[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-179":3,"related-179":87},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":31,"secondary_topics":37,"classification":44,"search_phrases":82,"slug":85,"view_count":13,"doi":8,"paper":8,"created_at":86},179,"Smart Agriculture Assistant: An Integrated AI-Driven Decision Support System for Crop Recommendation, Yield Prediction, Fertilizer Optimization and Market Price Forecasting","https:\u002F\u002Fgoldncloudpublications.com\u002Findex.php\u002Firjaem\u002Farticle\u002Fview\u002F1791","印度 Nagarjuna College of Engineering and Technology 的 Abeed K Attar 等提出 Smart Agriculture Assistant 综合 AI 驱动决策支持系统，集作物推荐、产量预测、肥料优化和市场价格预测于一体。系统利用 Random Forest、XGBoost、CatBoost、LSTM 和 SHAP 等多种机器学习算法分析 Kaggle 和 Agmarknet 收集的农业和市场数据集。框架处理土壤养分（N、P、K）、pH、温度、湿度、降雨量、作物特征和历史市场价格数据，为农民生成智能推荐和预测洞察。实验评估表明，所提出的混合框架达到 99% 的准确率，优于传统机器学习模型。",null,"## Authors\n\n*    Abeed K Attar  Department of Computer Science Nagarjuna College of Engineering and Technology Bengaluru Rural, Karnataka, India  Author \n*    Chetan K V  Department of Computer Science Nagarjuna College of Engineering and Technology Bengaluru Rural, Karnataka, India  Author \n*    Karthik S Tevari  Department of Computer Science Nagarjuna College of Engineering and Technology Bengaluru Rural, Karnataka, India  Author \n*    Sanjay Yogi  Department of Computer Science Nagarjuna College of Engineering and Technology Bengaluru Rural, Karnataka, India  Author \n*    Rashmi P Karchi  Department of Computer Science Nagarjuna College of Engineering and Technology Bengaluru Rural, Karnataka, India  Author \n\n## DOI:\n\n[https:\u002F\u002Fdoi.org\u002F10.47392\u002FIRJAEM.2026.0357](https:\u002F\u002Fdoi.org\u002F10.47392\u002FIRJAEM.2026.0357)\n## Keywords:\n\n Artificial Intelligence, Crop Recommendation, Ma-chine Learning, Market Price, Forecasting, Smart Agriculture. \n## Abstract\n\n_Agriculture plays a vital role in ensuring food security and supporting economic development; however, farmers continue to face challenges such as inappropriate crop selec-tion, unpredictable weather conditions, excessive fertilizer usage, fluctuating market prices, and the lack of integrated decision-support systems. To address these challenges, this paper proposes a Smart Agriculture Assistant, an integrated Artificial Intelli-gence (AI)-driven decision support system that combines crop recommendation, crop yield prediction, fertilizer optimization, and market price forecasting within a unified framework. The proposed system utilizes multiple machine learning algorithms, including Random Forest, XGBoost, CatBoost, Long Short-Term Memory (LSTM), and SHAP (SHapley Additive exPlanations), to analyze agricultural and market datasets collected from Kaggle and Agmarknet. The framework processes soil nutrients (N, P, K), pH, temperature, humidity, rainfall, crop characteristics, and historical market price data to generate intelligent recommen-dations and predictive insights for farmers. Data preprocessing techniques such as missing value handling, normalization, feature selection, categorical encoding, and train–test splitting are em-ployed to improve model performance and prediction reliability. Experimental evaluation demonstrates that the proposed hybrid framework achieves an accuracy of 99%, with 98% precision, 98% recall, and 98% F1-score, outperforming conventional machine learning models in agricultural decision-making tasks. The proposed system enables farmers to optimize crop selec-tion, improve fertilizer utilization, enhance crop productivity, and identify profitable market opportunities, thereby promoting sustainable, data-driven, and precision agriculture practices._\n\n## Downloads","IRJAEM Vol.04 Issue 07, July 