Identification of Bacterial Leaf Blight and Powdery Mildew Diseases Based on a Combination of Histogram of Oriented Gradient and Local Binary Pattern Features

Abstract

Quantity and quality of agricultural products are significantly reduced by diseases. Identification and classification of these plant diseases using plant leaf images is one of the important agricultural areas of research for which machinelearning models can be employed. The Powdery Mildew and Bacterial Leaf Blight diseases are two common diseases that can have a severe effect on crop production. To minimize the loss incurred by Powdery Mildew and Bacterial Leaf Blight diseases and to ensure more accurate automatic detection of these pathogens, this paper proposes an approach for identifying these diseases, based on a combination of Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) features (HOG + LBP) using Naïve Bayes (NB) Classifier. The NB classifier was also trained with only the HOG features and also trained with only the LBP features. However the NB classifier trained with the HOG + LBP features obtained a higher performance accuracy of 95.45% as compared to NB classifier trained with only HOG features and NB classifier trained only with LBP features with accuracy of 90.91% and 86.36% respectively