[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3248":3,"related-3248":44},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":21,"tags":23,"search_phrases":29,"slug":32,"view_count":33,"doi":8,"paper":34,"created_at":43},3248,"Crop recommendation in precision agriculture: a systematic literature review of methods, trends, and challenges（精准农业中的作物推荐：方法、趋势与挑战系统综述）","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F37471110","MDPI 发表精准农业作物推荐方法系统综述：从183项研究中严格筛选129篇发表于2020-2026年的文章，使用PRISMA协议进行综合分析。研究表明集成学习方法（特别是随机森林和XGBoost）在各种农业数据集的预测性能上具有强大能力；支持向量机、决策树、k近邻等传统ML方法仍被广泛使用；同时CNN和LSTM被用于遥感和时间相关农业分析。最常用的数据集来源是Kaggle，典型输入包括土壤养分（NPK）、土壤pH、天气条件和NDVI、EVI等卫星指数。研究主要研究空白：有限的实时部署、低多数据源集成、低跨区域验证、低模型可解释性。研究表明可扩展、可解释的AI系统对农业实际应用具有重要意义。",null,"MDPI","2026-09-22T00:00:00Z","论文",10,false,81,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":12,"relevant":19,"comment":20},18,22,13,1,"基于PRISMA的129篇文献系统综述，梳理作物推荐主流方法与四大研究空白，对农业AI落地有参考价值。",[22],{"name":9,"url":6},[24,25,26,27,28],"智慧农业","农业人工智能","机器学习","作物推荐","遥感监测",[30,31],"精准农业 作物推荐 系统综述","XGBoost 随机森林 作物推荐","精准农业作物推荐系统综述-3248",0,{"doi":8,"openalex_id":8,"authors":35,"venue":8,"cited_by_count":33,"oa_url":8,"card":36,"direction":40,"ingested_from":42},[],{"tldr":37,"method":38,"finding":39,"direction":40,"opportunity":41},"系统综述129篇2020-2026年文献，梳理精准农业作物推荐的方法、趋势与挑战。","PRISMA协议系统综述，分析183项研究筛选出的129篇文献。","集成学习（随机森林、XGBoost）表现最强，主要空白为实时部署、多源集成、跨区域验证与可解释性。","农业人工智能与决策模型","可探索可解释、可跨区域泛化的实时作物推荐系统，并融合多源遥感与物联网数据。","agent","2026-09-23T00:04:33.331160Z",{"total":45,"page":19,"page_size":45,"items":46},6,[47,90,128,161,208,252],{"id":48,"title":49,"url":50,"summary":51,"summary_zh":52,"content":8,"source_name":53,"source_url":50,"published_at":54,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":55,"score_detail":56,"sources":61,"tags":63,"search_phrases":65,"slug":68,"view_count":33,"doi":69,"paper":70,"created_at":89},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)","2026-09-07T00:00:00Z",40,{"impact":57,"substance":58,"depth":58,"authority":45,"freshness":59,"relevant":19,"comment":60},8,12,2,"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。",[62],{"name":53,"url":50},[24,25,64,26,27],"产量预测",[66,67],"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":69,"openalex_id":71,"authors":72,"venue":53,"cited_by_count":33,"oa_url":50,"card":82,"direction":87,"ingested_from":88},"W7211877620",[73,75,77,79,80],{"name":74,"orcid":8},"Choudhuri Saswat Pattnaik",{"name":76,"orcid":8},"Rojalini Mohanty",{"name":78,"orcid":8},"Bijaya Laxmi Hazra",{"name":78,"orcid":8},{"name":81,"orcid":8},"Akash Kumar Jena",{"tldr":83,"method":84,"finding":85,"direction":40,"opportunity":86},"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","openalex","2026-09-08T23:30:07.974730Z",{"id":91,"title":92,"url":93,"summary":94,"summary_zh":95,"content":8,"source_name":96,"source_url":93,"published_at":97,"category":11,"cover_url":8,"hotness":98,"is_selected":13,"score":99,"score_detail":100,"sources":104,"tags":108,"search_phrases":110,"slug":113,"view_count":33,"doi":114,"paper":115,"created_at":127},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。