2026","2026-07-11T00:00:00Z","论文",10,false,56,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,18,16,8,2,1,"论文提出集成AI决策支持系统，方法较新且性能指标高，但来源为普通期刊，时效性差，影响范围有限。",[25],{"name":10,"url":6},[27,28,29,30],"产量预测","决策支持系统","智能农业助手","SHAP XGBoost",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"status":45,"locked":14,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":51},[50,57,64,70,77],{"code":32,"score":17,"supported":51,"title":52,"abstract":54,"tags":56},true,[53],"decision support",[55],"机器学习",[28],{"code":39,"score":58,"supported":51,"title":59,"abstract":61,"tags":63},11,[60],"fertilizer",[62],"肥料",[],{"code":65,"score":58,"supported":51,"title":66,"abstract":68,"tags":69},"I1",[67],"smart agriculture",[67],[],{"code":71,"score":72,"supported":51,"title":73,"abstract":74,"tags":76},"H2",3,[],[75],"土壤",[],{"code":78,"score":22,"supported":14,"title":79,"abstract":80,"tags":81},"I3",[],[],[27],[83,84],"决策支持系统 智能农业助手 产量预测 SHAP XGBoost","决策支持系统 智能农业助手","决策支持系统智能农业助手产量预测SHAPXGBoost-179","2026-08-01T23:53:44.648369Z",{"total":88,"page":22,"page_size":88,"items":89},6,[90,174,278,341,404,490],{"id":91,"title":92,"url":93,"summary":94,"summary_zh":95,"content":8,"source_name":96,"source_url":93,"published_at":97,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":98,"score_detail":99,"sources":102,"tags":104,"primary_topic":110,"secondary_topics":111,"classification":118,"search_phrases":150,"slug":153,"view_count":154,"doi":8,"paper":155,"created_at":173},4670,"SMART FERTILIZER DISTRIBUTION SYSTEM: A COMPREHENSIVE REVIEW","https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F334","Agriculture is an important sector for food production and economic development, and the efficient use of fertilizers is essential for achieving better crop growth and productivity. Conventional fertilizer distribution methods largely depend on manual application, which can lead to uneven distribution, excessive fertilizer usage, increased labor requirements, and wastage of agricultural resources. The development of automation and Internet of Things (IoT) technologies has provided new opportunities for improving fertilizer distribution and management in agricultural fields. This review paper presents a comprehensive study of Smart Fertilizer Distribution Systems, focusing on the technologies, control methods, communication techniques, and approaches reported in existing research. Various systems using microcontrollers, IoT devices, wireless communication, web-based platforms, sensors, motor or pump control, driver circuits, and solenoid valves are reviewed. The working principles of automated fertilizer distribution systems and their role in controlling fertilizer flow and reducing manual intervention are discussed. Different communication technologies, including Wi-Fi and other wireless methods, are compared based on their applicability, cost, operating range, reliability, and control capability. The review also examines the major advantages and limitations of existing systems, such as automation, remote operation, improved fertilizer management, reduced labor, network dependency, power requirements, sensor accuracy, and system complexity. A comparative analysis of selected research works is presented to identify the techniques used, their contributions, and the limitations that remain to be addressed. The review further identifies research gaps and discusses future developments such as automatic fertilizer scheduling, multi-zone fertilizer distribution, real-time monitoring, cloud-based data storage, artificial intelligence, machine learning, and integration with renewable energy sources. The study concludes that smart fertilizer distribution can improve the efficiency, accuracy, and sustainability of fertilizer application and has significant potential for future precision agriculture applications.","农业是粮食生产和经济发展的重要部门，而肥料的高效利用对于实现作物更好生长和生产力提升至关重要。