An Empirical Study on Machine Learning Models for Potato Leaf Disease Classification using RGB Images

Abstract

In this work, an empirical study is conducted on classification models built using RGB images of potato leaves. A series of experiments are done by training convolutional neural network (CNN) and support vector machine (SVM) using images captured in laboratory and field conditions and processed samples of images captured in field. A salient region based segmentation algorithm is devised to generate processed version of the images captured in field which performed well with respect to manually segmented ground truth of the dataset. Severe inconsistencies are observed in experimental results, particularly when train and test samples of models are similar images but captured under different environmental conditions. Following the analysis of obtained results, we come up with a set of clear directions to create an image dataset, which can lead to a reliable classification accuracy.