[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3518":3,"related-3518":75},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":8,"secondary_topics":29,"classification":30,"search_phrases":57,"slug":60,"view_count":20,"doi":61,"paper":62,"created_at":74},3518,"Soil physical data integration in commercial precision agriculture platforms: barriers to compaction-relevant decision support","https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.22005.74727","Soil physical data integration in commercial precision agriculture platforms: barriers to compaction-relevant decision support。OpenAlex",null,"OpenAlex","2026-09-24T00:00:00Z","论文",10,false,47,{"impact":16,"substance":17,"depth":18,"authority":18,"freshness":19,"relevant":20,"comment":21},8,6,12,9,1,"学术论文探讨商业精准农业平台土壤物理数据整合障碍，与农业信息化相关但偏学术、公共价值有限，时效新。",[23],{"name":9,"url":6},[25,26,27,28],"智慧农业","精准农业","决策支持","土壤数据",[],{"status":31,"locked":13,"version":32,"method":33,"evidence":34},"needs_review","2026-10-22.2","rules",{"candidates":35,"agricultural_evidence":39,"reason":56},[36,44,50],{"code":37,"score":38,"supported":39,"title":40,"abstract":42,"tags":43},"I1",13,true,[41],"precision agriculture",[41],[25,26],{"code":45,"score":18,"supported":39,"title":46,"abstract":48,"tags":49},"H2",[47],"soil",[47],[28],{"code":51,"score":18,"supported":39,"title":52,"abstract":54,"tags":55},"I2",[53],"decision support",[53],[27],"多个产业证据相当，需要确认主归属",[58,59],"精准农业 土壤物理数据 平台","决策支持 土壤数据 智慧农业 精准农业","精准农业土壤物理数据平台-3518","10.13140\u002Frg.2.2.22005.74727",{"doi":61,"openalex_id":63,"authors":64,"venue":8,"cited_by_count":71,"oa_url":6,"card":8,"direction":72,"ingested_from":73},"W7214201555",[65,68],{"name":66,"orcid":67},"Hanna Radziuk","https:\u002F\u002Forcid.org\u002F0000-0001-5279-2175",{"name":69,"orcid":70},"Marcin Świtoniak","https:\u002F\u002Forcid.org\u002F0000-0002-9907-7088",0,"农业人工智能与决策模型","openalex","2026-09-25T23:30:59.544316Z",{"total":17,"page":20,"page_size":17,"items":76},[77,139,216,273,332,427],{"id":78,"title":79,"url":80,"summary":81,"summary_zh":82,"content":8,"source_name":83,"source_url":80,"published_at":84,"category":11,"cover_url":8,"hotness":85,"is_selected":13,"score":86,"score_detail":87,"sources":91,"tags":95,"primary_topic":98,"secondary_topics":103,"classification":104,"search_phrases":122,"slug":125,"view_count":126,"doi":127,"paper":128,"created_at":138},3282,"Design and Implementation of an Ensemble Learning Based Decision Support Model for Crop Selection in Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22895811","Soil composition governs which crop can be grown profitably in a given field, and the relationship between soil variables and crop suitability is nonlinear, interacting and therefore poorly served by heuristic rules. This paper presents SCR-XGB, a five-layer framework that couples a disciplined data-conditioning stage with a regularised gradient-boosted tree ensemble for crop recommendation from soil and climatic parameters. The acquisition layer collects nitrogen, phosphorus and potassium concentration together with temperature, humidity, soil pH and rainfall; the conditioning