传统的肥料分配方法在很大程度上依赖人工施用，这可能导致分配不均、肥料过量使用、劳动力需求增加以及农业资源浪费。自动化和物联网（IoT）技术的发展为改善农田肥料分配与管理提供了新的机遇。本文综述对智能肥料分配系统进行了全面研究，重点关注现有研究中报道的技术、控制方法、通信技术和方法。本文回顾了使用微控制器、物联网设备、无线通信、基于网络的平台、传感器、电机或泵控制、驱动电路和电磁阀的各种系统。讨论了自动化肥料分配系统的工作原理及其在控制肥料流量和减少人工干预方面的作用。基于适用性、成本、工作范围、可靠性和控制能力，比较了包括Wi-Fi和其他无线方法在内的不同通信技术。本综述还考察了现有系统的主要优势和局限性，如自动化、远程操作、改善肥料管理、减少劳动力、网络依赖、功率需求、传感器精度和系统复杂性。本文对选定的研究工作进行了比较分析，以识别所采用的技术、其贡献以及仍有待解决的局限性。本综述进一步指出了研究空白，并讨论了未来发展方向，如自动肥料调度、多区域肥料分配、实时监测、基于云的数据存储、人工智能、机器学习以及与可再生能源的集成。该研究得出结论，智能肥料分配可以提高肥料施用的效率、准确性和可持续性，并在未来精准农业应用中具有巨大潜力。","International Journal of Mechanical Engineering Research and Technology","2026-10-08T00:00:00Z",61,{"impact":17,"substance":19,"depth":100,"authority":17,"freshness":88,"relevant":22,"comment":101},15,"综述性论文系统梳理智能施肥系统的技术路线与研究空白，对智慧农业领域有参考价值，但属文献综述而非原创突破，时效性一般。",[103],{"name":96,"url":93},[105,106,107,108,109],"智慧农业","农业人工智能","精准施肥","农业物联网","智能农机",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[112,113],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":114,"slug":115,"name":116,"path":117,"parent_code":36},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":45,"locked":14,"version":46,"method":47,"evidence":119},{"candidates":120,"agricultural_evidence":51},[121,125,131,138,145],{"code":39,"score":100,"supported":51,"title":122,"abstract":123,"tags":124},[60],[62,60],[107],{"code":32,"score":13,"supported":51,"title":126,"abstract":127,"tags":130},[],[55,128,129],"machine learning","artificial intelligence",[106],{"code":114,"score":13,"supported":51,"title":132,"abstract":133,"tags":137},[],[134,135,136],"物联网","internet of things","IoT",[108],{"code":65,"score":139,"supported":51,"title":140,"abstract":141,"tags":144},7,[],[142,143],"精准农业","precision agriculture",[105],{"code":146,"score":22,"supported":14,"title":147,"abstract":148,"tags":149},"F5",[],[],[109],[151,152],"智能施肥系统 物联网","精准施肥 自动化","智能施肥系统物联网-4670",0,{"doi":8,"openalex_id":156,"authors":157,"venue":96,"cited_by_count":154,"oa_url":93,"card":166,"direction":170,"ingested_from":172},"W7221394563",[158,160,162,164],{"name":159,"orcid":8},"Anjali Manoj Kumbhar",{"name":161,"orcid":8},"Shruti Prashant Kulkarni",{"name":163,"orcid":8},"Snehal Santosh Khune",{"name":165,"orcid":8},"B. B. Godbole",{"tldr":167,"method":168,"finding":169,"direction":170,"opportunity":171},"综述智能施肥系统的技术、控制与通信方法，并指出研究空白与未来方向。","综述基于微控制器、物联网、无线通信、传感器与电磁阀的自动施肥系统研究。","智能施肥可提升施肥效率、精度与可持续性，但存在网络依赖、功耗与传感器精度等局限。","智慧农业 \u002F 农业物联网","可探索AI驱动的自动施肥调度、多区域分配与云边协同的实时精准施肥系统。","openalex","2026-10-11T23:30:16.128775Z",{"id":175,"title":176,"url":177,"summary":178,"summary_zh":179,"content":8,"source_name":180,"source_url":177,"published_at":181,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":182,"score_detail":183,"sources":187,"tags":189,"primary_topic":192,"secondary_topics":198,"classification":201,"search_phrases":247,"slug":250,"view_count":251,"doi":252,"paper":253,"created_at":277},3918,"UAV-based RGB imagery and supervised–unsupervised machine learning for estimating sorghum biomass and grain yield under water and nitrogen stress","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffenvs.2026.1928610","Precision agriculture emerged as a key strategy for rational management of water and nutrients amid growing food demand driven by global population expansion. Therefore, timely monitoring of field crops is crucial in maintaining adequate nutrient and water levels in the crop fields. Vegetation indices are increasingly being studied for their use in plant nutrient management and in guiding fertilizer recommendations. However, these indices were not adequately