[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3282":3,"related-3282":87},{"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":27,"primary_topic":33,"secondary_topics":39,"classification":40,"search_phrases":67,"slug":70,"view_count":71,"doi":72,"paper":73,"created_at":86},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。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-09-22T00:00:00Z","论文",25,false,71,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},12,21,17,9,1,"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。",[24,25],{"name":10,"url":6},{"name":10,"url":26},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22895812",[28,29,30,31,32],"智慧农业","农业人工智能","精准农业","作物推荐","土壤数据",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":41,"locked":14,"version":42,"method":43,"evidence":44},"classified","2026-10-22.2","rules",{"candidates":45,"agricultural_evidence":49},[46,54,59],{"code":47,"score":48,"supported":49,"title":50,"abstract":52,"tags":53},"I1",10,true,[51],"precision agriculture",[],[28,30],{"code":34,"score":20,"supported":49,"title":55,"abstract":57,"tags":58},[56],"decision support",[],[29],{"code":60,"score":61,"supported":49,"title":62,"abstract":63,"tags":66},"H2",7,[],[64,65],"土壤","soil",[32],[68,69],"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":72,"openalex_id":74,"authors":75,"venue":10,"cited_by_count":78,"oa_url":6,"card":79,"direction":83,"ingested_from":85},"W7214043002",[76],{"name":77,"orcid":9},"Prof. Nagendra Patel Sahil Verma",0,{"tldr":80,"method":81,"finding":82,"direction":83,"opportunity":84},"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。","农业人工智能与决策模型","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。","openalex","2026-09-23T23:30:35.342497Z",{"total":88,"page":21,"page_size":88,"items":89},6,[90,167,230,296,347,402],{"id":91,"title":92,"url":93,"summary":94,"summary_zh":95,"content":9,"source_name":96,"source_url":93,"published_at":97,"category":12,"cover_url":9,"hotness":48,"is_selected":14,"score":98,"score_detail":99,"sources":103,"tags":105,"primary_topic":106,"secondary_topics":107,"classification":117,"search_phrases":147,"slug":150,"view_count":71,"doi":151,"paper":152,"created_at":166},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":17,"substance":100,"depth":101,"authority":88,"freshness":20,"relevant":21,"comment":102},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。",[104],{"name":96,"url":93},[28,29,30,31,32],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[108,112],{"code":60,"slug":65,"name":109,"path":110,"parent_code":111},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":113,"slug":114,"name":115,"path":116,"parent_code":38},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":41,"locked":14,"version":42,"method":43,"evidence":118},{"candidates":119,"agricultural_evidence":49},[120,130,135,140],{"code":34,"score":121,"supported":49,"title":122,"abstract":126,"tags":129},34,[123,124,125],"machine learning","deep learning","artificial intelligence",[127,128,123,124,125],"深度学习","机器学习",[29],{"code":60,"score":131,"supported":49,"title":132,"abstract":133,"tags":134},15,[65],[64,65],[32],{"code":47,"score":48,"supported":49,"title":136,"abstract":138,"tags":139},[137],"smart agriculture",[],[28,30],{"code":113,"score":20,"supported":49,"title":141,"abstract":142,"tags":146},[],[143,144,145],"物联网","internet of things","IoT",[],[148,149],"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":151,"openalex_id":153,"authors":154,"venue":96,"cited_by_count":78,"oa_url":159,"card":160,"direction":165,"ingested_from":85},"W7212594050",[155,157],{"name":156,"orcid":9},"Snehal