layer imputes missing values and removes outliers by an interquartile filter; the feature engineering layer derives nutrient ratios, normalises and standardises the numeric fields and encodes the crop label; the ensemble layer fits an additive sequence of regression trees under a regularised objective with shrinkage and column subsampling; and the recommendation layer issues a ranked crop list with per-crop confidence. Four algorithms are specified in full, covering conditioning, feature construction, boosted training and inference, and a complexity analysis is given for each stage. Evaluated on a public corpus of soil and climate records against five baseline learners trained over the identical feature matrix, the proposed framework attains 99.31% accuracy, 100% precision, 99% recall and an F1-score of 99%, ahead of naive Bayes and random forest at 99.09%, support vector machine at 97.95%, logistic regression at 95.22% and a single decision tree at 90.00%. The 9.31 percentage point margin over the single tree, set against the 0.22 point margin over the strongest baseline, quantifies the benefit of boosting and shows where the remaining headroom on this task actually lies.","土壤组成决定了特定田块适宜种植何种作物才能获得经济效益，而土壤变量与作物适宜性之间的关系是非线性的、相互作用的，因此启发式规则难以有效处理这一问题。本文提出SCR-XGB，一个五层框架，将规范化的数据调理阶段与正则化梯度提升树集成相结合，用于基于土壤和气候参数的作物推荐。采集层收集氮、磷、钾浓度以及温度、湿度、土壤pH值和降雨量；调理层通过四分位距滤波器插补缺失值并剔除异常值；特征工程层推导养分比率，对数值字段进行归一化和标准化，并对作物标签进行编码；集成层在带有收缩和列子采样的正则化目标函数下拟合加性回归树序列；推荐层输出带有每种作物置信度的排序作物列表。本文完整给出了四种算法，涵盖调理、特征构建、提升训练和推理，并对每个阶段进行了复杂度分析。在公开的土壤和气候记录语料库上，与在相同特征矩阵上训练的五个基线学习器进行对比评估，所提框架达到了99.31%的准确率、100%的精确率、99%的召回率和99%的F1分数，优于朴素贝叶斯和随机森林的99.09%、支持向量机的97.95%、逻辑回归的95.22%以及单棵决策树的90.00%。相较于单棵决策树9.31个百分点的优势，与相较于最强基线0.22个百分点的优势相比，量化了提升方法的收益，并揭示了该任务上剩余提升空间的实际所在。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-22T00:00:00Z",25,71,{"impact":18,"substance":88,"depth":89,"authority":18,"freshness":19,"relevant":20,"comment":90},21,17,"方法完整、对比基线充分，但属常规机器学习应用论文，公共价值有限，可作主题聚合素材而非每日精选。",[92,93],{"name":83,"url":80},{"name":83,"url":94},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22895812",[25,96,26,97,28],"农业人工智能","作物推荐",{"code":51,"slug":99,"name":100,"path":101,"parent_code":102},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":105,"locked":13,"version":32,"method":33,"evidence":106},"classified",{"candidates":107,"agricultural_evidence":39},[108,112,116],{"code":37,"score":12,"supported":39,"title":109,"abstract":110,"tags":111},[41],[],[25,26],{"code":51,"score":19,"supported":39,"title":113,"abstract":114,"tags":115},[53],[],[96],{"code":45,"score":117,"supported":39,"title":118,"abstract":119,"tags":121},7,[],[120,47],"土壤",[28],[123,124],"SCR-XGB 作物推荐 土壤","集成学习 精准农业 选种","SCR-XGB作物推荐土壤-3282",2,"10.5281\u002Fzenodo.22895811",{"doi":127,"openalex_id":129,"authors":130,"venue":83,"cited_by_count":71,"oa_url":80,"card":133,"direction":72,"ingested_from":73},"W7214043002",[131],{"name":132,"orcid":8},"Prof. Nagendra Patel Sahil