Random Forest, DT And SVM Machine Learning Classifiers for Seed with Advanced WSN Sensor Node

S. D. Shingade, R. Mudhalwadkar, K. M. Masal
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引用次数: 1

Abstract

Random Forest- RF, Support Vector Machine - SVM and Decision Tree - DT Machine learning classifiers proposed and implemented to develop seed prediction model in Visual Studio code environment using python along with advanced Wireless Sensor Network’s raino-meter sensor node. Wireless sensor network's infrastructure consists of sensors and nodes. Use of a wireless system has the potential for significant savings of workforce, resources and time. By adding Soil pH, Temperature, Humidity and Rainfall sensor in the network one can predict the crop seed to be cultivated in the current time by analyzing the environmental conditions in the farm. Aim of this work is to develop model using data analytics and machine learning techniques. Prediction model is trained on historic environmental data of crops. Based on the history and current environmental data crop recommendation is proposed. Basic data collection nodes with the sensor network provides the required information about environmental parameters. Random Forest- RF, Support Vector Machine - SVM and Decision Tree - DT Machine learning classifiers implemented and results shows Random Forest is the best with 95.12 % accuracy, 94.94 % precision, 94.85 % recall and 95.12 % F1 score. This system helps to provide the correct advice at the correct time.
随机森林,DT和SVM机器学习分类器的种子与先进的WSN传感器节点
提出并实现了随机森林- RF,支持向量机- SVM和决策树- DT机器学习分类器,使用python在Visual Studio代码环境中与先进的无线传感器网络的雨量计传感器节点一起开发种子预测模型。无线传感器网络的基础设施由传感器和节点组成。使用无线系统可以节省大量人力、资源和时间。通过在网络中添加土壤pH、温度、湿度和降雨量传感器,可以通过分析农场的环境条件来预测当前要种植的作物种子。这项工作的目的是利用数据分析和机器学习技术开发模型。预测模型是根据作物的历史环境数据进行训练的。根据历史和当前的环境数据提出作物推荐。基础数据采集节点通过传感器网络提供所需的环境参数信息。随机森林- RF,支持向量机- SVM和决策树- DT机器学习分类器实现,结果表明随机森林是最好的,准确率为95.12%,精密度为94.94%,召回率为94.85%,F1得分为95.12%。该系统有助于在正确的时间提供正确的建议。
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