DOI : https://doi.org/10.18517/ijaseit.10.5.11665

Classification of Tomato Plants Diseases Using Convolutional Neural Network

I Ketut Gede Darma Putra (1), Rahmat Fauzi (2), Deden Witarsyah (3), I Putu Deva Jayantha Putra (4)
(1) Department of Information Technology, Faculty Engineering, Udayana University, Bali, 80351, Indonesia
(2) Department of Information System, Faculty of Industrial Engineering, Telkom University, Bandung, 40257, Indonesia
(3) Department of Information System, Faculty of Industrial Engineering, Telkom University, Bandung, 40257, Indonesia
(4) Department of Information Technology, Faculty Engineering, Udayana University, Bali, 80351, Indonesia
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How to cite (IJASEIT) :
Darma Putra, I Ketut Gede, et al. “Classification of Tomato Plants Diseases Using Convolutional Neural Network”. International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 5, Oct. 2020, pp. 1821-7, doi:10.18517/ijaseit.10.5.11665.
Citation Format :
Tomato plants are many cultivated by farmers to get their fruit. Several obstacles in the cultivation process result in the process of producing products that require maximization. Constraints faced by farmers are diseases that attack plants. Farmers in dealing with the disease simply recognize the disease with their naked eyes and take action without knowing how to deal with it. Several approaches have been made in the recognition process that can be handled by using deep learning. The results shown using this process produce a good performance. Therefore, this paper has the aim of making and evaluating recognition of the diseases in plants seen on tomato leaves automatically using a deep learning approach. Convolutional Neural Network (CNN) is one of the deep learning methods used in handling object recognition processes. Recognition process tomato plants disease used a data set consisting of 6 different types of disease. Recognition process used 100 image data for each type of disease as training data, while as many as 60 image data are used as testing. This study used three different types of data sets that are used differently, consisting of original image RGB, blending images, and a mixture of RGB images and blending images. The classification results using a mixture of RGB images and blending images have a better performance than others by producing a Genuine Acceptance Rate (GAR) of 96.7% following by the percentage of False Acceptance Rate (FAR) values of 3.3% and False Rejection Rate (FRR) of 3.3%.

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