| Title |
A Study on Improvement of Tomato Disease Classification Performance According to Various Image Augmentation |
| Authors |
함현식(Hyun-Sik Ham) ; 조현종(Hyun-Chong Cho) |
| DOI |
https://doi.org/10.5370/KIEE.2021.70.12.2000 |
| Keywords |
Convolutional Neural Network; Image Augmentation; Image Selection; Tomato Disease |
| Abstract |
Crop diseases are damaging agricultural food worldwide. Early detection of crop diseases is important to prevent damage to crops.
However, it is difficult to distinguish crop diseases unless not expert. Therefore, in this paper, a crop diseases classification system that can recognize diseases even before expert judgment is proposed. The characteristics of diseases were learned and classified using Convolutional Neural Network(CNN). In addition, images are augmented to increase classification performance. To augment the image, a basic augmentation policy consisting of Random Crop and Random Horizontal Flip and Google's AutoAugment are used. Then, the augmented image was selected as an base augmentation model by setting a threshold and then trained. We compared the performance of the original, augmentation model, and image selection model. As a result, the image selection model set to the threshold value of 0.7 in AutoAugment achieved the performance of F1 Score 0.958 |