[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3258":3,"related-3258":116},{"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":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":70,"slug":73,"view_count":74,"doi":75,"paper":76,"created_at":115},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。",null,"Smart Agricultural Technology","2026-09-20T00:00:00Z","论文",10,false,71,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,17,13,9,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","产量预测","机器学习","蜂产业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,60,65],{"code":33,"score":46,"supported":47,"title":48,"abstract":50,"tags":51},16,true,[49],"machine learning",[30,49],[28,30],{"code":53,"score":54,"supported":47,"title":55,"abstract":56,"tags":59},"I5",6,[],[57,58],"物联网","internet of things",[],{"code":61,"score":22,"supported":14,"title":62,"abstract":63,"tags":64},"I1",[],[],[27],{"code":66,"score":22,"supported":14,"title":67,"abstract":68,"tags":69},"I3",[],[],[29],[71,72],"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":75,"openalex_id":77,"authors":78,"venue":10,"cited_by_count":107,"oa_url":6,"card":108,"direction":112,"ingested_from":114},"W7213773430",[79,82,85,88,90,93,96,99,101,104],{"name":80,"orcid":81},"Roberto Ahumada‐García","https:\u002F\u002Forcid.org\u002F0000-0003-1107-4606",{"name":83,"orcid":84},"David Zabala‐Blanco","https:\u002F\u002Forcid.org\u002F0000-0002-5692-5673",{"name":86,"orcid":87},"Víctor Hugo Monzón","https:\u002F\u002Forcid.org\u002F0000-0001-9729-7768",{"name":89,"orcid":9},"Iván Sánchez",{"name":91,"orcid":92},"Nádia Félix Felipe da Silva","https:\u002F\u002Forcid.org\u002F0000-0002-3875-2211",{"name":94,"orcid":95},"Thierson Couto Rosa","https:\u002F\u002Forcid.org\u002F0000-0001-7117-3994",{"name":97,"orcid":98},"Alef Iury Siqueira Ferreira","https:\u002F\u002Forcid.org\u002F0000-0002-9119-6357",{"name":100,"orcid":9},"Xaviera López-Cortés",{"name":102,"orcid":103},"Marco Javier Flores-Calero","https:\u002F\u002Forcid.org\u002F0000-0001-7507-3325",{"name":105,"orcid":106},"Philip Vásquez-Iglesias","https:\u002F\u002Forcid.org\u002F0009-0008-2109-8787",0,{"tldr":109,"method":110,"finding":111,"direction":112,"opportunity":113},"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。","农业人工智能与决策模型","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。","openalex","2026-09-23T23:30:03.793669Z",{"total":54,"page":22,"page_size":54,"items":117},[118,196,261,332,399,455],{"id":119,"title":120,"url":121,"summary":122,"summary_zh":123,"content":9,"source_name":124,"source_url":121,"published_at":125,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":126,"score_detail":127,"sources":130,"tags":132,"primary_topic":134,"secondary_topics":135,"classification":136,"search_phrases":180,"slug":183,"view_count":54,"doi":184,"paper":185,"created_at":195},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":17,"substance":18,"depth":46,"authority":17,"freshness":128,"relevant":22,"comment":129},8,"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。",[131],{"name":124,"url":121},[27,28,29,30,133],"决策支持系统",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":137},{"candidates":138,"agricultural_evidence":47},[139,144,151,158,165,172,176],{"code":33,"score":18,"supported":47,"title":140,"abstract":142,"tags":143},[141],"decision