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Image Classification of Tomato Leaf Diseases using Convolutional Neural Network |
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| รหัสดีโอไอ | |
| Creator | Nutchanun Chinpanthana |
| Title | Image Classification of Tomato Leaf Diseases using Convolutional Neural Network |
| Publisher | The Association of Council of IT Deans (CITT) |
| Publication Year | 2566 |
| Journal Title | Journal of Information Science and Technology |
| Journal Vol. | 13 |
| Journal No. | 2 |
| Page no. | 40-49 |
| Keyword | Image Classification, Tomato Leaf Diseases, Deep Learning, Convolutional Neural Networks, Image Processing |
| URL Website | https://tci-thaijo.org/index.php/JIST |
| Website title | Journal of Information Science and Technology |
| ISSN | 2651-1053 |
| Abstract | Tomato is one of the most important cultivated vegetable plants in the world. The continuous expanded production and consumption of tomato has grown quickly. It is considered a mainstay of many economic country. Tomato crops can be damage due to various kinds of diseases that are recently discovery of diagnosis errors or not prevented and controlled timely. The problems faced by farmers are typically unnoticed and lack knowledge in crop production. For developing an early treatment process, the identify infections of plant diseases in a rapid can help to reduce huge economical suffering. In agricultural practices is detect of disease manually on crops which is very complex, time-consuming and more tedious tasks. This paper discussing the technique base on digital image processing, which employs the convolutional neural network deep learning model to classify tomato leaf diseases. The dataset is classified into 10 classes: bacterial leaf spot, early blight, late blight, leaf mold, septoria leaf spot, two-spotted spider mite, target spot, cucumber mosaic virus, yellow leaf curl virus and fresh leaf. The approach is composed of four main phases: (1) data preprocessing, (2) generated model convolutional neural network, and (3) model evaluation and (4) deployment. The experimental results indicated that deep learning with convolutional neural network technique has the highest effectiveness in recognizing tomato leaf diseases with the total average accuracy at 87.96% at learning rate 0.001 for 100 epochs. |
