| Title |
A Study of Tomato Disease Classification System Based on Deep Learning |
| Authors |
함현식(Hyun-sik Ham) ; 김동현(Dong-hyun Kim) ; 채정우(Jung-woo Chae) ; 이신애(Sin-ae Lee) ; 김윤지(Yun-ji Kim) ; 조현욱(Hyun Uk Cho) ; 조현종(Hyun-chong Cho) |
| DOI |
https://doi.org/10.5370/KIEE.2020.69.2.349 |
| Keywords |
Deep learning; Inception V3; Plant disease; Tomato |
| Abstract |
The early detection of plant disease is important in that it enhances the quality and productivity of crops. A large amount of research has considered machine learning classifiers to protect tomato plants from diseases, but the reliability of early disease diagnoses in this way remains uncertain due to the use of small datasets. Therefore, to enhance the dependability of them, this study examined a tomato disease classification system based on a deep learning using a dataset containing 17,063 images of tomato leaves infected with eight diseases. The deep learning model used in this classifier consisted of symmetric and asymmetric building blocks including convolutions, average pooling, max pooling, concats, dropouts, and fully connected layers. The obtained result indicated a high degree of accuracy (98.9%) which is high enough to be used as a proper diagnosis tool for farmers who lack professional knowledge of tomato diseases. |