evaluated for nutrient management of crops grown with different water and nitrogen application levels. This study investigated the potential application of RGB imagery from low-cost Unmanned Aerial Vehicles (UAVs), combined with machine learning techniques, for quantifying water and nutrient stress in sorghum [ Sorghum bicolor (L.) Moench] crop performance. The field experimental setup at Prairie View A&M University, located in Southeast Texas, applied four irrigation levels (0, 75, 100, and 125% of irrigation; the 100% irrigation level is a fully irrigated treatment with no water stress) and four nitrogen application rates (0, 90, 180, and 360 kg N ha −1 ). The RGB images were captured before harvesting the grain sorghum. Image segmentation for filtering sorghum crops from soil and weeds was performed using K-means clustering and a Convolutional Neural Network (CNN). The vegetation indices, including the Green-Red Vegetation Index (GRVI), Green Leaf Index (GLI), and Excess Green Index (ExG), derived from the segmented images were used to predict sorghum grain and biomass yields using multiple linear regression, Partial least squares regression, Ridge regression, and XGBoost. The results indicated that no single model consistently outperformed the others across both segmentation approaches and response variables, suggesting that the predictive information in the RGB indices is meaningful with selected ML approaches. For example, biomass prediction using ridge regression yielded a Pearson correlation coefficient (r) of 0.65 and an RMSE of 0.72 for K-means clustering-derived indices. The models exhibited moderate, stage-specific predictive performance, with no single regression method consistently outperforming the others across both segmentation approaches and response variables. Because imagery was acquired only once during the late reproductive period, the RGB indices primarily reflected canopy condition, maturity, and senescence at that time. The findings demonstrate the feasibility of a low-cost, single-date UAV RGB workflow for late-season sorghum assessment, while emphasizing the need for multi-temporal imagery and independent validation before application to season-long stress diagnosis or operational yield forecasting.","精准农业已成为在水资源与养分合理管理中的关键策略，以应对全球人口增长所驱动的日益增长的粮食需求。因此，及时监测田间作物对于维持作物田块中适宜的养分和水分水平至关重要。植被指数在植物养分管理和指导施肥推荐中的应用正日益受到研究。然而，这些指数尚未在不同水分和氮素施用水平下对作物养分管理进行充分评估。本研究探讨了来自低成本无人机（UAV）的RGB影像结合机器学习技术在量化高粱[Sorghum bicolor (L.) Moench]作物表现中水分和养分胁迫方面的潜在应用。位于德克萨斯州东南部的Prairie View A&M大学的田间试验设置了四个灌溉水平（灌溉量的0%、75%、100%和125%；100%灌溉水平为充分灌溉处理，无水分胁迫）和四个氮素施用量（0、90、180和360 kg N ha⁻¹）。RGB图像在收获籽粒高粱前采集。使用K-means聚类和卷积神经网络（CNN）进行图像分割，以将高粱作物与土壤和杂草分离。从分割图像中提取的植被指数，包括绿红植被指数（GRVI）、绿叶指数（GLI）和过量绿指数（ExG），被用于通过多元线性回归、偏最小二乘回归、岭回归和XGBoost预测高粱籽粒和生物量产量。结果表明，在两种分割方法和响应变量下，没有单一模型始终优于其他模型，这表明RGB指数中的预测信息在所选机器学习方法下具有意义。例如，使用岭回归进行生物量预测时，基于K-means聚类所得指数的Pearson相关系数（r）为0.65，RMSE为0.72。这些模型表现出中等、特定阶段的预测性能，在两种分割方法和响应变量下，没有单一回归方法始终优于其他方法。由于影像仅在生殖后期采集一次，RGB指数主要反映当时的冠层状况、成熟度和衰老程度。研究结果证明了低成本、","Frontiers in Environmental Science","2026-09-30T00:00:00Z",69,{"impact":17,"substance":184,"depth":19,"authority":185,"freshness":20,"relevant":22,"comment":186},20,13,"低成本无人机RGB影像结合机器学习估算高粱生物量与产量的方法学研究，结论审慎，对精准农业养分水分管理有参考价值，但属单点单期试验，影响范围有限。",[188],{"name":180,"url":177},[105,106,142,190,191],"遥感","高粱",{"code":193,"slug":194,"name":195,"path":196,"parent_code":197},"A5","tubers-grains","薯类与杂粮","\u002Ftopic\u002Ffield-crops\u002Ftubers-grains","A",[199,200],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":202},{"candidates":203,"agricultural_evidence":51},[204,209,213,220,228,232,237,243],{"code":193,"score":100,"supported":51,"title":205,"abstract":207,"tags":208},[206],"sorghum",[191,206],[191],{"code":32,"score":100,"supported