Sanjay Raut",{"name":158,"orcid":9},"Yogesh V. Chimate","https:\u002F\u002Fwww.irejournals.com\u002Fformatedpaper\u002F1722922.pdf",{"tldr":161,"method":162,"finding":163,"direction":83,"opportunity":164},"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":168,"title":169,"url":170,"summary":171,"summary_zh":172,"content":9,"source_name":10,"source_url":170,"published_at":173,"category":12,"cover_url":9,"hotness":48,"is_selected":14,"score":174,"score_detail":175,"sources":180,"tags":182,"primary_topic":184,"secondary_topics":185,"classification":187,"search_phrases":209,"slug":212,"view_count":213,"doi":214,"paper":215,"created_at":229},4374,"AI-Driven Crop Recommendation and Growth Prediction System Using Soil Fertility and Climate Intelligence","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23179023","Agricultural decisions are increasingly affected by changing climate conditions, soil fertility, and the availability of timely technical guidance. This mini project presents an AI-Driven Agricultural Intelligence System that combines crop recommendation, soil fertility assessment, weather-risk prediction, yield estimation, fertilizer recommendation, and multilingual conversational support in one web platform. The system accepts soil and climate parameters such as nitrogen, phosphorus, potassium, pH, temperature, humidity, rainfall, location, crop, and season. The supplied implementation uses a Random Forest model for crop recommendation, XGBoost for weather-risk prediction, a Random Forest regressor for yield estimation, a Random Forest classifier for fertilizer recommendation, and a deterministic rule-based soilfertility module with model-independent fallback logic. A FastAPI backend performs validation, model loading, chained inference, weather integration, authentication, and response formatting, while a React-based Progressive Web Application provides the user interface. OpenWeatherMap supplies current weather context and Google Gemini AI supports multilingual agricultural explanations with offline FAQ and rulebased fallbacks. Reported module performance is approximately 88-95%, and representative API, fallback, authentication, translation, rate-limiting, and chatbot tests pass in the supplied project material. The project demonstrates how machine learning, web development, external APIs, and multilingual AI can be combined to deliver practical, accessible, and data-driven agricultural decision support. Keywords: Machine Learning, Agriculture, Crop Recommendation, Soil Fertility, Weather Risk, Yield Prediction, Fertilizer Recommendation, FastAPI, React, XGBoost, Google Gemini AI, Precision Agriculture.","农业决策日益受到气候条件变化、土壤肥力以及及时技术指导可得性的影响。本小型项目提出了一套人工智能驱动的农业智能系统，将作物推荐、土壤肥力评估、天气风险预测、产量估算、肥料推荐和多语言对话支持集成于一个网络平台。该系统接受土壤和气候参数，如氮、磷、钾、pH值、温度、湿度、降雨量、地点、作物和季节。所提供的实现采用随机森林（Random Forest）模型进行作物推荐，采用XGBoost进行天气风险预测，采用随机森林回归器进行产量估算，采用随机森林分类器进行肥料推荐，并采用基于确定性规则的土壤肥力模块，具备与模型无关的回退逻辑。FastAPI后端执行验证、模型加载、链式推理、天气集成、身份认证和响应格式化，而基于React的渐进式网络应用提供用户界面。OpenWeatherMap提供当前天气上下文，Google Gemini AI支持多语言农业解释，并具备离线FAQ和基于规则的回退机制。所报告的模块性能约为88-95%，且在所提供的项目材料中，具有代表性的API、回退、身份认证、翻译、速率限制和聊天机器人测试均通过。该项目展示了如何将机器学习、网络开发、外部API和多语言AI相结合，以提供实用、可及且数据驱动的农业决策支持。