support",[30,49,141],[28,133,30],{"code":145,"score":54,"supported":47,"title":146,"abstract":147,"tags":150},"A8",[],[148,149],"粮食安全","food security",[],{"code":152,"score":54,"supported":47,"title":153,"abstract":154,"tags":157},"F3",[],[155,156],"施肥","fertilizer",[],{"code":159,"score":54,"supported":47,"title":160,"abstract":161,"tags":164},"H2",[],[162,163],"土壤","soil",[],{"code":166,"score":167,"supported":47,"title":168,"abstract":169,"tags":171},"J1",3,[],[170],"农业规划",[],{"code":61,"score":22,"supported":14,"title":173,"abstract":174,"tags":175},[],[],[27],{"code":66,"score":22,"supported":14,"title":177,"abstract":178,"tags":179},[],[],[29],[181,182],"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":184,"openalex_id":186,"authors":187,"venue":124,"cited_by_count":107,"oa_url":9,"card":190,"direction":112,"ingested_from":114},"W7213883348",[188],{"name":189,"orcid":9},"D.J.S. Sako",{"tldr":191,"method":192,"finding":193,"direction":112,"opportunity":194},"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":197,"title":198,"url":199,"summary":200,"summary_zh":201,"content":9,"source_name":202,"source_url":199,"published_at":203,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":204,"score_detail":205,"sources":208,"tags":210,"primary_topic":212,"secondary_topics":213,"classification":214,"search_phrases":236,"slug":239,"view_count":240,"doi":241,"paper":242,"created_at":260},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","2026-09-07T00:00:00Z",69,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":206,"relevant":22,"comment":207},7,"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。",[209],{"name":202,"url":199},[27,28,29,30,211],"奶牛养殖",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":215},{"candidates":216,"agricultural_evidence":47},[217,222,228,232],{"code":33,"score":218,"supported":47,"title":219,"abstract":220,"tags":221},19,[49],[30,49,141],[28,30],{"code":223,"score":74,"supported":47,"title":224,"abstract":225,"tags":227},"C2",[],[226],"奶牛",[211],{"code":61,"score":22,"supported":14,"title":229,"abstract":230,"tags":231},[],[],[27],{"code":66,"score":22,"supported":14,"title":233,"abstract":234,"tags":235},[],[],[29],[237,238],"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","农业人工智能产量预测奶牛养殖智慧农业-1909",2,"10.1038\u002Fs41598-026-66738-0",{"doi":241,"openalex_id":243,"authors":244,"venue":202,"cited_by_count":107,"oa_url":254,"card":255,"direction":112,"ingested_from":114},"W7210294025",[245,248,251],{"name":246,"orcid":247},"Zeynep Sönmez","https:\u002F\u002Forcid.org\u002F0000-0003-2696-9138",{"name":249,"orcid":250},"Tuba Adar","https:\u002F\u002Forcid.org\u002F0000-0003-4749-5226",{"name":252,"orcid":253},"İremnur AYDIN","https:\u002F\u002Forcid.org\u002F0000-0003-3374-4586","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-66738-0_reference.pdf",{"tldr":256,"method":257,"finding":258,"direction":112,"opportunity":259},"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":262,"title":263,"url":264,"summary":265,"summary_zh":266,"content":9,"source_name":267,"source_url":264,"published_at":203,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":268,"score_detail":269,"sources":271,"tags":273,"primary_topic":275,"secondary_topics":276,"classification":277,"search_phrases":309,"slug":311,"view_count":22,"doi":312,"paper":313,"created_at":331},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。","International