Effective Data Augmentation and Training Techniques for Improving Deep Learning in Plant Leaf Disease Recognition

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Published: Jul 27, 2022
DOI: https://doi.org/10.14416/j.asep.2021.01.003
Keywords:
Plant leaf disease recognition Deep learning Convolutional neural networks Transfer learning Data augmentation

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Prem Enkvetchakul
Multi-agent Intelligent Simulation Laboratory (MISL), Department of Information Technology, Faculty of Informatics, Mahasarakham University, Maha Sarakham, Thailand
Olarik Surinta
Multi-agent Intelligent Simulation Laboratory (MISL), Department of Information Technology, Faculty of Informatics, Mahasarakham University, Maha Sarakham, Thailand

Abstract

Plant disease is the most common problem in agriculture. Usually, the symptoms appear on leaves of the plants which allow farmers to diagnose and prevent the disease from spreading to other areas. An accurate and consistent plant disease recognition system can help to prevent the spread of diseases and to save maintenance costs. In this research, we present a plant leaf disease recognition system using two deep convolutional neural networks (CNNs); MobileNetV2 and NasNetMobile. These CNN architectures are designed to be suitable for smartphones due to the models being small. We have experimented on training techniques; online, offline, and mixed training techniques on two plant leaf diseases. As for data augmentation techniques, we found that the combination of rotation, shift, and zoom techniques significantly increases the performance of the CNN architectures. The experimental results show that the most accurate algorithm for plant leaf disease recognition is NASNetMobile architecture using transfer learning. Additionally, the most accurate result is obtained when combining the offline training technique with data augmentation techniques.

Article Details

How to Cite
Enkvetchakul, P., & Surinta, O. (2022). Effective Data Augmentation and Training Techniques for Improving Deep Learning in Plant Leaf Disease Recognition. Applied Science and Engineering Progress, 15(3), 3810. https://doi.org/10.14416/j.asep.2021.01.003
Issue
Vol. 15 No. 3 (2022)
Section
Research Articles

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