":51,"title":210,"abstract":211,"tags":212},[128],[55,128],[106],{"code":39,"score":214,"supported":51,"title":215,"abstract":216,"tags":219},9,[],[217,218,60],"施肥","养分管理",[],{"code":221,"score":214,"supported":51,"title":222,"abstract":223,"tags":227},"H3",[],[224,225,226],"灌溉","水资源","irrigation",[],{"code":65,"score":20,"supported":51,"title":229,"abstract":230,"tags":231},[],[142,143],[105,142],{"code":71,"score":88,"supported":51,"title":233,"abstract":234,"tags":236},[],[75,235],"soil",[],{"code":238,"score":72,"supported":51,"title":239,"abstract":240,"tags":242},"A7",[],[241],"间作",[],{"code":78,"score":22,"supported":14,"title":244,"abstract":245,"tags":246},[],[],[190],[248,249],"Prairie View A&M University 高粱 无人机","高粱 生物量 产量 无人机 RGB","PrairieViewAMUniversity高粱无人机-3918",4,"10.3389\u002Ffenvs.2026.1928610",{"doi":252,"openalex_id":254,"authors":255,"venue":180,"cited_by_count":154,"oa_url":177,"card":270,"direction":276,"ingested_from":172},"W7214994063",[256,258,261,263,266,268],{"name":257,"orcid":8},"Anoop Valiya Veettil",{"name":259,"orcid":260},"Ripendra Awal","https:\u002F\u002Forcid.org\u002F0000-0002-2453-2592",{"name":262,"orcid":8},"Ali Fares",{"name":264,"orcid":265},"Binita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-7093-4915",{"name":267,"orcid":8},"Almoutaz Elhassan",{"name":269,"orcid":8},"Atikur Rahman",{"tldr":271,"method":272,"finding":273,"direction":274,"opportunity":275},"用无人机RGB影像与机器学习估算水分氮胁迫下高粱生物量和产量。","低成本无人机RGB影像，K-means与CNN分割，多种回归模型预测。","单一模型未持续最优，RGB指数在特定时期具中等预测能力。","农业遥感与作物表型","可研究多时相RGB影像与深度学习融合，提升胁迫下作物产量预测精度。","农业人工智能与决策模型","2026-10-01T23:30:54.379150Z",{"id":279,"title":280,"url":281,"summary":282,"summary_zh":8,"content":8,"source_name":283,"source_url":8,"published_at":284,"category":285,"cover_url":8,"hotness":13,"is_selected":14,"score":286,"score_detail":287,"sources":291,"tags":293,"primary_topic":298,"secondary_topics":299,"classification":301,"search_phrases":336,"slug":339,"view_count":88,"doi":8,"paper":8,"created_at":340},3220,"指尖办农事，数字助粮安——齐鲁现代农事综合服务中心管理平台上线，水肥智慧大模型助力农户算好\"浇水账 施肥账\"","https:\u002F\u002Fnew.qq.com\u002Frain\u002Fa\u002F20260921A06Z6K00","9-21 大众新闻报道：齐鲁现代农事综合服务中心管理平台上线首发选择在德州——全国首个百万亩大面积\"吨半粮\"引领区，2026年省委一号文件明确要求布局建设现代农事综合服务中心，全省新增水肥一体化推广面积300万亩；德州计划到2030年水肥一体化面积达到200万亩。省农业科学院作物研究所副所长李娜娜带来了水肥智慧大模型，这项汇聚土壤、墒情、气象、苗情等海量数据的技术，能针对不同地块、不同作物，智能输出个性化的灌溉施肥方案。齐鲁现代农事综合服务中心管理平台把政府政策、农户需求、金融资源、社会服务有效贯通，推动农业社会化服务从\"碎片化供给\"向\"集成化服务\"跃升。","腾讯新闻·大众新闻","2026-09-21T00:00:00Z","报道",76,{"impact":288,"substance":289,"depth":19,"authority":214,"freshness":20,"relevant":22,"comment":290},22,21,"省级政策落地叠加水肥智慧大模型首发，数据与背景扎实，具备智慧农业主题聚合价值。",[292],{"name":283,"url":281},[294,105,295,296,297],"数字乡村","农业大模型","水肥一体化","农业社会化服务",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[300],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":302},{"candidates":303,"agricultural_evidence":51},[304,308,313,319,323,327,331],{"code":39,"score":100,"supported":51,"title":305,"abstract":306,"tags":307},[217],[217,296],[296],{"code":32,"score":17,"supported":51,"title":309,"abstract":311,"tags":312},[310],"大模型",[310],[295],{"code":314,"score":251,"supported":51,"title":315,"abstract":316,"tags":318},"G2",[],[317],"社会化服务",[297],{"code":71,"score":72,"supported":51,"title":320,"abstract":321,"tags":322},[],[75],[],{"code":221,"score":72,"supported":51,"title":324,"abstract":325,"tags":326},[],[224],[],{"code":65,"score":22,"supported":14,"title":328,"abstract":329,"tags":330},[],[],[105],{"code":332,"score":22,"supported":14,"title":333,"abstract":334,"tags":335},"I6",[],[],[294],[337,338],"水肥智慧大模型 德州","农业社会化服务 农业大模型 水肥一体化 