Research Journal on Advanced Engineering Hub (IRJAEH)",40,{"impact":128,"substance":17,"depth":17,"authority":54,"freshness":240,"relevant":22,"comment":270},"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。",[272],{"name":267,"url":264},[27,28,29,30,274],"作物推荐",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":278},{"candidates":279,"agricultural_evidence":47},[280,285,291,295,299,305],{"code":33,"score":281,"supported":47,"title":282,"abstract":283,"tags":284},11,[],[30,49,141],[28,30],{"code":61,"score":206,"supported":47,"title":286,"abstract":287,"tags":290},[],[288,289],"精准农业","precision agriculture",[27],{"code":145,"score":54,"supported":47,"title":292,"abstract":293,"tags":294},[],[148,149],[],{"code":159,"score":54,"supported":47,"title":296,"abstract":297,"tags":298},[],[162,163],[],{"code":300,"score":167,"supported":47,"title":301,"abstract":302,"tags":304},"H8",[],[303],"可持续农业",[],{"code":66,"score":22,"supported":14,"title":306,"abstract":307,"tags":308},[],[],[29],[310,238],"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作物推荐智慧农业-1885","10.47392\u002Firjaeh.2026.0696",{"doi":312,"openalex_id":314,"authors":315,"venue":267,"cited_by_count":107,"oa_url":264,"card":325,"direction":330,"ingested_from":114},"W7211877620",[316,318,320,322,323],{"name":317,"orcid":9},"Choudhuri Saswat Pattnaik",{"name":319,"orcid":9},"Rojalini Mohanty",{"name":321,"orcid":9},"Bijaya Laxmi Hazra",{"name":321,"orcid":9},{"name":324,"orcid":9},"Akash Kumar Jena",{"tldr":326,"method":327,"finding":328,"direction":112,"opportunity":329},"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。","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","2026-09-08T23:30:07.974730Z",{"id":333,"title":334,"url":335,"summary":336,"summary_zh":337,"content":9,"source_name":338,"source_url":335,"published_at":339,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":340,"score_detail":341,"sources":344,"tags":346,"primary_topic":347,"secondary_topics":348,"classification":349,"search_phrases":380,"slug":382,"view_count":74,"doi":383,"paper":384,"created_at":398},1410,"Machine Learning-Based Crop Yield Forecasting Using Environmental and Agricultural Parameters","https:\u002F\u002Fdoi.org\u002F10.66261\u002Fncprps68","Crop yield forecasting is an important component of precision agriculture, as reliable predictions can support agricultural planning, resource allocation, and food-security decisions. However, crop productivity is influenced by several interacting environmental and agricultural factors, making accurate prediction a challenging task. This study develops a machine learning framework for crop yield forecasting by considering rainfall, average temperature, pesticide usage, crop type, geographical area, and year-wise agricultural information. A processed dataset derived from publicly available FAO and World Bank sources was used for the experimental analysis. Seven regression algorithms, namely Linear Regression, Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbors (KNN), Decision Tree, and Bagging Regressor, were trained and comparatively evaluated. Their performance was assessed using Mean Squared Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), coefficient of determination (R²), and 5-Fold Cross-Validation. The experimental results show that ensemble-based models provide stronger predictive