Verma",{"tldr":134,"method":135,"finding":136,"direction":72,"opportunity":137},"提出SCR-XGB五层框架，用梯度提升树集成从土壤和气候参数推荐作物。","基于土壤气候数据，采用正则化梯度提升树集成，含缺失值插补、异常值过滤和特征工程。","模型准确率达99.31%，优于朴素贝叶斯、随机森林等基线，比单决策树提升9.31个百分点。","可探索将模型部署到田间实时决策，并融合遥感与物联网数据提升泛化能力。","2026-09-23T23:30:35.342497Z",{"id":140,"title":141,"url":142,"summary":143,"summary_zh":144,"content":8,"source_name":145,"source_url":142,"published_at":146,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":147,"score_detail":148,"sources":152,"tags":154,"primary_topic":155,"secondary_topics":156,"classification":166,"search_phrases":196,"slug":199,"view_count":126,"doi":200,"paper":201,"created_at":215},2509,"Artificial Intelligence-Based Crop Recommendation Using Soil and Climate Data: A Comprehensive Review of Machine Learning, Deep Learning, and Smart Agriculture Approaches","https:\u002F\u002Fdoi.org\u002F10.64388\u002Firev10i3-1722922","Crop recommendation systems that integrate soil and climate data with artificial intelligence (AI) have expanded rapidly since 2020, spanning classical machine learning (ML), ensemble methods, deep learning, explainable AI (XAI), and Internet of Things (IoT)-enabled sensing. This review critically synthesizes 35 sources — 33 peer-reviewed journal articles and conference papers plus 2 preprints retained only for background context — comprising 12 studies verified in full against their primary text and 23 studies verified at the bibliographic level, to examine what has been attempted, which data and algorithms have been used, and how reliable the reported results are. The reviewed literature shows convergent use of a narrow feature set (nitrogen, phosphorus, potassium, temperature, humidity, pH, and rainfall) and recurring near-ceiling accuracy, including cases at or above 98% [1], [5], [6] and, in one case, a reported 1.00 across accuracy, precision, recall, and F1-score following class-balancing [7]. Cross-examination of dataset descriptions across studies reveals inconsistent provenance and documentation: structurally similar seven-feature datasets are described with different national contexts and reported sample sizes ranging from 2,100 to 3,000 records [5], [6], [9]. Validation practice is dominated by random hold-out or k-fold cross-validation; among the studies examined in full, only one tested spatial cross-validation, reporting a substantial performance decline (AUC 0.89 to 0.55–0.62) relative to random-split results [4]. Explainable AI and uncertainty quantification remain minority practices in the reviewed literature. A prior conference proceedings paper self-described as a","自2020年以来，将土壤和气候数据与人工智能（AI）相结合的作物推荐系统迅速扩展，涵盖经典机器学习（ML）、集成方法、深度学习、可解释人工智能（XAI）以及物联网（IoT）赋能的传感技术。本综述批判性地综合了35个来源——33篇同行评审期刊论文和会议论文，以及2篇仅用于背景参考的预印本——其中包括12项经全文核实的研究和23项经书目层面核实的研究，以考察已尝试的研究方向、所使用的数据和算法，以及所报告结果的可靠性。所综述的文献显示，特征集使用趋同且范围狭窄（氮、磷、钾、温度、湿度、pH和降雨量），准确率反复接近上限，包括达到或超过98%的案例[1], [5], [6]，以及一例在类别平衡后准确率、精确率、召回率和F1分数均报告为1.00的研究[7]。对各项研究中数据集描述的交叉审查揭示了来源和文档记录的不一致：结构相似的七特征数据集被描述为不同的国家背景，报告的样本量从2,100到3,000条记录不等[5], [6], [9]。验证实践以随机留出法或k折交叉验证为主；在经全文审查的研究中，仅有一项测试了空间交叉验证，报告称相对于随机划分结果，性能显著下降（AUC从0.89降至0.55–0.62）[4]。可解释人工智能和不确定性量化在所综述文献中仍属少数实践。