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-02T00:00:00Z",25,62,{"impact":58,"substance":16,"depth":101,"authority":12,"freshness":102,"relevant":19,"comment":103},15,7,"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。",[105,106],{"name":96,"url":93},{"name":96,"url":107},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22245869",[24,25,26,27,109],"病害识别",[111,112],"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-1527","10.5281\u002Fzenodo.22245870",{"doi":114,"openalex_id":116,"authors":117,"venue":96,"cited_by_count":33,"oa_url":93,"card":122,"direction":87,"ingested_from":88},"W7206166550",[118,120],{"name":119,"orcid":8},"Jayashree S P",{"name":121,"orcid":8},"S Sahana",{"tldr":123,"method":124,"finding":125,"direction":40,"opportunity":126},"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",{"id":129,"title":130,"url":131,"summary":132,"summary_zh":133,"content":8,"source_name":134,"source_url":131,"published_at":135,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":136,"score_detail":137,"sources":141,"tags":143,"search_phrases":145,"slug":148,"view_count":33,"doi":149,"paper":150,"created_at":160},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":58,"substance":138,"depth":139,"authority":58,"freshness":57,"relevant":19,"comment":140},20,16,"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。",[142],{"name":134,"url":131},[24,25,64,26,144],"决策支持系统",[146,147],"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":149,"openalex_id":151,"authors":152,"venue":134,"cited_by_count":33,"oa_url":8,"card":155,"direction":40,"ingested_from":88},"W7213883348",[153],{"name":154,"orcid":8},"D.J.S. Sako",{"tldr":156,"method":157,"finding":158,"direction":40,"opportunity":159},"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":162,"title":163,"url":164,"summary":165,"summary_zh":166,"content":8,"source_name":167,"source_url":164,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":168,"score_detail":169,"sources":172,"tags":174,"search_phrases":177,"slug":180,"view_count":33,"doi":181,"paper":182,"created_at":207},3167,"Temporal trend analysis and multi-temporal satellite feature integration for mango orchard acreage estimation using machine learning algorithms approach","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-65828-3","Accurate estimation of horticultural crop acreage is essential for agricultural planning, market forecasting and evidence-based policy formulation. The present study investigated long-term trends in mango cultivation and developed a multi-temporal remote sensing framework for mango orchard acreage estimation in Navsari District using integrated optical, SAR and machine learning approaches. Time-series (temporal) data spanning 23 years (2001–02 to 2023–24) were analyzed using polynomial regression models to evaluate trends in mango area and production. Linear regression best represented area expansion trends (Adj. R² = 0.969), whereas cubic regression better captured production variability (Adj. R² = 0.634), indicating climatic and seasonal