[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1885":3,"related-1885":113},{"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":86,"slug":89,"view_count":21,"doi":90,"paper":91,"created_at":112},1885,"Ai Based Smart Crop Recommendation and Yield Prediction Using Ml and Weather Analytics","https:\u002F\u002Fdoi.org\u002F10.47392\u002Firjaeh.2026.0696","Agriculture plays a vital role in ensuring global food security; however, unpredictable climatic conditions, changing soil characteristics, and inefficient crop selection continue to affect agricultural productivity. Accurate crop recommendation and yield prediction are essential for supporting farmers in making informed cultivation decisions and maximizing crop production. This paper proposes an AI-Based Smart Crop Recommendation and Yield Prediction Framework that integrates machine learning techniques with weather analytics to provide intelligent decision support for precision agriculture. The proposed framework utilizes multiple environmental and agricultural parameters, including soil nutrients (Nitrogen, Phosphorus, and Potassium), soil pH, temperature, humidity, rainfall, and historical weather information, to recommend the most suitable crop and estimate its expected yield. A comprehensive data preprocessing pipeline involving missing value treatment, feature engineering, normalization, and feature selection is employed to improve model performance. Multiple machine learning algorithms, including Random Forest, XGBoost, LightGBM, and Support Vector Machine, are evaluated, and an ensemble learning approach is adopted to enhance prediction accuracy and model robustness. Weather analytics are incorporated to capture seasonal variations and climatic influences that significantly impact crop productivity. The proposed framework is validated using publicly available agricultural datasets and evaluated through cross-validation using standard performance metrics such as accuracy, precision, recall, F1-score, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and coefficient of determination (R²). The experimental results are expected to demonstrate that integrating weather analytics with ensemble machine learning significantly improves both crop recommendation accuracy and yield prediction performance compared with conventional single-model approaches. The proposed framework provides an intelligent, scalable, and data-driven decision support system that can assist farmers, agricultural experts, and policymakers in improving productivity, optimizing resource utilization, and promoting sustainable farming practices under varying climatic conditions.","农业在保障全球粮食安全方面发挥着至关重要的作用；然而，不可预测的气候条件、不断变化的土壤特性以及低效的作物选择持续影响着农业生产率。精准的作物推荐与产量预测对于帮助农民做出明智的种植决策并最大化作物产量至关重要。本文提出了一种基于人工智能的智能作物推荐与产量预测框架，该框架将机器学习技术与天气分析相结合，为精准农业提供智能决策支持。所提出的框架利用多种环境和农业参数，包括土壤养分（氮、磷和钾）、土壤pH值、温度、湿度、降雨量以及历史天气信息，来推荐最适宜的作物并估算其预期产量。采用包含缺失值处理、特征工程、归一化和特征选择的综合数据预处理流程，以提升模型性能。评估了多种机器学习算法，包括随机森林、XGBoost、LightGBM和支持向量机，并采用集成学习方法以增强预测精度和模型鲁棒性。引入天气分析以捕捉对作物生产力有显著影响的季节性变化和气候因素。所提出的框架使用公开可用的农业数据集进行验证，并通过交叉验证使用标准性能指标进行评估，如准确率、精确率、召回率、F1分数、平均绝对误差（MAE）、均方根误差（RMSE）和决定系数（R²）。预期实验结果表明，与传统的单一模型方法相比，将天气分析与集成机器学习相结合能显著提高作物推荐准确率和产量预测性能。所提出的框架提供了一个智能、可扩展且数据驱动的决策支持系统，能够帮助农民、农业专家和政策制定者在不同气候条件下提高生产力、优化资源利用并促进可持续农业实践。",null,"International Research Journal on Advanced Engineering Hub (IRJAEH)","2026-09-07T00:00:00Z","论文",10,false,40,{"impact":17,"substance":18,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},8,12,6,2,1,"论文提出结合天气分析的集成学习框架，但缺乏实证结果，影响有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","产量预测","机器学习","作物推荐",{"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":46},[44,52,60,67,74,81],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":51},11,true,[],[29,49,50],"machine