Machine Learning and Deep Learning for Maize Leaf Disease Classification: A Review

• Published in: Journal of physics. Conference series 2022-12, Vol.2406 (1), p.12019
• Main Authors: Setiawan, W, Rochman, E M S, Satoto, B D, Rachmad, A
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Classification; Computer vision; Corn; Decision trees; Deep learning; Feature extraction; Image classification; Image processing; Image segmentation; Machine learning; Physics; Plant diseases; Support vector machines
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/2406/1/012019

Image classification of maize disease is an agriculture computer vision application. In general, the application of computer vision uses two methods: machine learning and deep learning. Implementations of machine learning classification cannot stand alone. It needs image processing techniques such as preprocessing, feature extraction, and segmentation. Usually, the features are selected manually. The classification uses k-nearest neighbor, naïve bayes, decision tree, random forest, and support vector machine. On the other side, deep learning is part of machine learning. It is a development of an artificial neural network that performs automatic feature extraction. Deep learning is capable of recognizing large data but requires high-speed computation. This article compare machine learning and deep learning for maize leaf disease classification. There are five research questions: how to get data, how machine learning and deep learning classify images, how the classification result compare both of them and the opportunities & challenges of research on maize leaf disease classification. The number of articles to review was 62, consisting of 18 articles using machine learning, 28 articles applying deep learning, and the rest are supporting articles.