[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2302":3},{"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,"view_count":32,"doi":33,"paper":34,"created_at":61},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。",null,"The Open Agriculture Journal","2026-09-11T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,21,17,12,8,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","物联网","精准农业","作物推荐",0,"10.2174\u002F0118743315503187260909160504",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":6,"card":54,"direction":58,"ingested_from":60},"W7212307638",[37,39,42,45,48,51],{"name":38,"orcid":9},"Shreya Sriram",{"name":40,"orcid":41},"Prajeesh C B","https:\u002F\u002Forcid.org\u002F0000-0002-8404-8583",{"name":43,"orcid":44},"Delphin Raj Kesari Mary","https:\u002F\u002Forcid.org\u002F0000-0002-8989-7090",{"name":46,"orcid":47},"Anju S. Pillai","https:\u002F\u002Forcid.org\u002F0000-0001-5298-6789",{"name":49,"orcid":50},"R. V.","https:\u002F\u002Forcid.org\u002F0000-0002-0699-2990",{"name":52,"orcid":53},"V. M. Manikandan","https:\u002F\u002Forcid.org\u002F0000-0001-6903-7563",{"tldr":55,"method":56,"finding":57,"direction":58,"opportunity":59},"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。","智慧农业 \u002F 农业物联网","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。","openalex","2026-09-13T23:30:09.745236Z"]