Machine Learning-Based Real-Time Detection of Apple Leaf Diseases: An Enhanced Pre- processing Perspective

Abstract

Cedar, rust, spot, frogeye, and healthy leaf are the five general types of apple leaf diseases (ALDs). An early phase diagnosis and precise detection of ALDs can manage the extent of infection and confirm the well growth of the apple production. The previous analysis utilizes difficult digital image processing (DIP) and can’t be sure of a high accuracy rate for ALDs. This article introduces a precise detecting method for ALDs based on the deep learning (DL) method. It contains creating efficient PATHOLOGICAL images and proposing a new framework of a DL method to detect ALDs. Utilizing a database of 3,174 images of ALDs, the researched DL model is trained to detect the five general ALDs. This proposed work specifies that the research segmentation, transformation, and feature extraction methods give an enhanced outcome in disease handle for ALDs with maximum performance of detection rate. This article has created an effort to implement an approach that can detect the disease of apple leaves using different pre-processing methods. ALDs framework is designed for filtration, and color space transformation methods using Median, Gaussian, HIS, and HSV models. Grey Level Co-occurrence Matrix (GLCM) is used for the texture-based feature extraction (FE) method and the image creation method implemented in this article can improve the robustness of the improved feature extraction method.