Multi-Class Classification of Plant Leaf Diseases Using Feature Fusion of Deep Convolutional Neural Network and Local Binary Pattern

Plant diseases are one of the primary causes decreased agricultural production quality and quantity. With ongoing changes in plant structure cultivation techniques, new constantly arising on leaves. Thus, accurate classification detection leaf their early stages will limit spread infection support healthy development production. This work proposes a novel lightweight deep convolutional neural network (CNN) model for obtaining high-level hidden feature representations. The features then fused with traditional handcrafted local binary pattern (LBP) to capture texture information images. proposed is trained tested three publicly available datasets (Apple Leaf, Tomato Grape Leaf). On datasets, approach achieves 99%, 96.6%, 98.5% validation accuracies, respectively, 98.8%, 96.5%, 98.3% test respectively. results experiments show that can provide better control solution diseases.