learning","decision support",[27,29],{"code":53,"score":54,"supported":46,"title":55,"abstract":56,"tags":59},"I1",7,[],[57,58],"精准农业","precision agriculture",[26],{"code":61,"score":19,"supported":46,"title":62,"abstract":63,"tags":66},"A8",[],[64,65],"粮食安全","food security",[],{"code":68,"score":19,"supported":46,"title":69,"abstract":70,"tags":73},"H2",[],[71,72],"土壤","soil",[],{"code":75,"score":76,"supported":46,"title":77,"abstract":78,"tags":80},"H8",3,[],[79],"可持续农业",[],{"code":82,"score":21,"supported":14,"title":83,"abstract":84,"tags":85},"I3",[],[],[28],[87,88],"农业人工智能 产量预测 作物推荐 智慧农业","农业人工智能 产量预测","农业人工智能产量预测作物推荐智慧农业-1885","10.47392\u002Firjaeh.2026.0696",{"doi":90,"openalex_id":92,"authors":93,"venue":10,"cited_by_count":103,"oa_url":6,"card":104,"direction":110,"ingested_from":111},"W7211877620",[94,96,98,100,101],{"name":95,"orcid":9},"Choudhuri Saswat Pattnaik",{"name":97,"orcid":9},"Rojalini Mohanty",{"name":99,"orcid":9},"Bijaya Laxmi Hazra",{"name":99,"orcid":9},{"name":102,"orcid":9},"Akash Kumar Jena",0,{"tldr":105,"method":106,"finding":107,"direction":108,"opportunity":109},"提出AI框架，结合机器学习与天气分析，推荐作物并预测产量。","集成学习（RF、XGBoost等）与天气数据，含预处理和特征选择。","集成天气分析的集成模型比单一模型更准确。","农业人工智能与决策模型","可探索多源数据融合、可解释性模型及区域适应性，提升实际部署效果。","智慧农业 \u002F 农业物联网","openalex","2026-09-08T23:30:07.974730Z",{"total":19,"page":21,"page_size":19,"items":114},[115,180,262,331,392,456],{"id":116,"title":117,"url":118,"summary":119,"summary_zh":120,"content":9,"source_name":121,"source_url":118,"published_at":122,"category":12,"cover_url":9,"hotness":123,"is_selected":14,"score":124,"score_detail":125,"sources":132,"tags":136,"primary_topic":137,"secondary_topics":138,"classification":139,"search_phrases":164,"slug":167,"view_count":20,"doi":168,"paper":169,"created_at":179},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。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-22T00:00:00Z",25,78,{"impact":126,"substance":127,"depth":128,"authority":129,"freshness":130,"relevant":21,"comment":131},16,22,18,13,9,"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。",[133,134],{"name":121,"url":118},{"name":121,"url":135},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22896344",[26,27,29,57,30],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":140},{"candidates":141,"agricultural_evidence":46},[142,148,153,157],{"code":32,"score":45,"supported":46,"title":143,"abstract":144,"tags":147},[],[145,29,49,146],"深度学习","deep learning",[27,29],{"code":53,"score":13,"supported":46,"title":149,"abstract":151,"tags":152},[150],"smart agriculture",[],[26,57],{"code":68,"score":19,"supported":46,"title":154,"abstract":155,"tags":156},[],[71,72],[],{"code":158,"score":19,"supported":46,"title":159,"abstract":160,"tags":163},"I5",[],[161,162],"物联网","IoT",[],[165,166],"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","农业人工智能作物推荐智慧农业机器学习-3271","10.5281\u002Fzenodo.22896343",{"doi":168,"openalex_id":170,"authors":171,"venue":121,"cited_by_count":103,"oa_url":118,"card":174,"direction":110,"ingested_from":111},"W7214002748",[172],{"name":173,"orcid":9},"Prof. Nagendra Patel Sahil Verma",{"tldr":175,"method":176,"finding":177,"direction":108,"opportunity":178},"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":181,"title":182,"url":183,"summary":184,"summary_zh":185,"content":9,"source_name":186,"source_url":183,"published_at":187,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":188,"score_detail":189,"sources":193,"tags":195,"primary_topic":197,"secondary_topics":198,"classification":199,"search_phrases":219,"slug":222,"view_count":223,"doi":224,"paper":225,"created_at":261},3258,"Machine learning models combined with feature importance methods for honey yield classification: A replicable