Comparative Analysis of Machine Learning Algorithms for Disease Detection in Apple Leaves

Abstract

Leaves serve as unique indicators to distinguish the diseased leaves because the image information of the leaf changes when it is suffering from some disease. To detect these diseases, we need to recognize the patterns formed by the diseases in the leaves. Generally, plants are observed with a naked eye by either experts or farmers to detect and identify the plants. But this method can be expensive and time processing; therefore, it is essential to automate crop disease diagnosis in regions with few experts. This work revolves around an approach to developing a plant disease detection model based on apple leaves. The proposed methodology uses the following three feature extraction techniques: Hu Moments, Haralick Texture, and Color Histogram. The research work provides a comparative analysis of machine learning models for detecting diseases in apple leaves, namely: Black Rot, Cedar Apple Rust, and Apple Scab. The model is evaluated on a subset of the “Plant Village Dataset” dealing with apple leaves. Out of all the machine learning models fitted, Random Forest has obtained the highest test accuracy of 98.125 percent.