust）和煤烟病（Sooty Mould）十个类别。与传统的基于AlexNet的方法不同，所提出的框架集成了贝叶斯超参数优化、两阶段微调、测试时增强（TTA）以及基于Grad-CAM的可解释性，以同时提升预测性能和可解释性。实验结果表明，所提出的框架在使用八视图测试时增强（TTA）时达到了99.2%的最终测试准确率，相应的精确率、召回率和F1分数约为99.1%。该模型在精选测试集上还取得了1.00的微平均和宏平均ROC–AUC分数，表明在所考虑的病害类别间具有较强的判别能力。与MobileNetV2、DenseNet201、ConvNeXt和LeafNet的对比评估表明，该框架在保持适中计算复杂度和架构简洁性的同时，具有竞争性或更优的分类性能。此外，Grad-CAM可视化定性地突出了具有生物学相关性的病害症状，包括病斑、坏死区域、锈斑、真菌生长和组织变色，从而增强了模型的可解释性。尽管田间规模验证和定量可解释性评估仍有待未来研究，但所提出的框架为自动化芒果叶部病害诊断提供了一种准确、计算高效且可解释的方法，在智慧农业和精准农业应用中具有强大潜力。","Intelligent Data Analysis","2026-10-15T00:00:00Z",76,{"impact":297,"substance":101,"depth":19,"authority":102,"freshness":20,"relevant":21,"comment":426},"将贝叶斯调参、两阶段微调与Grad-CAM结合用于十类芒果叶病识别，准确率99.2%，方法新颖且可解释性强，对智慧农业植保应用有参考价值。",[428],{"name":422,"url":419},[108,26,28,124,430],"芒果病害",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":434},{"candidates":435,"agricultural_evidence":45},[436,440,445,451],{"code":32,"score":297,"supported":45,"title":437,"abstract":438,"tags":439},[215],[213,215],[26,28],{"code":120,"score":357,"supported":45,"title":441,"abstract":442,"tags":444},[],[108,124,443],"smart agriculture",[108,124],{"code":446,"score":56,"supported":45,"title":447,"abstract":448,"tags":450},"B2",[],[449],"芒果",[],{"code":452,"score":56,"supported":45,"title":453,"abstract":454,"tags":456},"H8",[],[455],"可持续农业",[],[458,459],"芒果叶片病害 AlexNet","Grad-CAM 芒果病害识别","芒果叶片病害AlexNet-5080","10.1177\u002F1088467x261495708",{"doi":461,"openalex_id":463,"authors":464,"venue":422,"cited_by_count":79,"oa_url":9,"card":485,"direction":490,"ingested_from":86},"W7222328160",[465,468,471,473,476,479,482],{"name":466,"orcid":467},"Anay Ghosh","https:\u002F\u002Forcid.org\u002F0009-0006-6237-7905",{"name":469,"orcid":470},"Tawfikur Rahman","https:\u002F\u002Forcid.org\u002F0000-0002-0456-5143",{"name":472,"orcid":9},"Md Moniruzzaman",{"name":474,"orcid":475},"Nibedita Deb","https:\u002F\u002Forcid.org\u002F0000-0002-1579-1138",{"name":477,"orcid":478},"Hanan Abdullah Mengash","https:\u002F\u002Forcid.org\u002F0000-0002-4103-2434",{"name":480,"orcid":481},"Noorlindawaty Md Jizat","https:\u002F\u002Forcid.org\u002F0000-0001-7618-2283",{"name":483,"orcid":484},"Samir Salem Al‐Bawri","https:\u002F\u002Forcid.org\u002F0000-0003-2852-575X",{"tldr":486,"method":487,"finding":488,"direction":84,"opportunity":489},"提出可解释深度学习框架，用AlexNet结合贝叶斯调参、微调和Grad-CAM实现芒果叶病十分类。","AlexNet+贝叶斯超参优化、两阶段微调、八视图TTA、Grad-CAM，约5","测试准确率达99.2%，ROC-AUC为1.00，Grad-CAM能定位病斑等生物相关症状。","可延伸至田间复杂背景验证、可解释性定量评估及轻量化边缘部署研究。","智慧农业 \u002F 农业物联网","2026-10-16T23:30:08.303796Z",{"id":493,"title":494,"url":495,"summary":496,"summary_zh":497,"content":9,"source_name":498,"source_url":495,"published_at":423,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":499,"score_detail":500,"sources":502,"tags":504,"primary_topic":507,"secondary_topics":508,"classification":509,"search_phrases":539,"slug":542,"view_count":21,"doi":543,"paper":544,"created_at":559},5076,"Trustworthy Explainable AI for Lettuce Growth Stage Prediction Using Sensor Data in Smart Agriculture","https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202610.1452.v1","Background In precision agriculture, especially in hydroponic systems where environmental management affects both production and resource efficiency, interpretable modeling of crop phenological stages is crucial. Typical visualization-based approaches are generally expensive and ecologically dependent. To classify and visualize lettuce growth stages (Seedling, Vegetative Growth, Heading, Bolting and Senescence) using temperature, humidity, total dissolved solids (TDS) and pH, this study proposed XGB-StageVision, a sensor-powered machine learning setup combining supervised and unsupervised learning techniques. Using XG Boost, LightGBM, CatBoost, ExtraTrees, Random