approach","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102575","Beekeepers require planning tools supported by modern technologies, such as machine learning and the Internet of Things, to address agricultural challenges such as the decrease and irregularity in honey production. To ensure replicability, this article presents a research workflow that begins with the creation of an open-access database, developed from annual production records and climatic variables (temperature and rainfall), integrating data construction, explainability analysis, and model evaluation. Then, feature importance methods and explainability techniques are applied, such as feature importance, the depth-wise frequency of each feature in random forest, and the Shapley Additive Explanations method. Finally, machine learning approaches are evaluated for honey yield prediction: logistic regression, k-nearest neighbors, support vector machine, decision tree, multilayer perceptron, random forest, linear discriminant analysis, gradient boosting, and Naive Bayes. These algorithms are compared considering: (1) a baseline corresponding to models without hyperparameter optimization, using leave-one-out cross-validation and stratified 10-fold cross-validation; (2) the baseline plus normalization\u002Fstandardization (div-max, min-max, and z-score); (3) the configuration in point 2 plus bagging; (4) evaluation of a data-augmentation and class-balancing strategy using SMOTE, together with model combination via the Voting Classifier. The results suggest that rainfall is one of the most important variables for honey yield prediction. By selecting certain features, the models improve in some cases or do not significantly degrade their performance. The min-max and z-score methods led to improved predictions in some algorithms; for example, support vector machine achieved an accuracy of 0.80, compared with the 0.67 accuracy reported in the reference study based on random forest, representing an increase of 13 percentage points. Finally, bagging techniques, SMOTE oversampling, and the Voting Classifier, using algorithms such as KNN and SVM, can achieve an ACC of 0.82. Overall, this study proposes a replicable data mining-based workflow that integrates machine learning techniques for predicting honey yield from climatic variables, including the use of an open-access dataset, explainability analysis, and a comparative evaluation of machine learning models, contributing to the development of future analysis and planning tools in the beekeeping sector.","养蜂人需要借助机器学习和物联网等现代技术支持的规划工具，以应对蜂蜜产量下降和波动等农业挑战。为确保可复现性，本文提出了一套研究流程：首先构建一个开放获取数据库，该数据库基于年度生产记录和气候变量（温度和降雨量）开发，并整合了数据构建、可解释性分析和模型评估。随后，应用特征重要性方法和可解释性技术，如特征重要性、随机森林中各特征的深度频率以及Shapley加性解释方法。最后，评估多种机器学习方法用于蜂蜜产量预测：逻辑回归、k近邻、支持向量机、决策树、多层感知机、随机森林、线性判别分析、梯度提升和朴素贝叶斯。这些算法在以下方面进行比较：（1）基线模型，即未进行超参数优化的模型，采用留一交叉验证和分层10折交叉验证；（2）基线加归一化\u002F标准化（最大值除法、最小-最大和z-score）；（3）第2点配置加装袋法；（4）使用SMOTE评估数据增强和类别平衡策略，并结合投票分类器进行模型组合。结果表明，降雨量是蜂蜜产量预测中最重要的变量之一。通过选择特定特征，模型在某些情况下性能得到提升，或性能未显著下降。最小-最大和z-score方法使部分算法的预测效果得到改善；例如，支持向量机达到了0.80的准确率，而参考研究中基于随机森林的准确率为0.67，提升了13个百分点。最后，使用KNN和SVM等算法结合装袋技术、SMOTE过采样和投票分类器，可以达到0.82的准确率。总体而言，本研究提出了一套可复现的、基于数据挖掘的工作流程，整合了机器学习技术以从气候变量预测蜂蜜产量，包括使用开放获取数据集、可解释性分析以及机器学习模型的比较评估，为养蜂领域未来分析和规划工具的开发做出了贡献。","Smart Agricultural Technology","2026-09-20T00:00:00Z",71,{"impact":18,"substance":190,"depth":191,"authority":129,"freshness":130,"relevant":21,"comment":192},20,17,"该论文提出可复现的机器学习工作流，结合开放数据集与可解释性方法预测蜂蜜产量，方法新颖、结论可靠，对养蜂业数字化规划有参考价值。",[194],{"name":186,"url":183},[26,27,28,29,196],"蜂产业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":200},{"candidates":201,"agricultural_evidence":46},[202,206,211,215],{"code":32,"score":126,"supported":46,"title":203,"abstract":204,"tags":205},[49],[29,49],[27,29],{"code":158,"score":19,"supported":46,"title":207,"abstract":208,"tags":210},[],[161,209],"internet