一篇先前会议论文集中自述为","Iconic Research and Engineering Journals","2026-09-14T00:00:00Z",61,{"impact":18,"substance":149,"depth":150,"authority":17,"freshness":19,"relevant":20,"comment":151},18,16,"系统综述揭示作物推荐模型普遍存在数据集来源混乱与验证方法单一（仅一项空间交叉验证即大幅掉点）的问题，对智慧农业AI落地有实质警示价值，但期刊影响力有限。",[153],{"name":145,"url":142},[25,96,26,97,28],{"code":51,"slug":99,"name":100,"path":101,"parent_code":102},[157,161],{"code":45,"slug":47,"name":158,"path":159,"parent_code":160},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":162,"slug":163,"name":164,"path":165,"parent_code":102},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":105,"locked":13,"version":32,"method":33,"evidence":167},{"candidates":168,"agricultural_evidence":39},[169,179,184,189],{"code":51,"score":170,"supported":39,"title":171,"abstract":175,"tags":178},34,[172,173,174],"machine learning","deep learning","artificial intelligence",[176,177,172,173,174],"深度学习","机器学习",[96],{"code":45,"score":180,"supported":39,"title":181,"abstract":182,"tags":183},15,[47],[120,47],[28],{"code":37,"score":12,"supported":39,"title":185,"abstract":187,"tags":188},[186],"smart agriculture",[],[25,26],{"code":162,"score":19,"supported":39,"title":190,"abstract":191,"tags":195},[],[192,193,194],"物联网","internet of things","IoT",[],[197,198],"农业人工智能 作物推荐 土壤数据 智慧农业","农业人工智能 作物推荐","农业人工智能作物推荐土壤数据智慧农业-2509","10.64388\u002Firev10i3-1722922",{"doi":200,"openalex_id":202,"authors":203,"venue":145,"cited_by_count":71,"oa_url":208,"card":209,"direction":214,"ingested_from":73},"W7212594050",[204,206],{"name":205,"orcid":8},"Snehal Sanjay Raut",{"name":207,"orcid":8},"Yogesh V. Chimate","https:\u002F\u002Fwww.irejournals.com\u002Fformatedpaper\u002F1722922.pdf",{"tldr":210,"method":211,"finding":212,"direction":72,"opportunity":213},"综述AI作物推荐研究，指出高准确率多源于数据与验证缺陷。","系统综述35篇文献，对比ML、DL、XAI与IoT方法及验证方式。","常用7特征数据集来源不一，随机验证致准确率虚高，空间验证性能骤降。","需建立标准化数据集并推广空间交叉验证与不确定性量化，提升模型真实泛化能力。","智慧农业 \u002F 农业物联网","2026-09-15T23:30:08.397298Z",{"id":217,"title":218,"url":219,"summary":220,"summary_zh":221,"content":8,"source_name":222,"source_url":219,"published_at":223,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":224,"score_detail":225,"sources":228,"tags":230,"primary_topic":231,"secondary_topics":232,"classification":233,"search_phrases":255,"slug":258,"view_count":20,"doi":259,"paper":260,"created_at":272},2338,"An optimized machine learning approach for reliable agronomic parameter prediction in precision farming systems","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-71592-1","Optimized machine learning models are very important for improving predictive performance in precision agriculture because they let us analyze soil and environmental data in a data-driven way. But conventional predictive methods often can’t be used to make generalizations because agronomic data is often very variable, has nonlinear interactions, and is very different from one another. The study presents an enhanced machine learning-based predictive framework for estimating agricultural parameters utilizing structured numerical soil and environmental datasets. The framework combines systematic data preprocessing, feature selection, and hyperparameter optimization to make models more stable and reliable. The model performance was