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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Marzuraikah Mohd Stofa
Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia
Computer Science & IT Engineering

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Classification of tomato leaf diseases using MobileNet v2 Siti Zulaikha Muhammad Zaki; Mohd Asyraf Zulkifley; Marzuraikah Mohd Stofa; Nor Azwan Mohammed Kamari; Nur Ayuni Mohamed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (538.914 KB) | DOI: 10.11591/ijai.v9.i2.pp290-296

Abstract

Tomato is a red-colored edible fruit originated from the American continent. There are a lot of plant diseases associated with tomatoes such as leaf mold, late blight, and mosaic virus. Tomato is an important vegetable crop that contributes to the world economically. Despite tremendous efforts in plant management, viral diseases are notoriously difficult to control and eradicate completely. Thus, accurate and faster detection of plant diseases is needed to mitigate the problem at the early stage. A computer vision approach is proposed to identify the disease by capturing the leaf images and detect the possibility of the diseases. A deep learning classifier is utilized to make a robust decision that covers a wide variety of leaf appearances. Compact deep learning architecture, which is MobileNet V2 has been fine-tuned to detect three types of tomato diseases. The algorithm is tested on 4,671 images from PlantVillage dataset. The results show that MobileNet V2 is able to detect the disease up to more than 90% accuracy.
Co-Authors Mohd Asyraf Zulkifley Nor Azwan Mohammed Kamari Nur Ayuni Mohamed Siti Zulaikha Muhammad Zaki