influences on productivity. For orchard classification and acreage estimation, multi-temporal Sentinel-2 imagery acquired from November 2023 to March 2024 was processed within a phenology-guided framework. Monthly composites were generated and integrated with Sentinel-1 SAR backscatter data, vegetation indices (NDVI, GNDVI, NDRE, SAVI, EVI and NDMI) and texture metrics derived from Gray Level Co-occurrence Matrix (GLCM) analysis. A comprehensive 72-band feature stack was developed for classification. Four machine learning algorithms, namely Random Forest (RF), XGBoost (XGB), Support Vector Machine (SVM) and Multinomial Logistic Regression (MNLR) were evaluated for orchard discrimination. Among the tested models, RF achieved the highest classification performance with an Overall Accuracy of 99.80% and a Kappa coefficient of 0.997, followed by SVM (99.30%), MNLR (98.21%) and XGB (9.20%). The RF model estimated mango orchard area at 36,099.94 ha, showing the closest agreement with official horticultural statistics (34,363 ha) with only 5.05% estimation error. In contrast, SVM and MNLR overestimated orchard extent by 16.03% and 43.37%, respectively. The proposed framework provides a reliable and scalable methodology for operational horticultural monitoring, crop inventory generation and precision agricultural planning in tropical orchard ecosystems.","准确估算园艺作物种植面积对于农业规划、市场预测和循证政策制定至关重要。本研究探讨了芒果种植的长期趋势，并开发了一个多时相遥感框架，结合光学、合成孔径雷达（SAR）和机器学习方法，用于纳夫萨里县芒果园种植面积估算。利用多项式回归模型分析了跨越23年（2001—02年至2023—24年）的时间序列数据，以评估芒果面积和产量的变化趋势。线性回归最能表征面积扩张趋势（调整R² = 0.969），而三次回归更能捕捉产量变异性（调整R² = 0.634），表明气候和季节性因素对生产力具有影响。在果园分类和面积估算方面，基于物候指导框架处理了2023年11月至2024年3月获取的多时相Sentinel-2影像。生成了月度合成影像，并将其与Sentinel-1 SAR后向散射数据、植被指数（NDVI、GNDVI、NDRE、SAVI、EVI和NDMI）以及基于灰度共生矩阵（GLCM）分析提取的纹理指标进行整合。构建了一个包含72个波段的综合特征集用于分类。评估了四种机器学习算法，即随机森林（RF）、XGBoost（XGB）、支持向量机（SVM）和多项逻辑回归（MNLR），用于果园判别。在测试的模型中，RF取得了最高的分类性能，总体精度为99.80%，Kappa系数为0.997，其次是SVM（99.30%）、MNLR（98.21%）和XGB（9.20%）。RF模型估算的芒果园面积为36,099.94公顷，与官方园艺统计数据（34,363公顷）最为接近，估算误差仅为5.05%。相比之下，SVM和MNLR分别高估了果园面积16.03%和43.37%。所提出的框架为热带果园生态系统中的业务化园艺监测、作物清单生成和精准农业规划提供了一种可靠且可扩展的方法。","Scientific Reports",78,{"impact":170,"substance":17,"depth":16,"authority":170,"freshness":12,"relevant":19,"comment":171},14,"方法扎实、数据规模大且精度高，但属区域性作物遥感估产研究，产业影响有限，可作为技术方法类精选。",[173],{"name":167,"url":164},[24,26,175,28,176],"芒果","作物估产",[178,179],"Navsari 芒果 遥感估产","Sentinel-2 芒果 果园面积","Navsari芒果遥感估产-3167","10.1038\u002Fs41598-026-65828-3",{"doi":181,"openalex_id":183,"authors":184,"venue":167,"cited_by_count":33,"oa_url":164,"card":201,"direction":205,"ingested_from":88},"W7213920056",[185,187,190,192,194,197,199],{"name":186,"orcid":8},"V. Raju",{"name":188,"orcid":189},"Yogesh A. Garde","https:\u002F\u002Forcid.org\u002F0000-0002-0297-316X",{"name":191,"orcid":8},"Dr. V. S. Thorat",{"name":193,"orcid":8},"V. T. Shinde",{"name":195,"orcid":196},"Nitin Varshney","https:\u002F\u002Forcid.org\u002F0000-0001-9144-5475",{"name":198,"orcid":8},"Alok Shrivastava",{"name":200,"orcid":8},"A. P. Chaudhary",{"tldr":202,"method":203,"finding":204,"direction":205,"opportunity":206},"融合多时相Sentinel-1\u002F2与机器学习，估算印度芒果园面积并分析23年种植趋势。","23年时序回归分析；Sentinel-2月合成+SAR+植被指数+GLCM纹理共","RF精度最高（总体精度99.80%，Kappa 