evaluated using five-fold cross-validation and standard regression metrics, including the Coefficient of Determination (R 2 ), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The average R 2 is 0.696 ± 0.148, the RMSE is 5.160 ± 1.571, and the MAE is 4.636 ± 1.545, which shows that the model can make accurate predictions across all validation folds. Further the classification of the growth stages of spinach is done with an accuracy of 84.63% and with the precision of 85% using the proposed Hybrid ensemble model. The results show that the proposed framework works well with nonlinear agricultural data and could be used for data-driven decisions in precision farming systems.","优化的机器学习模型对于提升精准农业中的预测性能至关重要，因为它使我们能够以数据驱动的方式分析土壤和环境数据。然而，传统的预测方法往往无法用于泛化，因为农艺数据通常变异性很大、具有非线性交互作用，且彼此之间差异显著。本研究提出了一种基于增强机器学习的预测框架，利用结构化数值土壤和环境数据集来估算农业参数。该框架结合了系统化的数据预处理、特征选择和超参数优化，使模型更加稳定可靠。采用五折交叉验证和标准回归指标评估模型性能，包括决定系数（R²）、均方根误差（RMSE）和平均绝对误差（MAE）。平均R²为0.696 ± 0.148，RMSE为5.160 ± 1.571，MAE为4.636 ± 1.545，表明该模型在所有验证折中均能做出准确预测。此外，利用所提出的混合集成模型对菠菜生长阶段进行分类，准确率达到84.63%，精确率达到85%。结果表明，所提出的框架能够很好地处理非线性农业数据，可用于精准农业系统中的数据驱动决策。","Scientific Reports","2026-09-12T00:00:00Z",67,{"impact":18,"substance":149,"depth":180,"authority":226,"freshness":16,"relevant":20,"comment":227},14,"该论文提出融合预处理、特征选择与超参数优化的机器学习框架，在土壤环境数据上取得R²约0.70的预测表现并以84.63%准确率识别菠菜生长期，方法扎实但属常规模型优化，产业影响有限，可作为智慧农业技术参考。",[229],{"name":222,"url":219},[25,96,177,26,28],{"code":51,"slug":99,"name":100,"path":101,"parent_code":102},[],{"status":105,"locked":13,"version":32,"method":33,"evidence":234},{"candidates":235,"agricultural_evidence":39},[236,240,244,248],{"code":51,"score":150,"supported":39,"title":237,"abstract":238,"tags":239},[172],[177,172],[96,177],{"code":37,"score":16,"supported":39,"title":241,"abstract":242,"tags":243},[],[26,41],[25,26],{"code":45,"score":117,"supported":39,"title":245,"abstract":246,"tags":247},[],[120,47],[28],{"code":249,"score":250,"supported":39,"title":251,"abstract":252,"tags":254},"I6",3,[],[253],"agricultural data",[],[256,257],"农业人工智能 土壤数据 智慧农业 机器学习","农业人工智能 土壤数据","农业人工智能土壤数据智慧农业机器学习-2338","10.1038\u002Fs41598-026-71592-1",{"doi":259,"openalex_id":261,"authors":262,"venue":222,"cited_by_count":71,"oa_url":219,"card":267,"direction":72,"ingested_from":73},"W7212396250",[263,265],{"name":264,"orcid":8},"T. Suba",{"name":266,"orcid":8},"K. Lakshmi Joshitha",{"tldr":268,"method":269,"finding":270,"direction":72,"opportunity":271},"提出优化机器学习框架，用土壤环境数据预测农艺参数并分类菠菜生长阶段。","数据预处理、特征选择、超参数优化，五折交叉验证与混合集成模型。","回归平均R²为0.696，菠菜生长阶段分类准确率达84.63%。","可探索跨作物、跨区域迁移学习与可解释性，提升非线性农艺数据泛化能力。","2026-09-13T23:30:43.564799Z",{"id":274,"title":275,"url":276,"summary":277,"summary_zh":278,"content":8,"source_name":279,"source_url":276,"published_at":280,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":281,"score_detail":282,"sources":285,"tags":287,"primary_topic":290,"secondary_topics":291,"classification":292,"search_phrases":308,"slug":311,"view_count":20,"doi":312,"paper":313,"created_at":331},2113,"Orthogonal delineation of site-specific management zones using soil data: A multi-property optimization approach","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112399","Orthogonal delineation of site-specific management zones using soil data: A multi-property optimization approach。