of things",[],{"code":53,"score":21,"supported":14,"title":212,"abstract":213,"tags":214},[],[],[26],{"code":82,"score":21,"supported":14,"title":216,"abstract":217,"tags":218},[],[],[28],[220,221],"蜂蜜产量 机器学习 预测","Smart Agricultural Technology 蜂蜜","蜂蜜产量机器学习预测-3258",4,"10.1016\u002Fj.atech.2026.102575",{"doi":224,"openalex_id":226,"authors":227,"venue":186,"cited_by_count":103,"oa_url":183,"card":256,"direction":108,"ingested_from":111},"W7213773430",[228,231,234,237,239,242,245,248,250,253],{"name":229,"orcid":230},"Roberto Ahumada‐García","https:\u002F\u002Forcid.org\u002F0000-0003-1107-4606",{"name":232,"orcid":233},"David Zabala‐Blanco","https:\u002F\u002Forcid.org\u002F0000-0002-5692-5673",{"name":235,"orcid":236},"Víctor Hugo Monzón","https:\u002F\u002Forcid.org\u002F0000-0001-9729-7768",{"name":238,"orcid":9},"Iván Sánchez",{"name":240,"orcid":241},"Nádia Félix Felipe da Silva","https:\u002F\u002Forcid.org\u002F0000-0002-3875-2211",{"name":243,"orcid":244},"Thierson Couto Rosa","https:\u002F\u002Forcid.org\u002F0000-0001-7117-3994",{"name":246,"orcid":247},"Alef Iury Siqueira Ferreira","https:\u002F\u002Forcid.org\u002F0000-0002-9119-6357",{"name":249,"orcid":9},"Xaviera López-Cortés",{"name":251,"orcid":252},"Marco Javier Flores-Calero","https:\u002F\u002Forcid.org\u002F0000-0001-7507-3325",{"name":254,"orcid":255},"Philip Vásquez-Iglesias","https:\u002F\u002Forcid.org\u002F0009-0008-2109-8787",{"tldr":257,"method":258,"finding":259,"direction":108,"opportunity":260},"构建可复现工作流，用气候变量与机器学习分类蜂蜜产量。","开放数据库、特征重要性\u002FSHAP、9种ML模型、SMOTE与投票集成。","降雨是最重要变量；SVM准确率0.80，集成后达0.82。","可扩展至多源物联网数据与实时预测，开发养蜂决策支持工具。","2026-09-23T23:30:03.793669Z",{"id":263,"title":264,"url":265,"summary":266,"summary_zh":267,"content":9,"source_name":268,"source_url":265,"published_at":269,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":270,"score_detail":271,"sources":273,"tags":275,"primary_topic":277,"secondary_topics":278,"classification":279,"search_phrases":315,"slug":318,"view_count":19,"doi":319,"paper":320,"created_at":330},3197,"YieldVision: Model-Driven Decision Support System for Crop Yield Prediction","https:\u002F\u002Fdoi.org\u002F10.56201\u002Frjpst.vol.9.no1.2026.pg176.194","Agriculture, a cornerstone of global food security, faces unprecedented challenges in achieving sustainable productivity. Crop yield prediction, a crucial aspect of agricultural planning, suffers from inefficiencies rooted in technological and methodological gaps. While traditional approaches rely on historical data and empirical estimations, they often fail to address the complexity of environmental interactions because they lack real-time data integration and limited access to accurate data, leading to inaccuracies and resource mismanagement. Addressing these issues requires the development of advanced Machine Learning (ML) models-based system that can predict crop yield with high accuracy in dynamic environments. This paper presents YieldVision, a decision support system driven by advanced ML models for Crop Yield Prediction based on environmental, soil, and crop-specific factors. In this work, we proposed three crop prediction models: Random Forest (RF), Long Short Time Memory (LSTM) and Hybrid (RF LSTM). RF regressor effectively captured the complex, nonlinear relationships among soil nutrients, fertilizer application, and temperature. The LSTM network learned the way short-term weather fluctuations impact crop development over time, and the hybrid model combined LSTM for environmental-related factors, and RF for soil-related factors. Experimental results on the Crop Yield Prediction Dataset containing