Classification of tomato leaf diseases using MobileNet v2

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 pl...

Full description

Saved in:
Bibliographic Details
Published in:IAES International Journal of Artificial Intelligence Vol. 9; no. 2; p. 290
Main Authors: Zaki, Siti Zulaikha Muhammad, Asyraf Zulkifley, Mohd, Mohd Stofa, Marzuraikah, Kamari, Nor Azwan Mohammed, Ayuni Mohamed, Nur
Format: Journal Article
Language:English
Published: Yogyakarta IAES Institute of Advanced Engineering and Science 01.06.2020
Subjects:
Algorithms
Blight
Computer vision
Deep learning
Image detection
Leaf mold
Machine learning
Medical imaging
Plant diseases
Plant management
Tomatoes
Viral diseases
Viruses
ISSN:2089-4872, 2252-8938, 2089-4872
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2089-4872
2252-8938
2089-4872
DOI:10.11591/ijai.v9.i2.pp290-296