performance than conventional regression approaches. Among the evaluated models, the Bagging Regressor produced the highest test R² score of 98.59% and a mean cross-validation score of 98.80%, while Random Forest and XGBoost also demonstrated strong predictive performance. The main contribution of this study is a comparative crop yield forecasting framework that combines agricultural and environmental variables with multi-model evaluation, cross-validation, feature-importance analysis, and multiple error metrics. The results demonstrate the potential of ensemble machine learning methods for crop yield estimation and provide a foundation for their future application in data-driven smart agriculture systems.","作物产量预测是精准农业的重要组成部分，可靠的预测能够为农业规划、资源配置和粮食安全决策提供支持。然而，作物生产力受到多种相互关联的环境和农业因素的影响，使得准确预测成为一项具有挑战性的任务。本研究开发了一种基于机器学习的作物产量预测框架，综合考虑降雨量、平均温度、农药使用量、作物类型、地理区域及逐年农业信息。实验分析使用了源自公开可获取的FAO和世界银行数据并经处理的数据集。研究训练并比较评估了七种回归算法，即线性回归、随机森林、梯度提升、XGBoost、K近邻（KNN）、决策树和Bagging回归器。其性能通过均方误差（MSE）、平均绝对误差（MAE）、平均绝对百分比误差（MAPE）、决定系数（R²）以及5折交叉验证进行评估。实验结果表明，基于集成学习的模型比传统回归方法具有更强的预测性能。在评估的模型中，Bagging回归器取得了最高的测试R²分数98.59%，平均交叉验证分数为98.80%，同时随机森林和XGBoost也展现出较强的预测性能。本研究的主要贡献在于构建了一个比较性的作物产量预测框架，该框架将农业与环境变量同多模型评估、交叉验证、特征重要性分析及多种误差指标相结合。研究结果证明了集成机器学习方法在作物产量估算方面的潜力，并为其在未来数据驱动的智慧农业系统中的实际应用奠定了基础。","Interdisciplinary Journal of AI Machine Learning & Data Science","2026-09-01T00:00:00Z",63,{"impact":17,"substance":342,"depth":46,"authority":13,"freshness":206,"relevant":22,"comment":343},18,"研究对比多种机器学习模型预测作物产量，方法系统，结果可靠，对精准农业有参考价值。",[345],{"name":338,"url":335},[27,28,29,30],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":350},{"candidates":351,"agricultural_evidence":47},[352,356,361,368,372,376],{"code":33,"score":46,"supported":47,"title":353,"abstract":354,"tags":355},[49],[30,49],[28,30],{"code":61,"score":13,"supported":47,"title":357,"abstract":358,"tags":360},[],[27,288,359,289],"smart agriculture",[27],{"code":362,"score":54,"supported":47,"title":363,"abstract":364,"tags":367},"F4",[],[365,366],"农药","pesticide",[],{"code":145,"score":167,"supported":47,"title":369,"abstract":370,"tags":371},[],[148],[],{"code":166,"score":167,"supported":47,"title":373,"abstract":374,"tags":375},[],[170],[],{"code":66,"score":22,"supported":14,"title":377,"abstract":378,"tags":379},[],[],[29],[381,238],"农业人工智能 产量预测 智慧农业 机器学习","农业人工智能产量预测智慧农业机器学习-1410","10.66261\u002Fncprps68",{"doi":383,"openalex_id":385,"authors":386,"venue":338,"cited_by_count":107,"oa_url":392,"card":393,"direction":330,"ingested_from":114},"W7204951521",[387,389],{"name":388,"orcid":9},"Pavan Sahu",{"name":390,"orcid":391},"Om Prakash Karada","https:\u002F\u002Forcid.org\u002F0009-0000-1266-4232","https:\u002F\u002Fijaimlds.com\u002Fijaimlds\u002Farticle\u002Fdownload\u002F94\u002F64",{"tldr":394,"method":395,"finding":396,"direction":112,"opportunity":397},"用机器学习集成方法预测作物产量，比较七种回归算法，发现Bagging效果最佳。","使用FAO和世界银行数据，训练七种回归模型，用MSE等指标和5折交叉验证评估。","集成模型优于传统回归，Bagging回归器R²最高达98.59%。","可探索集成模型在区域尺度或不同作物上的泛化性，或引入更多环境变量如土壤湿度。","2026-09-02T23:30:12.303117Z",{"id":400,"title":401,"url":402,"summary":403,"summary_zh":404,"content":9,"source_name":405,"source_url":402,"published_at":406,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":407,"score_detail":408,"sources":410,"tags":412,"primary_topic":415,"secondary_topics":416,"classification":417,"search_phrases":435,"slug":438,"view_count":107,"doi":439,"paper":440,"created_at":454},5587,"Calibration-Shift