soil and weather parameters showed that RF has the highest accuracy 𝑅2 = 99.10% and outperforms both LSTM and hybrid which have 87.50% and 97.30% accuracy respectively, confirming its suitability for the dataset. RF has the lowest RMSE and MAE compared to LSTM and hybrid models. The models are integrated into a decision support system that is deployed to operationalize the models in real-world settings with user interfaces accessible via mobile or web-delivered real-time alerts and yield forecasts to farmers and agronomists.","农业作为全球粮食安全的基石，在实现可持续生产力方面面临着前所未有的挑战。作物产量预测作为农业规划的关键环节，因技术和方法上的不足而效率低下。传统方法依赖历史数据和经验估算，但由于缺乏实时数据集成且难以获取准确数据，往往无法应对环境相互作用的复杂性，导致预测不准确和资源管理不善。解决这些问题需要开发基于先进机器学习（ML）模型的系统，能够在动态环境中高精度地预测作物产量。本文提出了YieldVision，一个由先进机器学习模型驱动的决策支持系统，用于基于环境、土壤和作物特定因素的作物产量预测。在本研究中，我们提出了三种作物预测模型：随机森林（RF）、长短期记忆网络（LSTM）和混合模型（RF-LSTM）。随机森林回归器有效捕捉了土壤养分、施肥量和温度之间复杂的非线性关系。LSTM网络学习了短期天气波动如何随时间影响作物发育，而混合模型将LSTM用于环境相关因素，RF用于土壤相关因素。在包含土壤和天气参数的作物产量预测数据集上的实验结果表明，随机森林具有最高的准确率𝑅2 = 99.10%，优于LSTM和混合模型，后两者的准确率分别为87.50%和97.30%，证实了随机森林对该数据集的适用性。与LSTM和混合模型相比，随机森林具有最低的RMSE和MAE。这些模型被集成到一个决策支持系统中，该系统已部署用于在实际环境中运行这些模型，其用户界面可通过移动端或网页访问，向农民和农艺师提供实时警报和产量预测。","RESEARCH JOURNAL OF PURE SCIENCE AND TECHNOLOGY","2026-09-21T00:00:00Z",68,{"impact":18,"substance":190,"depth":126,"authority":18,"freshness":17,"relevant":21,"comment":272},"提出RF、LSTM及混合模型并集成决策支持系统，方法对比与精度数据扎实，对智慧农业产量预测有参考价值，但属单篇论文且来源期刊影响力有限。",[274],{"name":268,"url":265},[26,27,28,29,276],"决策支持系统",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":280},{"candidates":281,"agricultural_evidence":46},[282,286,290,297,301,307,311],{"code":32,"score":190,"supported":46,"title":283,"abstract":284,"tags":285},[50],[29,49,50],[27,276,29],{"code":61,"score":19,"supported":46,"title":287,"abstract":288,"tags":289},[],[64,65],[],{"code":291,"score":19,"supported":46,"title":292,"abstract":293,"tags":296},"F3",[],[294,295],"施肥","fertilizer",[],{"code":68,"score":19,"supported":46,"title":298,"abstract":299,"tags":300},[],[71,72],[],{"code":302,"score":76,"supported":46,"title":303,"abstract":304,"tags":306},"J1",[],[305],"农业规划",[],{"code":53,"score":21,"supported":14,"title":308,"abstract":309,"tags":310},[],[],[26],{"code":82,"score":21,"supported":14,"title":312,"abstract":313,"tags":314},[],[],[28],[316,317],"YieldVision 作物产量预测","RF LSTM 混合模型 产量预测","YieldVision作物产量预测-3197","10.56201\u002Frjpst.vol.9.no1.2026.pg176.194",{"doi":319,"openalex_id":321,"authors":322,"venue":268,"cited_by_count":103,"oa_url":9,"card":325,"direction":108,"ingested_from":111},"W7213883348",[323],{"name":324,"orcid":9},"D.J.S. Sako",{"tldr":326,"method":327,"finding":328,"direction":108,"opportunity":329},"提出YieldVision决策支持系统，用RF、LSTM及混合模型预测作物产量并部署应用。","基于土壤、环境与作物数据，构建RF、LSTM和RF-LSTM混合模型并集成到We","随机森林表现最佳，R²达99.10%，优于LSTM（87.50%）和混合模型（97.30%）。","可探索多源实时数据融合与模型可解释性，提升跨区域泛化能力并降低对高精度历史数据的依赖。","2026-09-22T23:30:43.591303Z",{"id":332,"title":333,"url":334,"summary":335,"summary_zh":336,"content":9,"source_name":337,"source_url":334,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":338,"score_detail":339,"sources":341,"tags":343,"primary_topic":345,"secondary_topics":346,"classification":347,"search_phrases":369,"slug":371,"view_count":20,"doi":372,"paper":373,"created_at":391},1909,"Prediction of lactation milk yields using machine learning based on milk production data and reproduction performance indicators","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-66738-0","The purpose of this research is prediction of total lactation milk yields with the application of machine learning techniques on the basis of data set containing production and reproductive performance indicators for Brown Swiss cows reared on a commercial dairy farm. The dataset includes variables age, number of