关键词：机器学习、农业、作物推荐、土壤肥力、天气风险、产量预测、肥料推荐、FastAPI、React、XGBoost、Google Gemini AI、精准农业。","2026-10-06T00:00:00Z",50,{"impact":176,"substance":177,"depth":178,"authority":88,"freshness":20,"relevant":21,"comment":179},8,14,13,"学生级迷你项目论文，方法组合常规、性能数据自报，工程整合有一定参考价值但学术与产业增量有限，可作智慧农业应用案例收录。",[181],{"name":10,"url":170},[28,29,30,31,183],"土壤肥力",{"code":60,"slug":65,"name":109,"path":110,"parent_code":111},[186],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"status":41,"locked":14,"version":42,"method":43,"evidence":188},{"candidates":189,"agricultural_evidence":49},[190,194,198,202],{"code":60,"score":131,"supported":49,"title":191,"abstract":192,"tags":193},[65],[64,65],[183],{"code":34,"score":48,"supported":49,"title":195,"abstract":196,"tags":197},[],[128,123,56],[29],{"code":47,"score":176,"supported":49,"title":199,"abstract":200,"tags":201},[],[30,51],[28,30],{"code":203,"score":88,"supported":49,"title":204,"abstract":205,"tags":208},"F3",[],[206,207],"肥料","fertilizer",[],[210,211],"AI 作物推荐 土壤肥力","XGBoost 天气风险 产量预测","AI作物推荐土壤肥力-4374",3,"10.5281\u002Fzenodo.23179023",{"doi":214,"openalex_id":216,"authors":217,"venue":10,"cited_by_count":78,"oa_url":170,"card":224,"direction":83,"ingested_from":85},"W7220664944",[218,220,222],{"name":219,"orcid":9},"Gunashekar B.",{"name":221,"orcid":9},"Saiteja G.",{"name":223,"orcid":9},"Maniteja K.",{"tldr":225,"method":226,"finding":227,"direction":83,"opportunity":228},"构建集成作物推荐、产量预测、肥料建议与多语言问答的AI农业决策Web平台。","随机森林、XGBoost、FastAPI、React PWA、OpenWeath","各模块准确率约88-95%，API、认证、翻译与聊天机器人测试均通过。","可引入真实田间时序数据与可解释性，验证模型跨区域跨季节泛化能力。","2026-10-07T23:30:47.248380Z",{"id":231,"title":232,"url":233,"summary":234,"summary_zh":235,"content":9,"source_name":236,"source_url":233,"published_at":237,"category":12,"cover_url":9,"hotness":48,"is_selected":14,"score":238,"score_detail":239,"sources":242,"tags":244,"primary_topic":246,"secondary_topics":247,"classification":249,"search_phrases":278,"slug":281,"view_count":21,"doi":282,"paper":283,"created_at":295},3877,"Machine learning classification techniques for smart agriculture: a review of disease detection and crop recommendation","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrai.2026.1878006","The adoption of Internet of Things (IoT) and Machine Learning (ML) in agriculture presents a revolutionary step toward efficient agricultural practices using data to address global issues surrounding food security. This review conducted a systematic search of the Scopus, IEEE Xplore, ScienceDirect, and SpringerLink databases for articles published between January 2023 and January 2026. It used keyword combinations like “IoT and machine learning and agriculture”, “crop recommendation”, “plant disease detection”, and “precision farming”. From the initial pool of articles found, 30 articles met the criteria for empirical methodology, quantitative performance reporting, and clear dataset descriptions. These were chosen for detailed analysis. It was discovered that the ensemble, as well as the traditional machine learning methods, such as Random Forest, Support Vector Machine (SVM), and Decision Trees, produce highly accurate predictions of the crop yields with an accuracy of 99.9%. Meanwhile, Convolutional Neural Networks (CNN) and ResNet-based deep learning models detect diseases with an accuracy above 98% while Gated Recurrent Units (GRU) and Long-Short Term Memory (LSTM) time-series models provide forecasts of weather conditions with an accuracy ranging from 94 to 98%, due to the complex nature of the environment dynamics. The application of IoT systems for precision agriculture produced a significant improvement in terms of resource utilization, resulting in 60–96% of water savings, 25% increased yield, and 36–38% reduced herbicide use. Although these advancements have been made, wider application is still hindered by high costs of implementation, lack of technological skills among farmers, data privacy concerns, issues of scalability within different agricultural settings, and consistent internet connection in rural areas. The current paper evaluates the use of IoT and ML in the agriculture sector from diverse data sources and studies to reveal the immense benefits offered by the two systems in enhancing farming activities.","物联网（IoT）与机器学习（ML）在农业中的应用标志着向高效农业实践迈出了革命性的一步，即利用数据来解决全球粮食安全相关问题。