数字乡村","水肥智慧大模型德州-3220","2026-09-23T00:04:29.579062Z",{"id":342,"title":343,"url":344,"summary":345,"summary_zh":346,"content":8,"source_name":347,"source_url":344,"published_at":348,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":349,"score_detail":350,"sources":353,"tags":355,"primary_topic":357,"secondary_topics":358,"classification":360,"search_phrases":385,"slug":388,"view_count":72,"doi":389,"paper":390,"created_at":403},2517,"Artificial Intelligence-Based Intelligent Fertigation Recommendation Systems for Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.64388\u002Firev10i3-1722972","Artificial-intelligence-enabled fertilizer and fertigation recommendation has progressed from single-task crop or fertilizer classification toward integrated decision-support architectures. However, predictive classification, agronomic dose calculation, irrigation scheduling, explainability, real-time sensing, and farmer-facing delivery are often studied separately. This paper presents a structured narrative review of these strands and an applied machine-learning case study using the publicly available Crop and Fertilizer Dataset for Western Maharashtra. The case study contains 4,513 records spanning five districts, 16 crops, and 19 fertilizer classes. Seven classifiers were compared using an 80:20 stratified train-test split with five-fold stratified cross-validation on the training set. XGBoost achieved 97.34% test accuracy and 94.21% ± 1.15% cross-validated accuracy, while Random Forest achieved 93.58% and 90.89% ± 0.80%, respectively. TreeSHAP analysis of Random Forest identified crop identity, potassium, and nitrogen as the leading predictors of the historical fertilizer class. These results are interpreted as a computational baseline rather than proof of agronomic optimality because the target label represents recorded fertilizer choices. The review also incorporates evidence on IoT\u002Fedge-cloud sensing, multilingual agricultural advisory, and federated learning. It concludes that RF\u002FSHAP, IoT sensing, and multilingual interfaces are established capabilities; a more defensible research direction is an auditable pipeline that separates fertilizer identity, nutrient dose, and application timing, connects explainable prediction to sequential scheduling, and independently benchmarks outputs against authoritative agronomic guidance. The proposed Intelligent Fertigation Recommendation System (IFRS) is therefore presented as a research framework requiring multi-season and field validation before claims of yield, water, nutrient-use-efficiency, or adoption benefits.","人工智能驱动的肥料与灌溉施肥推荐已从单一任务的作物或肥料分类，发展为集成式决策支持架构。然而，预测性分类、农艺用量计算、灌溉调度、可解释性、实时感知以及面向农户的交付往往被分别研究。本文对这些研究脉络进行了结构化叙述性综述，并基于公开的“西马哈拉施特拉邦作物与肥料数据集”开展了一项应用机器学习案例研究。该案例研究包含4，513条记录，涵盖五个地区、16种作物和19个肥料类别。采用80：20分层训练-测试划分，并在训练集上进行五折分层交叉验证，比较了七种分类器。XGBoost取得了97.34%的测试准确率和94.21% ± 1.15%的交叉验证准确率，而随机森林分别取得了93.58%和90.89% ± 0.80%。对随机森林的TreeSHAP分析表明，作物身份、钾和氮是历史肥料类别的主要预测因子。这些结果被解释为计算基线，而非农艺最优性的证明，因为目标标签代表的是有记录的肥料选择。该综述还纳入了关于物联网\u002F边缘-云感知、多语言农业咨询和联邦学习的证据。结论认为，随机森林\u002FSHAP、物联网感知和多语言界面已是成熟能力；更具可辩护性的研究方向是构建一条可审计的流水线，将肥料身份、养分用量和施用时机分离，将可解释预测与序贯调度相连接，并依据权威农艺指南对输出进行独立基准测试。因此，所提出的智能灌溉施肥推荐系统（IFRS）被作为一个研究框架提出，在声称产量、水分、养分利用效率或采用效益之前，仍需进行多季和田间验证。","Iconic Research and Engineering Journals","2026-09-14T00:00:00Z",62,{"impact":17,"substance":18,"depth":351,"authority":88,"freshness":214,"relevant":22,"comment":352},17,"对AI水肥推荐研究进行系统综述并给出可复现的机器学习基线，方法透明、结论审慎，但来源为普通工程类期刊且属综述性论文，产业影响有限，适合作为技术参考而非每日精选头条。",[354],{"name":347,"url":344},[105,106,107,356,296],"可解释AI",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[359],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":361},{"candidates":362,"agricultural_evidence":51},[363,369,373,377,381],{"code":39,"score":364,"supported":51,"title":365,"abstract":367,"tags":368},19,[366],"fertigation",[217,62,60,366],[296,107],{"code":32,"score":185,"supported":51,"title":370,"abstract":371,"tags":372},[129],[55],[106,356],{"code":65,"score":214,"supported":51,"title":374,"abstract":375,"tags":376},[143],[],[105],{"code":221,"score":88,"supported":51,"title":378,"abstract":379,"tags":380},[],[224,226],[],{"code":114,"score":88,"supported":51,"title":382,"abstract":383,"tags":384},[],[134,136],[],[386,387],"农业人工智能 