0.997），面积估算误差仅5.05%，优于SVM和","农业遥感与作物表型","可迁移该多时相SAR-光学特征框架至其他热带果园，并探索深度学习与物候自适应特征优化。","2026-09-22T23:30:22.619289Z",{"id":209,"title":210,"url":211,"summary":212,"summary_zh":213,"content":8,"source_name":214,"source_url":211,"published_at":215,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":216,"score_detail":217,"sources":221,"tags":223,"search_phrases":226,"slug":229,"view_count":33,"doi":230,"paper":231,"created_at":251},3013,"AI and remote sensing for fungal and oomycete disease surveillance: current landscape and biological integration","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs41348-026-01352-w","AI and remote sensing for fungal and oomycete disease surveillance: current landscape and biological integration。Journal of Plant Diseases and Protection","人工智能与遥感在真菌及卵菌病害监测中的应用：现状与生物学整合。《植物病害与保护杂志》","Journal of Plant Diseases and Protection","2026-09-19T00:00:00Z",77,{"impact":16,"substance":138,"depth":218,"authority":18,"freshness":219,"relevant":19,"comment":220},17,9,"核心期刊综述，系统梳理AI与遥感在真菌及卵菌病害监测中的进展与生物学整合路径，对智慧农业植保方向有参考价值。",[222],{"name":214,"url":211},[24,25,224,28,225],"植物病害","病害预警",[227,228],"AI 遥感 真菌病害 监测","植物病害 遥感 预警","AI遥感真菌病害监测-3013","10.1007\u002Fs41348-026-01352-w",{"doi":230,"openalex_id":232,"authors":233,"venue":214,"cited_by_count":33,"oa_url":8,"card":246,"direction":205,"ingested_from":88},"W7213649225",[234,236,238,240,243],{"name":235,"orcid":8},"Biju Vadakkemukadiyil Chellappan",{"name":237,"orcid":8},"C. L. Biji",{"name":239,"orcid":8},"Vanshika Arun Meda",{"name":241,"orcid":242},"Sajad Ali","https:\u002F\u002Forcid.org\u002F0000-0002-3230-1436",{"name":244,"orcid":245},"Sherif Mohamed El‐Ganainy","https:\u002F\u002Forcid.org\u002F0000-0001-5226-4604",{"tldr":247,"method":248,"finding":249,"direction":205,"opportunity":250},"综述AI与遥感在真菌及卵菌病害监测中的现状，强调生物信息整合。","文献综述，整合AI、遥感与病原生物学数据。","AI与遥感结合可提升病害监测，但需融入病原生物学机制。","可研究将病原生活史与遥感时序特征耦合的病害预警模型。","2026-09-20T23:30:21.177583Z",{"id":253,"title":254,"url":255,"summary":256,"summary_zh":257,"content":8,"source_name":258,"source_url":255,"published_at":259,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":260,"score_detail":261,"sources":264,"tags":266,"search_phrases":269,"slug":272,"view_count":33,"doi":273,"paper":274,"created_at":291},2926,"An artificial intelligence-based stacking ensemble framework for smart irrigation pump control using IoT sensor data","https:\u002F\u002Fdoi.org\u002F10.11591\u002Fijece.v16i5.pp2652-2663","Efficient water management is essential for sustainable agricultural production, particularly in arid and semi-arid regions where water resources are limited. Machine-learning-based irrigation systems can support automated pump-operation decisions using environmental and soil-related sensor data. However, most previous studies have focused on individual machine-learning models, while the application of stacked ensembles to binary irrigation pump-status prediction remains relatively limited. This study proposes a stacking-based