本综述对Scopus、IEEE Xplore、ScienceDirect和SpringerLink数据库中2023年1月至2026年1月间发表的文章进行了系统性检索，使用了“物联网与机器学习与农业”“作物推荐”“植物病害检测”和“精准农业”等关键词组合。从初步检索到的文章池中，30篇文章符合实证方法、定量性能报告和清晰数据集描述的标准，被选入进行详细分析。研究发现，集成方法以及随机森林、支持向量机（SVM）和决策树等传统机器学习方法对作物产量的预测精度高达99.9%。同时，卷积神经网络（CNN）和基于ResNet的深度学习模型检测病害的准确率超过98%，而门控循环单元（GRU）和长短期记忆（LSTM）时间序列模型由于环境动态的复杂性，对天气状况的预测准确率在94%至98%之间。物联网系统在精准农业中的应用在资源利用方面产生了显著改善，实现了60%至96%的节水、25%的产量提升以及36%至38%的除草剂用量减少。尽管取得了这些进展，更广泛的应用仍受到实施成本高昂、农民缺乏技术技能、数据隐私顾虑、不同农业场景下的可扩展性问题以及农村地区互联网连接不稳定等因素的制约。本文通过来自不同数据来源和研究，评估了物联网与机器学习在农业领域的应用，揭示了这两个系统在提升农业活动方面所带来的巨大效益。","Frontiers in Artificial Intelligence","2026-09-30T00:00:00Z",76,{"impact":100,"substance":240,"depth":19,"authority":178,"freshness":176,"relevant":21,"comment":241},20,"系统综述整合30篇实证研究，量化了IoT与机器学习在作物推荐、病害检测和精准灌溉上的性能与节水增产效益，对智慧农业技术选型有参考价值。",[243],{"name":236,"url":233},[28,29,245,30,31],"病虫害识别",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[248],{"code":113,"slug":114,"name":115,"path":116,"parent_code":38},{"status":41,"locked":14,"version":42,"method":43,"evidence":250},{"candidates":251,"agricultural_evidence":49},[252,257,261,265,272],{"code":34,"score":253,"supported":49,"title":254,"abstract":255,"tags":256},19,[123],[127,128,123,124],[29,245],{"code":47,"score":101,"supported":49,"title":258,"abstract":259,"tags":260},[137],[30,51],[28,30],{"code":113,"score":20,"supported":49,"title":262,"abstract":263,"tags":264},[],[143,144,145],[],{"code":266,"score":88,"supported":49,"title":267,"abstract":268,"tags":271},"A8",[],[269,270],"粮食安全","food security",[],{"code":273,"score":213,"supported":49,"title":274,"abstract":275,"tags":277},"F4",[],[276],"除草",[],[279,280],"农业人工智能 病虫害识别 作物推荐 智慧农业","农业人工智能 病虫害识别","农业人工智能病虫害识别作物推荐智慧农业-3877","10.3389\u002Ffrai.2026.1878006",{"doi":282,"openalex_id":284,"authors":285,"venue":236,"cited_by_count":78,"oa_url":233,"card":290,"direction":165,"ingested_from":85},"W7214889993",[286,288],{"name":287,"orcid":9},"M. Umamaheswari",{"name":289,"orcid":9},"Kavitha D",{"tldr":291,"method":292,"finding":293,"direction":83,"opportunity":294},"综述2023-2026年30篇文献，评估物联网与机器学习在作物病害检测和推荐中的应用。","系统检索Scopus等四个数据库，筛选30篇实证研究进行性能与数据集分析。","集成学习与SVM等预测产量准确率达99.9%，CNN病害检测超98%，IoT节水60-96%。","可研究低成本、低连接依赖的边缘ML模型，解决小农户部署与数据隐私问题。","2026-10-01T23:30:09.158196Z",{"id":297,"title":298,"url":299,"summary":300,"summary_zh":301,"content":9,"source_name":10,"source_url":299,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":302,"score_detail":303,"sources":306,"tags":310,"primary_topic":311,"secondary_topics":312,"classification":313,"search_phrases":333,"slug":335,"view_count":71,"doi":336,"paper":337,"created_at":346},3271,"A Systematic Study of Supervised and Ensemble Learning Approaches for Crop Selection in Smart Agriculture","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22896343","Selecting the crop best matched to a field’s soil and climate is one of the highest-leverage decisions in agriculture, and one that farmers have traditionally made by intuition or inherited practice. Soil pH together with nitrogen, phosphorus and potassium concentration, and the local