A Robust Solution for Tea Leaf Classification using the SMOTE and Soft Voting Classifier

Background/Objectives: The purpose of the proposed work is to identify how a tea leaf is conceptualized. This further enables the categorization of leaves into healthy and unhealthy groups, assisting both the vendor and customer in obtaining the right quality for their requirements. The primary cause of the low production of tea in various parts of the country is the late detection of diseased leaves. Literature suggests that the current AI techniques can detect but have a high level of similarities. Hence, the objective is to identify diseased leaf images at early stage. Methods: Earlier studies have used mostly CNN for classification which is more time consuming, and they create their own parameters. In this study, we extracted the textual features of images by using GLCM, and further classification was implemented with the help of SMOTE and a soft voting classifier. Findings: The proposed method resulted in optimal timeline without using CNN for classification. The accuracy is better when compared to current methods. Novelty: This work presents a novel approach in calculating textual features of leaves that can be further used for classification or tea-leaf grading purposes. Keywords: Image processing, Feature extraction, GLCM, SMOTE, Voting classifier