Integrated analysis of machine learning and deep learning in chili pest and disease identification
Document Type
Article
Publication Date
7-1-2021
Abstract
BACKGROUND 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 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. RESULTS In this research, chili pest and disease features extracted using the traditional approach were compared with features extracted using a deep-learning-based approach. A total of 974 chili leaf images were collected, which consisted of five types of diseases, two types of pest infestations, and a healthy type. 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 a support vector machine (SVM), a random forest (RF), and an artificial neural network (ANN) for the identification task. The results showed that deep learning feature-based approaches performed better than the traditional feature-based approaches. The best accuracy of 92.10% was obtained with the SVM classifier. CONCLUSION A deep-learning feature-based approach could capture the details and characteristics between different types of chili pests and diseases even though they possessed similar visual patterns and symptoms. (c) 2020 Society of Chemical Industry
Keywords
Chili pest and disease, Feature extraction, Machine learning, Deep learning
Divisions
InstituteofBiologicalSciences
Funders
Universiti Malaya [Grant No: ST013-2019]
Publication Title
Journal of the Science of Food and Agriculture
Volume
101
Issue
9
Publisher
Wiley
Publisher Location
111 RIVER ST, HOBOKEN 07030-5774, NJ USA