lactations, number of days in milk (DIM), peak milk yield, 7-day average milk yield (DMY7), dry period, number of inseminations and calving interval, as well as the reproductive status as a categorical variable. Total lactation milk yield is used as a target variable. Performance of various machine learning algorithms was compared with 5-fold cross-validation and the separate test set using R², RMSE, MAE and MAPE criteria. Test results showed that the best prediction performance was provided by Extra Trees algorithm (R² = 0.924, RMSE = 510.27 kg, MAPE = 7.63%). The results showed that ensemble-based tree models allow to better capture non-linear dependencies between milk yield and production\u002Freproductive performance indicators. The interpretation of the model with SHAP analysis showed that day of milking and short-term milk production indicators have the highest importance for prediction. The results suggest that machine learning-based approaches may serve as a reliable tool for decision support systems in milk yield prediction.","本研究旨在利用机器学习技术，基于商业奶牛场饲养的瑞士褐牛的生产与繁殖性能指标数据集，预测全泌乳期产奶量。数据集包含年龄、泌乳次数、泌乳天数（DIM）、峰值产奶量、7天平均产奶量（DMY7）、干奶期、输精次数和产犊间隔等变量，以及作为分类变量的繁殖状态。全泌乳期产奶量作为目标变量。通过5折交叉验证和独立测试集，采用R²、RMSE、MAE和MAPE指标比较了多种机器学习算法的性能。测试结果表明，Extra Trees算法提供了最佳的预测性能（R² = 0.924，RMSE = 510.27 kg，MAPE = 7.63%）。结果显示，基于集成的树模型能够更好地捕捉产奶量与生产\u002F繁殖性能指标之间的非线性依赖关系。通过SHAP分析对模型进行解释表明，泌乳天数和短期产奶指标对预测具有最高的重要性。研究结果表明，基于机器学习的方法可作为产奶量预测决策支持系统的可靠工具。","Scientific Reports",69,{"impact":18,"substance":190,"depth":191,"authority":129,"freshness":54,"relevant":21,"comment":340},"基于机器学习预测奶牛泌乳量，方法新颖且数据可靠，对智慧养殖有参考价值。",[342],{"name":337,"url":334},[26,27,28,29,344],"奶牛养殖",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":348},{"candidates":349,"agricultural_evidence":46},[350,355,361,365],{"code":32,"score":351,"supported":46,"title":352,"abstract":353,"tags":354},19,[49],[29,49,50],[27,29],{"code":356,"score":223,"supported":46,"title":357,"abstract":358,"tags":360},"C2",[],[359],"奶牛",[344],{"code":53,"score":21,"supported":14,"title":362,"abstract":363,"tags":364},[],[],[26],{"code":82,"score":21,"supported":14,"title":366,"abstract":367,"tags":368},[],[],[28],[370,88],"农业人工智能 产量预测 奶牛养殖 智慧农业","农业人工智能产量预测奶牛养殖智慧农业-1909","10.1038\u002Fs41598-026-66738-0",{"doi":372,"openalex_id":374,"authors":375,"venue":337,"cited_by_count":103,"oa_url":385,"card":386,"direction":108,"ingested_from":111},"W7210294025",[376,379,382],{"name":377,"orcid":378},"Zeynep Sönmez","https:\u002F\u002Forcid.org\u002F0000-0003-2696-9138",{"name":380,"orcid":381},"Tuba Adar","https:\u002F\u002Forcid.org\u002F0000-0003-4749-5226",{"name":383,"orcid":384},"İremnur AYDIN","https:\u002F\u002Forcid.org\u002F0000-0003-3374-4586","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-66738-0_reference.pdf",{"tldr":387,"method":388,"finding":389,"direction":108,"opportunity":390},"用机器学习基于生产和繁殖数据预测奶牛总泌乳量，Extra Trees表现最佳。","比较多种机器学习算法，5折交叉验证，SHAP分析特征重要性。","Extra Trees预测最优（R²=0.924），泌乳天数和短期产奶量最重要。","可探索将繁殖性能指标与生产数据结合，优化模型泛化性，并开发实时决策支持系统。","2026-09-08T23:30:31.298607Z",{"id":393,"title":394,"url":395,"summary":396,"summary_zh":397,"content":9,"source_name":398,"source_url":395,"published_at":399,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":400,"score_detail":401,"sources":405,"tags":407,"primary_topic":408,"secondary_topics":409,"classification":410,"search_phrases":434,"slug":435,"view_count":20,"doi":436,"paper":437,"created_at":455},1765,"An Integrated AI-Based Crop Prediction and Advisory Platform for Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.55041\u002Fijcope.v2i9.017","Agriculture is an area where a small difference in information can affect a farmer’s decision about what to grow, how to manage a crop and whether an expected return is