Integrated Analysis of Machine Learning and Deep Learning in Chili Pest and Disease Identification.

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Abstract

Chili is one of the most important and high-value vegetable crops worldwide. However, pest and disease infections are among the main limiting factors in chili cultivation. These diseases cannot be eradicated but can be handled and monitored to mitigate the damage. Hence, the use of an automated identification system based on the images will promote quick identification of chili disease. The features extracted from the images are of utmost importance to develop such an accurate identification system.
In this research, chili pest and disease features extracted using the traditional-based approach were compared with features extracted using the deep learning-based approach. A total of 974 chili leaf images consisted of five types of diseases, two types of pest infestations, and a healthy class were collected. Six traditional feature-based approaches and six deep learning feature-based approaches were used to extract significant pests and disease features from the chili leaf images. The extracted features were fed into three machine learning classifiers namely Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN) for the identification task. The results showed that deep learning feature-based approaches performed comparably better than the traditional feature-based approaches by obtained the best accuracy of 92.10% with SVM classifier.
Deep learning feature-based approach could capture the details and characteristics between different types of chili pests and diseases despite similar visual patterns and symptoms among them. This article is protected by copyright. All rights reserved.
This article is protected by copyright. All rights reserved.