An Optimized Gaussian Extreme Learning Machine (GELM) for Predicting the Crop Yield using Soil Factors

• 全文链接

利用土壤因子预测作物产量的优化高斯极限学习机 (GELM)

关键词：

来源：

IEEE

全文链接：

//agri.nais.net.cn/topic/downloadFile/725e53b8-ae69-4de4-a776-708dd0a07681

来源地址：

//agri.nais.net.cn/topic/downloadFile/725e53b8-ae69-4de4-a776-708dd0a07681

资源所属：

农业水土资源监测专题

类型：

会议论文

语种：

英语

原文发布日期：

2022-06-02

摘要：

Indian agriculture is extremely important and plays a predominant role in economy and employment. The agriculture has seen a significant technological transition because of data collection, environmental factors, crop selection, soil nutrients, pesticides and plant disease for making better farming decisions. This revolution in agriculture is addressed by using emerging technologies. Early detection and management of crop yield indicator problems can help to increase the yield and subsequent profit. Machine learning is an emerging technology used in agricultural research for yield prediction. To produce accurate results, a simplest and very fast optimized learning algorithm called GELM (Gaussian Extreme Learning Machine) classifier with different kinds of activation functions are used. For the soil dataset, the classifier is trained using 50 hidden neurons with different activation functions. The performance analysis of the system shows that gaussian extreme learning achieves an accuracy of 97% compared to other algorithms. This analysis helps in interpretation of results in efficient manner for any regional soil data.