Inference for Computer Vision Agriculture Yield Models Under Retrofit Sensor Replacement: An Artificial Intelligence Post-Deployment Recalibration Guarantee","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23544195","Computer vision yield models deployed in agricultural fields are routinely paired with retrofit sensor packages that replace or supplement the original imaging hardware, and the resulting calibration shift can silently invalidate the model's predictive guarantees. The analysis characterises the operating characteristics of the design under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64, using 2000 replications per design cell across cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The design attains 80% power at a 5 percentage point effect with 54 clusters. The paper states a formal guarantee: under stated assumptions on the stability of the calibration map, the before-after-control-impact estimator yields a consistent and asymptotically normal estimate of the calibration shift, and the associated confidence interval covers the true shift at the nominal rate. The contribution is a design-theoretic account of how retrofit-induced calibration shift can be detected and bounded without observational data, together with the sample-size requirements that follow.","部署在农田中的计算机视觉产量模型通常与改装传感器组件配合使用，这些组件用于替换或补充原始成像硬件，由此产生的校准偏移可能悄然使模型的预测保证失效。该分析在声明的方差结构下刻画了设计的工作特性，其中组间标准差为6.0、残差标准差为4.5、组内相关系数为0.64，每个设计单元采用2000次重复，聚类数分别为8、16、24和32，效应量为3、5和8个百分点。该设计在54个聚类、5个百分点效应下达到80%的检验力。论文给出了一项形式化保证：在关于校准映射稳定性的既定假设下，前后对照影响（before-after-control-impact）估计量给出校准偏移的一致且渐近正态的估计，且相关置信区间以名义覆盖率覆盖真实偏移。其贡献在于从设计理论角度说明了如何在没有观测数据的情况下检测并界定改装引起的校准偏移，以及随之而来的样本量要求。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",65,{"impact":128,"substance":342,"depth":19,"authority":17,"freshness":13,"relevant":22,"comment":409},"方法学扎实、统计设计严谨的预印本论文，但偏理论设计层面，缺乏田间实证数据，产业影响有限，适合作为技术参考而非每日精选头条。",[411],{"name":405,"url":402},[27,28,413,29,414],"计算机视觉","传感器校准",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":418},{"candidates":419,"agricultural_evidence":47},[420,427,431],{"code":33,"score":421,"supported":47,"title":422,"abstract":425,"tags":426},24,[423,424],"artificial intelligence","computer vision",[413,424],[28,413],{"code":61,"score":22,"supported":14,"title":428,"abstract":429,"tags":430},[],[],[27],{"code":66,"score":22,"supported":14,"title":432,"abstract":433,"tags":434},[],[],[29],[436,437],"计算机视觉 产量预测 传感器校准","农业人工智能 传感器校准 计算机视觉 产量预测","计算机视觉产量预测传感器校准-5587","10.5281\u002Fzenodo.23544195",{"doi":439,"openalex_id":441,"authors":442,"venue":405,"cited_by_count":107,"oa_url":402,"card":449,"direction":112,"ingested_from":114},"W7223104380",[443,445,447],{"name":444,"orcid":9},"Tigist Alemu Gebremariam",{"name":446,"orcid":9},"Hawi Tesfaye Desta",{"name":448,"orcid":9},"Mulugeta Bekele Wolde",{"tldr":450,"method":451,"finding":452,"direction":112,"opportunity":453},"提出改造传感器替换后视觉产量模型校准漂移的检测与置信区间保证方法。","用前后对照影响估计量，基于方差结构做2000次重复的模拟设计分析。","5个百分点效应下54个集群可达80%检验力，估计量一致且渐近正态。","可将该设计理论扩展到多传感器异构替换与在线校准监测，填补真实田间验证空白。","2026-10-22T11:21:01.554995Z",{"id":456,"title":457,"url":458,"summary":459,"summary_zh":460,"content":9,"source_name":405,"source_url":458,"published_at":406,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":461,"score_detail":462,"sources":464,"tags":466,"primary_topic":469,"secondary_topics":470,"classification":475,"search_phrases":491,"slug":493,"view_count":107,"doi":494,"paper":495,"created_at":506},5570,"Evaluation