| Title |
Tomato Leaf Disease Classification with Image Augmentation Methods |
| Authors |
함현식(Hyun-sik Ham) ; 조현종(Hyun-chong Cho) |
| DOI |
https://doi.org/10.5370/KIEE.2021.70.1.184 |
| Keywords |
Convolutional Neural Network; Generative Adversarial Network; Image Augmentation; Tomato Disease |
| Abstract |
The tomato is one of important crops in the world market with high commercial value. The early detection of disease is crucial for an successful crop yield. Many studies have recently been conducted to identify plant disease. In this paper, tomato disease classification using leaf images is proposed. Using the convolutional neural network(CNN), the features of disease are extracted and learned to classify. Data augmentation methods, Google’s AutoAugment algorithm and GAN(Generative Adversarial Networks), are used to increase tomato disease data. The classification model classifies nine classes of tomato disease. We compared the original model with the data augmentation models and explored that the classification that produced good performance. As a result, the SVHN policy of AutoAugment model achieved F1 Score 0.945 |