Computers and Electronics in Agriculture","基于土壤数据的特定管理区正交划分：一种多属性优化方法。《计算机与电子农业》","Computers and Electronics in Agriculture","2026-09-11T00:00:00Z",72,{"impact":18,"substance":283,"depth":89,"authority":226,"freshness":19,"relevant":20,"comment":284},20,"核心期刊论文，提出基于多属性优化的管理分区正交划分方法，方法新颖且对精准农业变量管理有直接参考价值，但属细分领域学术进展，公共影响有限。",[286],{"name":279,"url":276},[25,288,26,28,289],"变量施肥","管理分区",{"code":45,"slug":47,"name":158,"path":159,"parent_code":160},[],{"status":105,"locked":13,"version":32,"method":33,"evidence":293},{"candidates":294,"agricultural_evidence":39},[295,299,303],{"code":45,"score":180,"supported":39,"title":296,"abstract":297,"tags":298},[47],[120,47],[28],{"code":37,"score":126,"supported":13,"title":300,"abstract":301,"tags":302},[],[],[25,26],{"code":304,"score":20,"supported":13,"title":305,"abstract":306,"tags":307},"F3",[],[],[288],[309,310],"变量施肥 土壤数据 智慧农业 管理分区","变量施肥 土壤数据","变量施肥土壤数据智慧农业管理分区-2113","10.1016\u002Fj.compag.2026.112399",{"doi":312,"openalex_id":314,"authors":315,"venue":279,"cited_by_count":71,"oa_url":324,"card":325,"direction":329,"ingested_from":73},"W7157647968",[316,319,321],{"name":317,"orcid":318},"Salvador J. Vicencio-Medina","https:\u002F\u002Forcid.org\u002F0000-0002-3285-097X",{"name":320,"orcid":8},"Armin Lüer‐Villagra",{"name":322,"orcid":323},"Gonzalo Méndez-Vogel","https:\u002F\u002Forcid.org\u002F0000-0002-6455-7305","https:\u002F\u002Ffigshare.com\u002Farticles\u002Fjournal_contribution\u002FOrthogonal_delineation_of_site-specific_management_zones_using_soil_data_a_multi-property_optimization_approach\u002F32095483",{"tldr":326,"method":327,"finding":328,"direction":329,"opportunity":330},"提出多属性优化方法，利用土壤数据正交划分特定地点管理分区。","多属性优化与正交划分，基于土壤属性数据。","多属性优化可有效生成管理分区，提升分区独立性。","农业遥感与作物表型","可结合遥感与实时传感器数据，发展动态管理分区与变量施肥决策。","2026-09-11T23:30:01.392377Z",{"id":333,"title":334,"url":335,"summary":336,"summary_zh":337,"content":8,"source_name":338,"source_url":335,"published_at":339,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":340,"score_detail":341,"sources":344,"tags":346,"primary_topic":348,"secondary_topics":349,"classification":356,"search_phrases":403,"slug":406,"view_count":71,"doi":407,"paper":408,"created_at":426},5553,"Artificial Intelligence–Driven Precision Agriculture Systems: Engineering Frameworks for Climate-Resilient Food Production","https:\u002F\u002Fdoi.org\u002F10.53982\u002Fajerd.2026.01.14-j","Climate change, soil issues, water scarcity, and the increasing need for food worldwide mean we need smart farming systems that can manage resources flexibly. Old farming methods used the same inputs and simple rules for decisions. AI can help us manage resources better by adapting and optimizing them as we deal with complex environmental factors. This paper looks closely at AI-powered precision agriculture systems designed to make food production more resilient to changing weather. The authors reviewed research from 2021 to 2026 in agricultural engineering, computer