temperature, humidity and rainfall regime, jointly determine which crop will flourish and which will fail, and the relationship between those variables and crop performance is neither linear nor independent. Machine learning has therefore become the dominant approach to automated crop recommendation. This paper reviews the field across four technique families — classical supervised learning, ensemble and boosting methods, deep learning and metaheuristic hybrids, and IoT and deployment-oriented systems — and compares twenty-four representative studies published between 2016 and 2026 in terms of method, data source, reported accuracy, advantage and limitation. A generic seven-stage recommendation pipeline is presented and each family is situated within it. The comparison shows that reported accuracy on the standard nutrient-and-climate benchmark has converged in a narrow band between roughly 98 and 99.5 per cent, that boosting and ensemble methods occupy the upper part of that band, and that further gains on the benchmark are no longer the binding constraint on the field. The gaps that remain open are instead the absence of socio-economic and market variables from the decision, the lack of region-specific and long-horizon environmental validation, dataset narrowness and geographic bias, limited interpretability, and the accessibility of these systems to small and resource-poor farmers. These are consolidated into a set of research directions for future work.","选择与田块土壤和气候最匹配的作物是农业中杠杆效应最高的决策之一，而农民传统上依靠直觉或世代相传的经验来做出这一决策。土壤pH值以及氮、磷、钾浓度，加上当地的气温、湿度和降雨状况，共同决定了哪种作物能够茁壮成长、哪种会歉收，而这些变量与作物表现之间的关系既非线性也非相互独立。因此，机器学习已成为自动化作物推荐的主流方法。本文从四个技术族系——经典监督学习、集成与提升方法、深度学习与元启发式混合方法，以及物联网与面向部署的系统——对该领域进行了综述，并从方法、数据来源、报告精度、优势和局限性方面比较了2016年至2026年间发表的二十四项代表性研究。本文提出了一个通用的七阶段推荐流程，并将每个技术族系置于该流程中加以定位。比较结果表明，在标准养分与气候基准上的报告精度已收敛于约98%至99.5%的狭窄区间内，提升与集成方法占据该区间的上端，而在该基准上进一步提升已不再是该领域的约束瓶颈。真正尚未填补的空白在于：决策中缺乏社会经济和市场变量，缺少针对特定区域和长期环境验证，数据集狭窄且存在地理偏差，可解释性有限，以及这些系统对小型和资源匮乏农户的可及性不足。这些空白被归纳为未来工作的一系列研究方向。",78,{"impact":101,"substance":304,"depth":100,"authority":178,"freshness":20,"relevant":21,"comment":305},22,"系统综述24项研究并指出基准精度已趋饱和，真正瓶颈转向社会经济变量与可解释性，对智慧农业选种方向有参考价值。",[307,308],{"name":10,"url":299},{"name":10,"url":309},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22896344",[28,29,128,30,31],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":314},{"candidates":315,"agricultural_evidence":49},[316,321,325,329],{"code":34,"score":317,"supported":49,"title":318,"abstract":319,"tags":320},11,[],[127,128,123,124],[29,128],{"code":47,"score":48,"supported":49,"title":322,"abstract":323,"tags":324},[137],[],[28,30],{"code":60,"score":88,"supported":49,"title":326,"abstract":327,"tags":328},[],[64,65],[],{"code":113,"score":88,"supported":49,"title":330,"abstract":331,"tags":332},[],[143,145],[],[334,149],"农业人工智能 作物推荐 智慧农业 机器学习","农业人工智能作物推荐智慧农业机器学习-3271","10.5281\u002Fzenodo.22896343",{"doi":336,"openalex_id":338,"authors":339,"venue":10,"cited_by_count":78,"oa_url":299,"card":341,"direction":165,"ingested_from":85},"W7214002748",[340],{"name":77,"orcid":9},{"tldr":342,"method":343,"finding":344,"direction":83,"opportunity":345},"系统综述2016-2026年24项作物推荐研究，比较四类机器学习方法并指出基准精度已趋饱和。","综述监督学习、集成提升、深度学习与元启发式、物联网部署四类方法及七阶段流程。","标准基准精度收敛于98%-99.5%，提升集成法最优，但精度已非领域瓶颈。","将社会经济与市场变量、区域长期环境验证及可解释性纳入作物推荐，服务小农户。","2026-09-23T23:30:09.369987Z",{"id":348,"title":349,"url":350,"summary":351,"summary_zh":352,"content":9,"source_name":353,"source_url":350,"published_at":354,"category":12,"cover_url":9,"hotness":48,"is_selected":14,"score":355,"score_detail":356,"sources":