realistic. Farmers may have useful experience, but information about soil conditions, weather, crop diseases, expected yield and market prices is not always available in one place. Contract farming creates an additional information problem because farmers and contractors may not always have the same information when discussing prices and terms. SmartCrop AI is proposed as a practical digital platform that brings these requirements together. The system combines machine-learning based crop and yield prediction, soil-suitability assessment, an AI advisory component, simulated IoT data, mandi-price information and farmer-oriented reporting. Random Forest and Gradient Boosting are used in the prototype for prediction and suitability scoring, while an LLM-based Kisan Mitra component is intended to explain results in natural language. The design is based on findings from five related studies covering contract farming, IoT-based crop recommendation, AI advisory, sustainability assessment and yield-demand based crop recommendation. The present work focuses on the system design and prototype architecture; real-field sensor validation and large-scale farmer testing are considered future work. Keywords: Precision Agriculture, Crop Recommendation, Machine Learning, Artificial Intelligence, IoT, Random Forest, Yield Prediction, Contract Farming, Mandi Market Intelligence, AI Advisor.","农业是一个信息上的微小差异就可能影响农民决策的领域，这些决策包括种植何种作物、如何管理作物以及预期收益是否现实可行。农民可能拥有宝贵的经验，但关于土壤条件、天气、作物病害、预期产量和市场价格的信息并不总能集中获取。合同农业（Contract Farming）带来了额外的信息问题，因为农民和承包商在讨论价格和条款时，所掌握的信息未必总是对等的。本文提出将SmartCrop AI作为一个实用的数字平台，以整合上述各项需求。该系统融合了基于机器学习的作物与产量预测、土壤适宜性评估、AI咨询组件、模拟物联网（IoT）数据、曼迪（Mandi）市场价格信息以及面向农民的报告功能。原型中采用随机森林（Random Forest）和梯度提升（Gradient Boosting）算法进行预测和适宜性评分，同时设计了一个基于大语言模型（LLM）的Kisan Mitra组件，旨在以自然语言解释结果。该设计基于五项相关研究的发现，涵盖合同农业、基于物联网的作物推荐、AI咨询、可持续性评估以及基于产量与需求的作物推荐。当前工作聚焦于系统设计和原型架构；实地传感器验证和大规模农民测试被视为未来工作。关键词：精准农业、作物推荐、机器学习、人工智能、物联网、随机森林、产量预测、合同农业、曼迪市场情报、AI顾问。","International Journal of Creative and Open Research in Engineering and Management","2026-09-05T00:00:00Z",48,{"impact":17,"substance":402,"depth":403,"authority":17,"freshness":76,"relevant":21,"comment":404},15,14,"系统设计论文，提出整合AI与IoT的农业平台，但缺乏实地验证，影响有限。",[406],{"name":398,"url":395},[26,27,28,30],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":411},{"candidates":412,"agricultural_evidence":46},[413,417,422,426,430],{"code":53,"score":402,"supported":46,"title":414,"abstract":415,"tags":416},[58],[57,58],[26],{"code":32,"score":13,"supported":46,"title":418,"abstract":419,"tags":421},[],[29,49,420],"artificial intelligence",[27],{"code":68,"score":19,"supported":46,"title":423,"abstract":424,"tags":425},[],[71,72],[],{"code":158,"score":19,"supported":46,"title":427,"abstract":428,"tags":429},[],[161,162],[],{"code":82,"score":21,"supported":14,"title":431,"abstract":432,"tags":433},[],[],[28],[87,88],"农业人工智能产量预测作物推荐智慧农业-1765","10.55041\u002Fijcope.v2i9.017",{"doi":436,"openalex_id":438,"authors":439,"venue":398,"cited_by_count":103,"oa_url":395,"card":450,"direction":110,"ingested_from":111},"W7208816280",[440,442,444,446,448],{"name":441,"orcid":9},"Anurag Gadhave",{"name":443,"orcid":9},"Rajashri Chaube",{"name":445,"orcid":9},"Ravina Khadtare",{"name":447,"orcid":9},"Shubhangi Suryawanshi",{"name":449,"orcid":9},"Sushama Telrandhe",{"tldr":451,"method":452,"finding":453,"direction":110,"opportunity":454},"提出SmartCrop AI平台，集成机器学习预测、土壤评估、AI咨询和市场价格信息，辅助精准农业决","使用随机森林和梯度提升进行作物与产量预测，结合模拟IoT数据和LLM咨询组件。","系统设计整合多源信息，但缺乏实地传感器验证和大规模农民测试。","未来可进行实地传感器数据验证，并评估平台对合同农业中信息不对称的实际影响。","2026-09-06T23:30:14.324747Z",{"id":457,"title":458,"url":459,"summary":460,"summary_zh":461,"content":9,"source_name":121,"source_url":459,"published_at":462,"category":12,"cover_url":9,"hotness":123,"is_selected":14,"score":463,"score_detail":464,"sources":466,"tags":470,"primary_topic":472,"secondary_topics":473,"classification":474,"search_phrases":506,"slug":507,"view_count":20,"doi":508,"paper":509,"created_at":521},1527,"Smart