engineering, and environmental systems. The authors examined how machine learning, deep learning, reinforcement learning, computer vision, robotics, and IoT sensors are used for predicting crop yields, spotting diseases, improving irrigation, managing nutrients, and automating field tasks. Our analysis shows that hybrid deep learning models (CNN-LSTM) are better than single models for predicting crop yields over space and time. Reinforcement learning algorithms are also better at adjusting irrigation cycles when weather is unpredictable. Using edge computing for system design reduces delays and improves real-time decision-making compared to traditional cloud-based systems. However, there are still big challenges with understanding how AI models work, having enough diverse data, ensuring cybersecurity, setting common standards, and making these systems affordable, especially in developing countries. To tackle these problems, we suggest a framework that combines AI analysis, distributed sensor networks, adaptive controls, and climate risk assessments. The authors provided critical insights into essential research gaps and outline a technological roadmap towards achieving sustainable, resilient and intelligent agricultural systems.","气候变化、土壤问题、水资源短缺以及全球日益增长的粮食需求，意味着我们需要能够灵活管理资源的智能农业系统。传统农业方法对相同的投入采用固定模式，并依赖简单规则进行决策。人工智能可以帮助我们更好地管理资源，通过适应和优化资源利用来应对复杂的环境因素。本文深入探讨了人工智能驱动的精准农业系统，旨在使粮食生产对不断变化的天气更具韧性。作者综述了2021年至2026年间农业工程、计算机工程和环境系统领域的研究。作者考察了机器学习、深度学习、强化学习、计算机视觉、机器人技术及物联网传感器如何被用于作物产量预测、病害识别、灌溉优化、养分管理和田间作业自动化。我们的分析表明，混合深度学习模型（CNN-LSTM）在时空作物产量预测方面优于单一模型。在天气不可预测的情况下，强化学习算法在调整灌溉周期方面也表现更优。与传统基于云的系统相比，采用边缘计算进行系统设计可降低延迟并改善实时决策能力。然而，在理解人工智能模型的工作原理、获取足够多样化的数据、确保网络安全、制定通用标准以及使这些系统具备可负担性方面，仍存在重大挑战，尤其是在发展中国家。为应对这些问题，我们提出了一个融合人工智能分析、分布式传感器网络、自适应控制和气候风险评估的框架。作者对关键研究空白提供了重要见解，并勾勒了实现可持续、韧性和智能农业系统的技术路线图。","ABUAD Journal of Engineering Research and Development (AJERD)","2026-10-21T00:00:00Z",80,{"impact":149,"substance":342,"depth":149,"authority":38,"freshness":19,"relevant":20,"comment":343},22,"系统综述AI驱动精准农业的工程框架与技术路线，方法梳理扎实、结论有参考价值，但属综述类论文，产业落地影响有限。",[345],{"name":338,"url":335},[25,96,192,26,347],"气候韧性",{"code":51,"slug":99,"name":100,"path":101,"parent_code":102},[350,351],{"code":162,"slug":163,"name":164,"path":165,"parent_code":102},{"code":352,"slug":353,"name":354,"path":355,"parent_code":160},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":105,"locked":13,"version":32,"method":33,"evidence":357},{"candidates":358,"agricultural_evidence":39},[359,365,369,375,381,388,392,398],{"code":51,"score":149,"supported":39,"title":360,"abstract":361,"tags":364},[174],[176,177,362,172,173,363],"计算机视觉","computer vision",[96],{"code":37,"score":150,"supported":39,"title":366,"abstract":367,"tags":368},[41],[26,41],[25,26],{"code":162,"score":12,"supported":39,"title":370,"abstract":371,"tags":374},[],[192,372,194,373],"边缘计算","edge computing",[192],{"code":352,"score":19,"supported":39,"title":376,"abstract":377,"tags":380},[],[378,379,353],"灌溉","水资源",[],{"code":382,"score":117,"supported":39,"title":383,"abstract":384,"tags":387},"H7",[],[385,386],"气候变化","climate