358,"tags":360,"primary_topic":361,"secondary_topics":362,"classification":363,"search_phrases":384,"slug":387,"view_count":21,"doi":388,"paper":389,"created_at":401},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":17,"substance":100,"depth":131,"authority":177,"freshness":176,"relevant":21,"comment":357},"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。",[359],{"name":353,"url":350},[28,29,128,30,32],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":364},{"candidates":365,"agricultural_evidence":49},[366,370,374,378],{"code":34,"score":101,"supported":49,"title":367,"abstract":368,"tags":369},[123],[128,123],[29,128],{"code":47,"score":176,"supported":49,"title":371,"abstract":372,"tags":373},[],[30,51],[28,30],{"code":60,"score":61,"supported":49,"title":375,"abstract":376,"tags":377},[],[64,65],[32],{"code":379,"score":213,"supported":49,"title":380,"abstract":381,"tags":383},"I6",[],[382],"agricultural data",[],[385,386],"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":388,"openalex_id":390,"authors":391,"venue":353,"cited_by_count":78,"oa_url":350,"card":396,"direction":83,"ingested_from":85},"W7212396250",[392,394],{"name":393,"orcid":9},"T. Suba",{"name":395,"orcid":9},"K. Lakshmi Joshitha",{"tldr":397,"method":398,"finding":399,"direction":83,"opportunity":400},"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":403,"title":404,"url":405,"summary":406,"summary_zh":407,"content":9,"source_name":408,"source_url":405,"published_at":409,"category":12,"cover_url":9,"hotness":48,"is_selected":14,"score":238,"score_detail":410,"sources":412,"tags":414,"primary_topic":415,"secondary_topics":416,"classification":418,"search_phrases":461,"slug":463,"view_count":213,"doi":464,"paper":465,"created_at":490},2302,"AgriSphere: A Smart Agriculture Framework Integrating IoT and Artificial Intelligence for Adaptive Crop Selection","https:\u002F\u002Fdoi.org\u002F10.2174\u002F0118743315503187260909160504","Introduction Rapid climate change, soil degradation, and changing environmental conditions make crop selection difficult for farmers. This study proposes an IoT- and AI-based framework to recommend suitable crops using current soil conditions and future weather forecasts. It also identifies the key environmental factors influencing crop selection. Methods A three-layer architecture was designed with data collection, communication, and data processing modules. Real-time data on nitrogen, phosphorus, potassium, pH, temperature, and humidity were collected through IoT sensors and combined with rainfall and historical agricultural data. A dataset containing multiple environmental features and 22 crop classes was used for model development. Machine learning and deep learning methods, including Random Forest, XGBoost, K-Nearest Neighbours (KNN), Support Vector Machines (SVM), Convolutional Neural Networks (CNN), Decision Trees (DT), Deep Neural Networks (DNN), and Long Short-Term Memory (LSTM), were applied for classification and forecasting. Performance was evaluated using accuracy, F1-Score, MAE, RMSE, and R 2 . Pareto analysis was also performed to identify the most influential parameters. Results Random Forest and CNN achieved the highest classification accuracy of 99.54% with an F1-Score of 0.995, while XGBoost also performed strongly with 99.32% accuracy. Regression analysis showed that ensemble models outperformed linear models. Pareto analysis revealed that rainfall, humidity, and potassium were the most influential factors in crop recommendation. In a real-time case study, the