水肥一体化 智慧农业 精准施肥","农业人工智能 水肥一体化","农业人工智能水肥一体化智慧农业精准施肥-2517","10.64388\u002Firev10i3-1722972",{"doi":389,"openalex_id":391,"authors":392,"venue":347,"cited_by_count":154,"oa_url":397,"card":398,"direction":170,"ingested_from":172},"W7212934364",[393,395],{"name":394,"orcid":8},"Shraddha S. Tayade",{"name":396,"orcid":8},"Yogesh V. Chimate","https:\u002F\u002Fwww.irejournals.com\u002Fformatedpaper\u002F1722972.pdf",{"tldr":399,"method":400,"finding":401,"direction":276,"opportunity":402},"综述AI施肥推荐研究，并用马哈拉施特拉数据集比较七种分类器，提出可审计智能施肥推荐框架。","结构化综述加机器学习案例，4513条记录，七分类器对比，XGBoost与Tree","XGBoost测试准确率97.34%，但标签仅为历史施肥选择，不能证明农艺最优。","可研究分离肥料种类、养分剂量与施用时序的可审计推荐流水线，并进行多季田间验证。","2026-09-15T23:30:12.675690Z",{"id":405,"title":406,"url":407,"summary":408,"summary_zh":409,"content":8,"source_name":410,"source_url":407,"published_at":411,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":412,"score_detail":413,"sources":415,"tags":417,"primary_topic":420,"secondary_topics":425,"classification":428,"search_phrases":474,"slug":477,"view_count":21,"doi":478,"paper":479,"created_at":489},1888,"Smart Technologies for Precision Nutrient Management in Smallholder Farming: A Comprehensive Review","https:\u002F\u002Fdoi.org\u002F10.4038\u002Fjur.v14i1.8134","Smallholder farmers with less than 2 hectares contribute more than 1\u002F3rd of the world food supply but continue to suffer the challenges of low nutrient use efficiency (30-40%), reduced soil fertility and lack of access to precision agriculture technologies. The aim of the review is to synthesize the evidence of 235 peer-reviewed articles (2010-2025) to assess the precision nutrient management (PNM) technologies, namely, in terms of their applicability, economic feasibility, and adoption capacity within the smallholder farming systems. Systematic literature review according to PRISMA, the analysis of articles on satellite and UAV-based remote sensing, IoT-enabled soil sensors, variable rate application systems, machine learning algorithms, and case studies worldwide were summarized. The application of variable rates can cut fertilizer use by 10-25 % and boost yields by 7-15 % but only small holders adopt it globally because of capital limitations, fragmented land holdings (1-2 ha), poor rural connectivity and insufficient extension services. Multi-source data forecasts nutrient needs with 85-92 % accuracy using machine learning algorithms (Random Forests, Support Vector Machines. Open-source sensor development, offline mobile decision support systems, equipment sharing cooperatives, blended finance (30–50% tech subsidies), and farmer involvement must all be part of the democratization of PNM for smallholders. Institutional adjustments, unified extension services, public-private cooperation, and supportive nutrient management regulations are all necessary for effective scaling. PNM adoption produces 15-30% nutrient runoff reduction, 10-20% greenhouse gas emission reductions, and 0.4-1.2 tons CO2-equivalent per hectare of soil carbon sequestration. PNM adoption produces 15-30% nutrient runoff reduction, 10-20% greenhouse gas emission reductions, and 0.4-1.2 tons CO2-equivalent per hectare of soil carbon sequestration. Economic analyses indicated that farms over 10 ha have positive returns, though smallholders required special support to overcome initial costs and limitations.","拥有不足2公顷土地的小农户贡献了全球粮食供应总量的三分之一以上，却持续面临养分利用效率低下（30-40%）、土壤肥力下降以及缺乏精准农业技术获取途径等挑战。