Forest and LSTM under an 80\u002F20 train-test split, a dataset of 3,169 unique observations from 70 hydroponically grown lettuce plants was evaluated. Methods and Results XGB-StageVision achieved 99.86% test accuracy, a ROC AUC of 1.00, an MCC of 0.9982 and perfect precision, recall, F1 scores and specificity across all classes, performing competitively with the best baseline models, including LightGBM, which achieved statistically comparable cross validation performance. The unsupervised t-SNE visualization showed clearly distinct structural clusters across growth stages, while SHAP revealed temperature as the strongest predictor (≈75% contribution), followed by TDS. Novel Contributions SHAP, LIME and t-SNE provided global, local and structural interpretability and transparent perspectives on environmental influences regarding plant development. Statistical analysis and 10-fold cross-validation were used to further test the model’s robustness, reliability and generalizability. Conclusion XGB-StageVision provides an IoT-ready, interpretable and imaging-free system for monitoring lettuce growth stages in real time. The framework combines supervised predictive modeling with unsupervised structural visualization to ensure transparency, sustainability and high performance, supporting resource-efficient, data-driven strategic planning in contemporary hydroponic agriculture to lower monitoring costs, optimize inputs and improve yield consistency.","背景 在精准农业中，尤其是在环境管理同时影响产量和资源效率的水培系统中，作物物候阶段的可解释建模至关重要。典型的基于可视化的方法通常成本高昂且依赖生态条件。为了利用温度、湿度、总溶解固体（TDS）和pH对生菜生长阶段（幼苗期、营养生长期、结球期、抽薹期和衰老期）进行分类和可视化，本研究提出了XGB-StageVision，一种由传感器驱动的机器学习方案，结合了监督学习和无监督学习技术。在80\u002F20的训练-测试划分下，使用XGBoost、LightGBM、CatBoost、ExtraTrees、随机森林和LSTM，对来自70株水培生菜的3，169个独特观测值数据集进行了评估。方法与结果 XGB-StageVision达到了99.86%的测试准确率、1.00的ROC AUC、0.9982的MCC，并在所有类别上实现了完美的精确率、召回率、F1分数和特异性，与最佳基线模型表现相当，包括LightGBM，其交叉验证性能在统计上具有可比性。无监督t-SNE可视化显示了各生长阶段之间明显不同的结构聚类，而SHAP揭示温度是最强的预测因子（约75%贡献），其次是TDS。新颖贡献 SHAP、LIME和t-SNE提供了全局、局部和结构可解释性，以及关于环境对植物发育影响的可透明视角。使用统计分析和10折交叉验证进一步检验了模型的稳健性、可靠性和泛化性。结论 XGB-StageVision提供了一种物联网就绪、可解释且无需成像的系统，用于实时监测生菜生长阶段。该框架将监督预测建模与无监督结构可视化相结合，以确保透明度、可持续性和高性能，支持当代水培农业中资源高效、数据驱动的战略规划，以降低监测成本、优化投入并提高产量一致性。","Preprints.org",64,{"impact":17,"substance":18,"depth":19,"authority":89,"freshness":20,"relevant":21,"comment":501},"预印本论文，方法新颖、数据规模可观且强调可解释性，但尚未经同行评审，属细分领域技术进展。",[503],{"name":498,"url":495},[108,26,28,505,506],"水培生菜","传感器监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":510},{"candidates":511,"agricultural_evidence":45},[512,516,520,527,533],{"code":120,"score":219,"supported":45,"title":513,"abstract":514,"tags":515},[443],[124,125],[108],{"code":32,"score":103,"supported":45,"title":517,"abstract":518,"tags":519},[],[27,47],[26,28],{"code":521,"score":89,"supported":45,"title":522,"abstract":523,"tags":526},"I5",[],[524,525],"物联网","IoT",[],{"code":528,"score":56,"supported":45,"title":529,"abstract":530,"tags":532},"B1",[],[531],"生菜",[],{"code":534,"score":56,"supported":45,"title":535,"abstract":536,"tags":538},"B7",[],[537],"hydroponic",[],[540,541],"XGB-StageVision 生菜 生长阶段","水培生菜 传感器 生长预测","XGB-StageVision生菜生长阶段-5076","10.20944\u002Fpreprints202610.1452.v1",{"doi":543,"openalex_id":545,"authors":546,"venue":498,"cited_by_count":79,"oa_url":495,"card":554,"direction":490,"ingested_from":86},"W7222080602",[547,549,552],{"name":548,"orcid":9},"Muhammad Majid",{"name":550,"orcid":551},"Attia Ghafoor","https:\u002F\u002Forcid.org\u002F0009-0000-3828-6479",{"name":553,"orcid":9},"Amna Munir",{"tldr":555,"method":556,"finding":557,"direction":490,"opportunity":558},"提出XGB-StageVision，用传感器数据与可解释AI预测水培生菜生长阶段。","XGBoost等模型结合SHAP、LIME、t-SNE，基于3169条温湿度TD","测试准确率99.86%，温度贡献约75%，t-SNE显示各生长阶段聚类清晰。","可探索多作物、多环境迁移与低成本传感器部署下的可解释模型泛化能力。","2026-10-16T23:30:08.020788Z"]