framework recommended rice as the most suitable crop for the given input conditions. Discussion The results show that integrating IoT sensing with AI-based forecasting supports proactive crop planning before sowing and improves sustainable farming decisions under changing climate conditions. Conclusion The proposed framework effectively combines real-time monitoring, predictive analytics, and intelligent crop recommendation, offering a practical foundation for scalable precision agriculture systems.","引言 快速的气候变化、土壤退化以及不断变化的环境条件使农民难以进行作物选择。本研究提出了一种基于物联网（IoT）和人工智能（AI）的框架，利用当前土壤条件和未来天气预报来推荐适宜的作物。研究还识别了影响作物选择的关键环境因素。方法 设计了一个三层架构，包括数据采集、通信和数据处理模块。通过物联网传感器采集氮、磷、钾、pH值、温度和湿度的实时数据，并结合降雨量和历史农业数据。使用包含多个环境特征和22种作物类别的数据集进行模型开发。应用机器学习和深度学习方法进行分类和预测，包括随机森林（Random Forest）、XGBoost、K近邻（KNN）、支持向量机（SVM）、卷积神经网络（CNN）、决策树（DT）、深度神经网络（DNN）和长短期记忆网络（LSTM）。采用准确率、F1分数、MAE、RMSE和R²评估性能。同时进行帕累托分析以识别最具影响力的参数。结果 随机森林和CNN取得了最高的分类准确率99.54%，F1分数为0.995，XGBoost也表现强劲，准确率为99.32%。回归分析表明，集成模型优于线性模型。帕累托分析显示，降雨量、湿度和钾是作物推荐中影响最大的因素。在实时案例研究中，该框架推荐水稻为给定输入条件下最适宜的作物。讨论 结果表明，将物联网感知与基于人工智能的预测相结合，有助于在播种前进行主动的作物规划，并在气候变化条件下改善可持续农业决策。结论 所提出的框架有效结合了实时监测、预测分析和智能作物推荐，为可扩展的精准农业系统提供了实用基础。","The Open Agriculture Journal","2026-09-11T00:00:00Z",{"impact":100,"substance":18,"depth":19,"authority":17,"freshness":176,"relevant":21,"comment":411},"该论文提出IoT与AI融合的智能选种框架，多模型对比与实时案例验证充分，方法新颖且结论可靠，对智慧农业精准种植有较高参考价值。",[413],{"name":408,"url":405},[28,29,143,30,31],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[417],{"code":113,"slug":114,"name":115,"path":116,"parent_code":38},{"status":41,"locked":14,"version":42,"method":43,"evidence":419},{"candidates":420,"agricultural_evidence":49},[421,425,429,433,440,444,451,457],{"code":34,"score":100,"supported":49,"title":422,"abstract":423,"tags":424},[125],[127,128,123,124],[29],{"code":47,"score":101,"supported":49,"title":426,"abstract":427,"tags":428},[137],[30,51],[28,30],{"code":113,"score":131,"supported":49,"title":430,"abstract":431,"tags":432},[145],[143,145],[143],{"code":434,"score":88,"supported":49,"title":435,"abstract":436,"tags":439},"A1",[],[437,438],"水稻","rice",[],{"code":60,"score":88,"supported":49,"title":441,"abstract":442,"tags":443},[],[64,65],[],{"code":445,"score":88,"supported":49,"title":446,"abstract":447,"tags":450},"H7",[],[448,449],"气候变化","climate change",[],{"code":452,"score":213,"supported":49,"title":453,"abstract":454,"tags":456},"H8",[],[455],"可持续农业",[],{"code":379,"score":213,"supported":49,"title":458,"abstract":459,"tags":460},[],[382],[],[462,149],"农业人工智能 作物推荐 智慧农业 精准农业","农业人工智能作物推荐智慧农业精准农业-2302","10.2174\u002F0118743315503187260909160504",{"doi":464,"openalex_id":466,"authors":467,"venue":408,"cited_by_count":78,"oa_url":405,"card":485,"direction":165,"ingested_from":85},"W7212307638",[468,470,473,476,479,482],{"name":469,"orcid":9},"Shreya Sriram",{"name":471,"orcid":472},"Prajeesh C B","https:\u002F\u002Forcid.org\u002F0000-0002-8404-8583",{"name":474,"orcid":475},"Delphin Raj Kesari Mary","https:\u002F\u002Forcid.org\u002F0000-0002-8989-7090",{"name":477,"orcid":478},"Anju S. Pillai","https:\u002F\u002Forcid.org\u002F0000-0001-5298-6789",{"name":480,"orcid":481},"R. V.","https:\u002F\u002Forcid.org\u002F0000-0002-0699-2990",{"name":483,"orcid":484},"V. M. Manikandan","https:\u002F\u002Forcid.org\u002F0000-0001-6903-7563",{"tldr":486,"method":487,"finding":488,"direction":165,"opportunity":489},"提出IoT与AI融合框架，结合实时土壤数据与天气预报推荐适宜作物。","三层IoT架构采集NPK、pH等数据，用RF、CNN等8种模型分类22种作物。","RF与CNN分类准确率达99.54%，降雨、湿度和钾是作物选择最关键因素。","可探索多源遥感与边缘计算融合，实现小农户低成本、可解释的实时作物推荐系统。","2026-09-13T23:30:09.745236Z"]