本综述旨在综合235篇同行评审论文（2010-2025年）的证据，评估精准养分管理（PNM）技术在小农农业系统中的适用性、经济可行性和采纳能力。依据PRISMA方法进行系统性文献综述，总结了基于卫星和无人机遥感、物联网土壤传感器、变量施肥系统、机器学习算法及全球案例研究的分析结果。变量施肥技术的应用可减少化肥使用量10-25%，提高产量7-15%，但由于资金限制、土地碎片化（1-2公顷）、农村网络覆盖不足和推广服务欠缺，全球仅有少数小农户采纳该技术。利用机器学习算法（随机森林、支持向量机）进行多源数据预测，养分需求预测准确率可达85-92%。实现小农户PNM技术的普及化，必须包括开源传感器开发、离线移动决策支持系统、设备共享合作社、混合融资（30-50%技术补贴）以及农户参与等多方面措施。有效的规模化推广还需要制度调整、统一的推广服务、公私合作以及支持性养分管理法规的配套。PNM技术的采纳可减少养分径流15-30%，降低温室气体排放10-20%，并实现每公顷0.4-1.2吨二氧化碳当量的土壤碳封存。经济分析表明，10公顷以上的农场可获得正收益，但小农户需要特殊支持以克服初始成本和限制因素。","Journal of the University of Ruhuna","2026-09-07T00:00:00Z",72,{"impact":18,"substance":288,"depth":18,"authority":17,"freshness":21,"relevant":22,"comment":414},"系统综述小农户精准养分管理技术，数据详实，但时效性低，适合专题参考。",[416],{"name":410,"url":407},[105,107,418,55,419],"农业遥感","小农户",{"code":314,"slug":421,"name":422,"path":423,"parent_code":424},"farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[426,427],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":429},{"candidates":430,"agricultural_evidence":51},[431,436,440,444,448,453,460,464,468],{"code":314,"score":100,"supported":51,"title":432,"abstract":434,"tags":435},[433],"smallholder",[419,433],[419],{"code":39,"score":13,"supported":51,"title":437,"abstract":438,"tags":439},[],[217,218,60],[107],{"code":32,"score":13,"supported":51,"title":441,"abstract":442,"tags":443},[],[55,128,53],[55],{"code":65,"score":139,"supported":51,"title":445,"abstract":446,"tags":447},[],[142,143],[105],{"code":78,"score":139,"supported":51,"title":449,"abstract":450,"tags":452},[],[190,451],"remote 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Liyanage",{"tldr":485,"method":486,"finding":487,"direction":170,"opportunity":488},"综述小农户精准养分管理技术，分析其适用性、经济可行性与采纳障碍。","PRISMA系统综述235篇文献，分析遥感、物联网、变量施肥及机器学习案例。","变量施肥可减肥10-25%并增产7-15%，但小农户因资金、土地细碎等采纳率低。","小农户精准养分管理技术民主化路径，如低成本传感器、离线决策支持系统及合作社共享模式。","2026-09-08T23:30:08.156887Z",{"id":491,"title":492,"url":493,"summary":494,"summary_zh":8,"content":495,"source_name":496,"source_url":8,"published_at":497,"category":285,"cover_url":8,"hotness":13,"is_selected":51,"score":498,"score_detail":499,"sources":503,"tags":505,"primary_topic":510,"secondary_topics":511,"classification":513,"search_phrases":533,"slug":536,"view_count":72,"doi":8,"paper":8,"created_at":537},945,"第二十七届全国肥料信息交流暨产品交易会发布智慧肥水大模型 奇稷·肥水大模型同步上线","https:\u002F\u002Ffinance.sina.com.cn\u002Fjjxw\u002F2026-08-25\u002Fdoc-inipmxch1919969.shtml","日前在内蒙古呼和浩特举行的第二十七届全国肥料信息交流暨产品交易会上，全国农业技术推广服务中心正式发布智慧肥水大模型，集成20年全国测土配方施肥数据、每年30万户农户施肥调查数据与全国土壤墒情监测数据，构建土壤养分查询、墒情监测预警、农田气象感知、植株营养诊断、肥水智慧推荐等八大智能体。同会上国家农业信息化工程技术研究中心智慧水肥技术团队联合农芯科技(北京)发布国内首个农业水肥管理垂直大模型「奇稷·肥水大模型」，具备问土、探墒、知天、诊苗、巧算、识肥、智报、慧答八大核心功能，填补通用大模型在农田水肥精准管理方面的专业空白。截至2025年底全国水肥一体化技术应用面积达1.88亿亩，其中粮油作物8800万亩。会议搭建水肥科研、生产、推广、应用对接平台，吸引约320家企业参展、超120万人次观展。","在日前于内蒙古呼和浩特举行的第二十七届全国肥料信息交流暨产品交易会上，多名专家齐聚一堂，热议肥水精准高效利用，全面支撑大面积单产提升。\n\n“我国化肥产能大，施肥量也大，一些化肥产品和农业生产需求的匹配度不够，限制了粮食产能的提升。未来通过精准施用肥料，进一步提升效率、促进增产是我们的努力方向。”中国工程院院士、中国农业大学教授张福锁说，当前我国农业生产已进入规模化、机械化、智能化的时代，要大力发展适配机械施肥、水肥一体化和无人机作业的专用肥料，同时充分挖掘有机养分潜力，推动肥料产业绿色低碳转型。\n\n国家农业智能装备工程技术研究中心研究员郑文刚认为，肥水精准高效施用的核心是“测得准、配得准、施得准”，即土壤水分和养分检测快而准、灌溉制度和肥料配方生成精而准、作物水肥实施精而准。他表示，未来要更好地借助AI大模型实现不同场景下的水肥耦合调控，同时加强智慧水肥相关标准制定，加快智能水肥车、施肥无人机等智能装备的推广应用。\n\n全国农业技术推广服务中心数据显示，截至2025年底，全国水肥一体化技术应用面积达到1.88亿亩，其中粮油作物8800万亩。内蒙古通辽、新疆伊犁等地建设玉米百万亩、千万亩水肥一体化“超吨粮田”等典型场景，实现水肥利用效率和粮食单产“双提升”。水肥一体化已经成为玉米、小麦、大豆等粮油作物大面积单产提升的关键举措。\n\n大会上，全国农业技术推广服务中心正式发布了智慧肥水大模型。这个模型基于20年全国测土配方施肥数据、每年30万户农户施肥调查数据以及全国土壤墒情监测数据等构建，集成了土壤养分查询、墒情监测预警、农田气象感知、植株营养诊断、肥水智慧推荐等八大智能体，是水肥提单产的专业工具和智能助手。\n\n据了解，第二十七届全国肥料信息交流暨产品交易会以“精准肥水 增粮增效”为主题，由全国农业技术推广服务中心、中国农业技术推广协会主办。交易会搭建水肥科研、生产、推广、应用等多方对接平台，吸引约320家肥料、节水、农机与智能装备等企业参展，超120万人次线上线下观展。","经济参考报 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