A Hybrid Model for Pest identification in Groundnut Plants using BLR and SVM Techniques

Abstract

Plants and trees are vital for life of person and animal. The plants have diseases that are also caused by fungi, insects, Pest and viruses. For identifying the types of pests and the percentage of disease affected, various strategies have been developed and applied. Techniques for image classification are commonly used in a variety of applications.This paper investigates the use of a hybrid model of Binomial Logistic Regression and Support Vector Machine (BLR-SVM) for pest detection in groundnut leaf photographs. Image classification plays a vital part of pest identification by image processing.For pests and diseases classification, a range of techniques and algorithms have been developed. The efficiency of a hybrid method of BLR-SVM classification algorithms for detecting pests in groundnut leaf images is examined in this article. The Accuracy, F1-Score, and Area Under the Curve (AUC) are used to demonstrate the efficiency of the hybrid image classification method in the analysis.