Holy Basil Curl Leaf Disease Classification using Edge Detection and Machine Learning

Abstract

Holy basil (Ocimum basilicum L.) is one of the most vital economic crops that has a significant impact on export earnings. However, the holy basil prices could be dropped due to a curl leaf disease caused by pests. Several previous studies focused on plant leaf disease detection based on the leaf color. Unfortunately, the leaf curl disease sometimes changes a shape of the leaf not the color so it cannot be detected with those schemes. We proposed a novel approach aims to automatically detect a curling leaf on holy basil. This paper presents a Neural Network (NN) model and Logistic Regression (LR) model to automatically detect a curling leaf on holy basil. To be able to detect the infected one not by colors but by its shape, we have applied edge detection algorithms which are Canny and Sobel model. To speed up processing time, images were resized and converted to grayscale before passing them to machine learning models. Moreover, NN and LR were modified with mini-batch technique in order to increase the speed of the processing time. The dataset contains 600 images of holy basil leaves with 300 images of healthy leaves and 300 images of infected leaves. The experimental results indicate that the proposed method effectively detects the curling leaves on holy basil.