framework for predicting irrigation pump operation in an ON\u002FOFF classification setting. The framework uses environmental and soil-related variables, including soil moisture, temperature, humidity, and a numerical time-related feature. Random forest (RF), extreme gradient boosting (XGBoost), and multilayer perceptron models were trained as base learners, and their out-of-fold (OOF) predictions were combined using a logistic-regression meta-learner. The models were evaluated on a held-out test set using accuracy, precision, recall, specificity, and F1-score. The individual models achieved accuracies ranging from 97.34% to 99.95%, with XGBoost providing the best individual performance. The proposed stacking ensemble achieved 99.97% accuracy, 99.94% precision, 100.00% recall, 99.94% specificity, and a 99.97% F1-score. Compared with XGBoost, the ensemble further refined predictive performance, improving accuracy by 0.02 percentage points and F1-score by 0.01 percentage points while achieving complete elimination of false negatives (100.00% recall). These results demonstrate the potential of stacked ensemble learning to improve binary pump-operation prediction and support data-driven irrigation management in water-limited environments.","高效的水资源管理对可持续农业生产至关重要，尤其是在水资源有限的干旱和半干旱地区。基于机器学习的灌溉系统可以利用环境和土壤相关传感器数据支持自动化水泵运行决策。然而，以往大多数研究集中于单一机器学习模型，而堆叠集成（stacked ensemble）在二元灌溉水泵状态预测中的应用仍相对有限。本研究提出了一种基于堆叠（stacking）的框架，用于在开\u002F关（ON\u002FOFF）分类场景下预测灌溉水泵运行状态。该框架使用环境和土壤相关变量，包括土壤湿度、温度、湿度和一个数值型时间相关特征。随机森林（RF）、极端梯度提升（XGBoost）和多层感知机模型被训练为基学习器，其折外（OOF）预测结果通过逻辑回归元学习器进行组合。模型在留出测试集上使用准确率、精确率、召回率、特异度和F1分数进行评估。单一模型的准确率范围为97.34%至99.95%，其中XGBoost的单一模型性能最佳。所提出的堆叠集成达到了99.97%的准确率、99.94%的精确率、100.00%的召回率、99.94%的特异度和99.97%的F1分数。与XGBoost相比，该集成进一步优化了预测性能，准确率提高了0.02个百分点，F1分数提高了0.01个百分点，同时实现了假阴性的完全消除（100.00%召回率）。这些结果表明，堆叠集成学习在改进二元水泵运行预测和支持水资源受限环境下的数据驱动灌溉管理方面具有潜力。","International Journal of Power Electronics and Drive Systems\u002FInternational Journal of Electrical and Computer Engineering","2026-09-18T00:00:00Z",72,{"impact":58,"substance":262,"depth":218,"authority":18,"freshness":219,"relevant":19,"comment":263},21,"提出基于IoT传感器数据的堆叠集成模型实现灌溉水泵开关预测，准确率达99.97%，方法新颖、结论可靠，但属实验室验证阶段，产业影响有限。",[265],{"name":258,"url":255},[24,25,26,267,268],"物联网","智能灌溉",[270,271],"智能灌溉 水泵控制 物联网","农业人工智能 智慧农业 智能灌溉 机器学习","智能灌溉水泵控制物联网-2926","10.11591\u002Fijece.v16i5.pp2652-2663",{"doi":273,"openalex_id":275,"authors":276,"venue":258,"cited_by_count":33,"oa_url":255,"card":286,"direction":87,"ingested_from":88},"W7213546497",[277,280,283],{"name":278,"orcid":279},"Sarra Gourari","https:\u002F\u002Forcid.org\u002F0009-0005-1846-0886",{"name":281,"orcid":282},"Wafa Difallah","https:\u002F\u002Forcid.org\u002F0000-0002-6181-0395",{"name":284,"orcid":285},"Belkacem Draoui","https:\u002F\u002Forcid.org\u002F0000-0001-5490-3991",{"tldr":287,"method":288,"finding":289,"direction":87,"opportunity":290},"提出基于堆叠集成学习的灌溉泵ON\u002FOFF预测框架，用物联网传感器数据实现智能灌溉控制。","用RF、XGBoost、MLP作基学习器，逻辑回归元学习器融合OOF预测，基于土","堆叠集成达99.97%准确率和100%召回率，完全消除假阴性，优于单一XGBoost模型。","可探索多模态传感器融合与在线增量学习，提升堆叠集成在不同作物和气候区的泛化与实时部署能力。","2026-09-19T23:30:11.009076Z"]