of Counterfactual Explanation Robustness for Computer Vision Agriculture Yield-Forecast Models Under Satellite Cloud-Mask Gaps: An Interpretability Reliability Bound for Cooperative Storage Decisions","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545079","Counterfactual explanations are increasingly proposed as the interface through which cooperative storage managers interrogate computer-vision yield-forecast models, yet their reliability under the satellite cloud-mask gaps that routinely afflict agricultural remote sensing has not been bounded. The bound is derived from a Lipschitz continuity assumption on the forecast model and a masking-operator perturbation analysis, and it is expressed as a function of the cloud-mask gap fraction, the model's local Lipschitz constant, and the counterfactual search tolerance. A replication-based evaluation of the before-after-control-impact estimator was conducted to characterise the operating characteristics of the design under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64), with power estimated across 2000 design cells. The reliability bound degrades non-linearly as the gap fraction rises, and that the intraclass correlation of 0.64 implies that cooperative-level aggregation of counterfactual recourse is necessary to preserve explanation stability. The contribution is a decision-relevant reliability bound that storage cooperatives can use to set a maximum tolerable cloud-mask gap before counterfactual recourse is suspended.","反事实解释日益被提出作为合作式仓储管理者质询计算机视觉产量预测模型的接口，然而在农业遥感中经常出现的卫星云掩膜缺口条件下，其可靠性尚未得到界定。该界基于预测模型上的Lipschitz连续性假设和掩膜算子扰动分析推导得出，并表示为云掩膜缺口比例、模型局部Lipschitz常数和反事实搜索容差的函数。对前后对照影响估计量进行了基于重复的评估，以表征该设计在给定方差结构（组间标准差=6.0，残差标准差=4.5，组内相关系数=0.64）下的运行特征，并在2000个设计单元中估计了检验效能。可靠性界随缺口比例上升呈非线性退化，且组内相关系数0.64意味着需要对反事实救济进行合作社层面的聚合，以保持解释的稳定性。本文的贡献在于提供了一个与决策相关的可靠性界，仓储合作社可据此设定在暂停反事实救济之前可容忍的最大云掩膜缺口。",67,{"impact":128,"substance":18,"depth":19,"authority":17,"freshness":13,"relevant":22,"comment":463},"该论文提出反事实解释在卫星云掩膜缺口下的可靠性边界，方法新颖且结论可用于合作社决策，但属细分方法学研究，产业影响有限。",[465],{"name":405,"url":458},[27,28,29,467,468],"遥感","可解释性",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[471],{"code":66,"slug":472,"name":473,"path":474,"parent_code":37},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":40,"locked":14,"version":41,"method":42,"evidence":476},{"candidates":477,"agricultural_evidence":47},[478,482,487],{"code":33,"score":17,"supported":47,"title":479,"abstract":480,"tags":481},[424],[413],[28],{"code":66,"score":128,"supported":47,"title":483,"abstract":484,"tags":486},[],[467,485],"remote sensing",[29,467],{"code":61,"score":22,"supported":14,"title":488,"abstract":489,"tags":490},[],[],[27],[492,238],"农业人工智能 产量预测 可解释性 智慧农业","农业人工智能产量预测可解释性智慧农业-5570","10.5281\u002Fzenodo.23545079",{"doi":494,"openalex_id":496,"authors":497,"venue":405,"cited_by_count":107,"oa_url":458,"card":500,"direction":504,"ingested_from":114},"W7223114002",[498],{"name":499,"orcid":9},"Kadiatou Barry",{"tldr":501,"method":502,"finding":503,"direction":504,"opportunity":505},"推导卫星云掩膜缺口下反事实解释可靠性的理论界，并评估其对合作社储粮决策的影响。","基于Lipschitz连续性假设与掩膜扰动分析推导可靠性界，并用2000个设计单","缺口比例上升使可靠性非线性下降；ICC=0.64表明需合作社层级聚合以保持解释稳定。","农业遥感与作物表型","可研究不同作物区云掩膜统计特性下的自适应解释暂停阈值与聚合策略。","2026-10-22T11:20:39.240791Z"]