change",[347],{"code":45,"score":17,"supported":39,"title":389,"abstract":390,"tags":391},[],[120,47],[],{"code":393,"score":250,"supported":39,"title":394,"abstract":395,"tags":397},"A7",[],[396],"间作",[],{"code":304,"score":250,"supported":39,"title":399,"abstract":400,"tags":402},[],[401],"养分管理",[],[404,405],"AI 精准农业 气候韧性","CNN-LSTM 作物产量预测","AI精准农业气候韧性-5553","10.53982\u002Fajerd.2026.01.14-j",{"doi":407,"openalex_id":409,"authors":410,"venue":338,"cited_by_count":71,"oa_url":335,"card":421,"direction":214,"ingested_from":73},"W7223121456",[411,414,416,418],{"name":412,"orcid":413},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":415,"orcid":8},"Oluwadare OYEBODE",{"name":417,"orcid":8},"Iretiayo Adelaiye",{"name":419,"orcid":420},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":422,"method":423,"finding":424,"direction":72,"opportunity":425},"综述2021-2026年AI驱动精准农业系统，提出气候韧性粮食生产工程框架。","综述机器学习、深度学习、强化学习、计算机视觉、机器人与物联网在精准农业中的应用。","CNN-LSTM混合模型和强化学习在产量预测与灌溉调度上优于单一方法，边缘计算提升实时性。","可研究边缘计算与强化学习结合的轻量级灌溉决策系统，并探索发展中国家低成本部署与标准化路径。","2026-10-22T11:20:18.151376Z",{"id":428,"title":429,"url":430,"summary":431,"summary_zh":8,"content":8,"source_name":432,"source_url":430,"published_at":433,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":434,"score_detail":435,"sources":437,"tags":439,"primary_topic":442,"secondary_topics":446,"classification":447,"search_phrases":459,"slug":462,"view_count":71,"doi":463,"paper":464,"created_at":479},5539,"Robust and transparent crop-row detection from RGB field imagery for vision-based agricultural guidance systems","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10473-z","Robust and transparent crop-row detection from RGB field imagery for vision-based agricultural guidance systems。Precision Agriculture","Precision Agriculture","2026-10-22T00:00:00Z",79,{"impact":150,"substance":88,"depth":149,"authority":226,"freshness":12,"relevant":20,"comment":436},"核心期刊新论文，提出鲁棒且可解释的RGB作物行检测方法，对视觉导航农机具参考价值明确，但属细分技术进展，未达重大突破层级。",[438],{"name":432,"url":430},[25,96,26,440,441],"机器视觉","作物行检测",{"code":37,"slug":443,"name":444,"path":445,"parent_code":102},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":105,"locked":13,"version":32,"method":33,"evidence":448},{"candidates":449,"agricultural_evidence":39},[450,455],{"code":37,"score":451,"supported":39,"title":452,"abstract":453,"tags":454},5,[],[41],[25,26],{"code":51,"score":126,"supported":13,"title":456,"abstract":457,"tags":458},[],[],[96,440],[460,461],"RGB 作物行检测","农业人工智能 作物行检测 智慧农业 机器视觉","RGB作物行检测-5539","10.1007\u002Fs11119-026-10473-z",{"doi":463,"openalex_id":465,"authors":466,"venue":432,"cited_by_count":71,"oa_url":8,"card":8,"direction":8,"ingested_from":73},"W7223117828",[467,470,473,476],{"name":468,"orcid":469},"Esther Gascó","https:\u002F\u002Forcid.org\u002F0000-0003-1798-4483",{"name":471,"orcid":472},"Clara I. López-González","https:\u002F\u002Forcid.org\u002F0000-0002-2755-1692",{"name":474,"orcid":475},"Eva Besada-Portas","https:\u002F\u002Forcid.org\u002F0000-0001-6129-4653",{"name":477,"orcid":478},"Gonzalo Pájares","https:\u002F\u002Forcid.org\u002F0000-0003-0915-6282","2026-10-22T11:20:09.678587Z"]