Tomato Leaf Disease Detection Using Convolution Neural Network

被引：23

作者：

Kibriya, Hareem ^{[1
]}

Rafique, Rimsha ^{[1
]}

Ahmad, Wakeel ^{[1
]}

Adnan, S. M. ^{[1
]}

机构：

[1] Univ Engn & Technol Taxila, Dept Comp Sci, Taxila, Pakistan

来源：

PROCEEDINGS OF 2021 INTERNATIONAL BHURBAN CONFERENCE ON APPLIED SCIENCES AND TECHNOLOGIES (IBCAST) | 2021年

关键词：

Tomato Disease; Deep Features; Image Processing; Late and Early Blight; Bacterial Spot;

D O I：

10.1109/IBCAST51254.2021.9393311

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The quality and quantity of the crop are significantly affected by numerous diseases in plants. In this regard, an early detection of such diseases is highly effective. Tomato is one of the important crops that is produced in large quantities with high commercial value. Several types of tomato diseases affect the crop at an alarming rate. In this paper, we deployed two Convolution Neural Network (CNN) based models i.e. GoogLeNet and VGG16 for tomato leaf disease classification. The proposed work aims to find the best solution to the problem of tomato leaf disease detection using a deep learning approach. VGG16 obtained 98% accuracy while GoogLeNet obtained 99.23% on Plant Village dataset containing 10735 leaf images. The proposed system can be used in tomato fields for early detection of disease to avoid production loss.

引用

页码：346 / 351

页数：6

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