Machine learning-based application to detect pepper leaf diseases using histgradientboosting classifier with fused hog and lbp features

Pepper leaf disease detection is one of the interesting challenges in the field of machine learning. In this paper, a machine learning-based approach is proposed to extract texture features and use dimensionality reduction techniques called principal component analysis (PCA) and create a composite feature descriptor. There are two different texture-based feature representations extracted by using HOG and LBP feature engineering techniques were used for the pepper leaf images, and PCA is applied to obtain reduced representations. These representations are fused and passed to machine learning models like logistic regression, naïve Bayes, decision tree, support vector machine, and HistGradientBoosting classifier for classifica-tion. HistGradientBoosting classifier achieved highest the accuracy of 89.11% and outperformed other models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.