Crop Recommendation System","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22245870","Agriculture remains the primary driver of India's economic stability and food security. However, conventional farming heavily relies on subjective experience rather than scientific data, resulting in sub-optimal crop selection, improper resource usage, and heightened vulnerability to changing weather and plant diseases. To resolve these limitations, this paper presents the Smart Crop Recommendation System, an integrated decision-support web platform utilizing Artificial Intelligence, Machine Learning, Deep Learning, and Real-time Weather Analytics. The proposed application evaluates soil composition—specifically Nitrogen (N), Phosphorus (P), Potassium (K), and pH levels—alongside environmental parameters including temperature, humidity, and rainfall to accurately recommend optimal crops using Scikit-Learn classification algorithms. Real-time weather forecasting is integrated via the OpenWeather API to guide critical agricultural schedules such as sowing and irrigation. Furthermore, a Deep Learning module employing a Convolutional Neural Network (CNN) detects crop diseases from uploaded leaf images and outputs targeted treatment strategies. Implemented with a Django web framework, SQLite database, Power BI analytical dashboards, and cloud infrastructure, the platform offers an end-to-end digital assistant that boosts yield productivity, minimizes farming risks, and supports sustainable precision agriculture.","农业仍然是印度经济稳定和粮食安全的主要驱动力。然而，传统农业严重依赖主观经验而非科学数据，导致作物选择欠佳、资源利用不当，以及对气候变化和植物病害的脆弱性增加。为解决这些局限性，本文提出了智能作物推荐系统——一个集成的决策支持网络平台，利用人工智能、机器学习、深度学习和实时天气分析技术。该应用系统评估土壤成分——特别是氮（N）、磷（P）、钾（K）和pH值——以及包括温度、湿度和降雨量在内的环境参数，通过Scikit-Learn分类算法准确推荐最佳作物。通过OpenWeather API集成实时天气预报，以指导播种和灌溉等关键农事安排。此外，采用卷积神经网络（CNN）的深度学习模块可从上传的叶片图像中检测作物病害，并输出针对性的治理策略。该平台基于Django网络框架、SQLite数据库、Power BI分析仪表板和云基础设施实现，提供端到端的数字助手，可提高产量生产力、降低农业风险，并支持可持续的精准农业。","2026-09-02T00:00:00Z",62,{"impact":18,"substance":128,"depth":402,"authority":13,"freshness":54,"relevant":21,"comment":465},"系统整合AI与气象数据，提供作物推荐与病害检测，对精准农业有参考价值。",[467,468],{"name":121,"url":459},{"name":121,"url":469},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22245869",[26,27,29,30,471],"病害识别",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":475},{"candidates":476,"agricultural_evidence":46},[477,481,485,489,493,500],{"code":32,"score":18,"supported":46,"title":478,"abstract":479,"tags":480},[],[145,29,49,146,420],[27,29,471],{"code":53,"score":54,"supported":46,"title":482,"abstract":483,"tags":484},[],[57,58],[26],{"code":61,"score":19,"supported":46,"title":486,"abstract":487,"tags":488},[],[64,65],[],{"code":68,"score":19,"supported":46,"title":490,"abstract":491,"tags":492},[],[71,72],[],{"code":494,"score":19,"supported":46,"title":495,"abstract":496,"tags":499},"H3",[],[497,498],"灌溉","irrigation",[],{"code":501,"score":76,"supported":46,"title":502,"abstract":503,"tags":505},"H7",[],[504],"气候变化",[],[165,166],"农业人工智能作物推荐智慧农业机器学习-1527","10.5281\u002Fzenodo.22245870",{"doi":508,"openalex_id":510,"authors":511,"venue":121,"cited_by_count":103,"oa_url":459,"card":516,"direction":110,"ingested_from":111},"W7206166550",[512,514],{"name":513,"orcid":9},"Jayashree S P",{"name":515,"orcid":9},"S Sahana",{"tldr":517,"method":518,"finding":519,"direction":108,"opportunity":520},"提出智能作物推荐系统，结合AI与实时天气，推荐作物并检测病害。","使用Scikit-Learn分类算法、CNN、OpenWeather API、D","系统能提高产量、降低风险，支持可持续精准农业。","可扩展至多作物区域适应性、考虑经济因素及用户反馈的个性化推荐。","2026-09-03T23:30:09.661533Z"]