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Open Access Library Journal

2020, Volume 7, e6296

ISSN Online: 2333-9721

ISSN Print: 2333-9705

DOI:

10.4236/oalib.1106296 May 11, 2020 1 Open Access Library Journal

Deep Learning Convolution Neural Network to

Detect and Classify Tomato Plant Leaf Diseases

Thair A. Salih, Ahmed J. Ali, Mohammed N. Ahmed

Technical Engineering College, Northern Technical University, Mosul, Iraq

Abstract

The tomato crop is an important staple in the market and it is one of the most

common crops daily consumed

. Plant or crop diseases cause reduction of

quality and quantity of the production;

therefore detection and classification

of these diseases are very necessary. There are many types of diseases that in-

fect tomato plant like (bacterial spot, late blight, sartorial leaf spot, to

mato

mosaic and yellow curved). Early detection of plant diseases increases pro-

duction and improves its quality. Currently, intelligent approaches have

been

widely used to detect and classify these diseases. This approach helps the far-

mers to identify the types of diseases that infect crop. The main object of

the

current work is

to apply a modern technique to identify and classify

the disease. Intelligent technique is based on using convolution neural net-

work (CNN) which is a part of machine learning to

obtain an early detection

about the situation of plants. CNN method depends

on feature extraction

(such as color, leaves edge, etc.) from input image and on this basis the deci-

sion of classification is done. A Matlab m-file has been used to build

the CNN

structure. A dataset obtained from plant village has been used for training the

network (CNN). The suggested neural network has been applied to

classify

six types of tomato

leaves situation (one healthy and five types of leave

plant diseases). The results show that the convolution neural

network (CNN)

has achieved a classification accuracy of 96.43%. Real images are used to va-

lidate the ability of

suggested CNN technique for detection and classification,

and obtained using a 5-megapixel camera from a real

farm because most

common diseases which infect the planet are similar.

Subject Areas

Agricultural Science, Artificial Intelligence, Computer Engineering

Keywords

Convolution Neural Network (CNN), Tomato Plant Leaf Diseases, Machine

How to cite this paper:

Salih, T.A.,

Ali,

.J. and Ahmed, M.N. (2020)

Deep Learning

Convolution Neural Network to Detect and

Classify Tomato Plant Leaf Diseases

Open

Access Library Journal

: e6296.

https://doi.org/10.4236/oalib.1106296

Received:

April 2, 2020

Accepted:

May 8, 2020

Published:

May 11, 2020

20 by author(s) and Open

Access Library Inc

This work is licensed under the

Creative

Commons Attribution International

License (CC BY

4.0).

http://creativecommons.org/licenses/by/4.0/

Open Access

T. A. Salih et al.

DOI:

10.4236/oalib.1106296 3 Open Access Library Journal

Prajwala TM

et al.

[4] suggested a method to detect and classify tomato plant

leaf diseases using convolution neural network (CNN) based on using a

pre-trained network model called (LeNet). The achieved accuracy was 94% - 95%.

Santosh Adhikari

et al.

[5] had created a system containing Raspberry Pi mi-

crocontroller (RPM) with a convolution neural network model to detect and

classify tomato plant leaf diseases with 89% accuracy achieved.

H. Sabrol

et al.

[6] proposed approach to identify tomato plant disease by us-

ing Tree classifier model (TCM). Five types of diseases and one healthy were

classified which used 382 images and 97.3% accuracy achieved.

Vetal

et al.

[7] introduced a method to find solution for classifying four types

of diseases using Kurtosis, skewness filters and multi-class support vector ma-

chine (SVM) classifier model with the accuracy of 93.75% achieved.

Ishak

et al.

[8] had discussed approach to analyze the plant leaf quality, the

process started from image acquisition, image processing and classification. Im-

age acquisition was done by using 8-mege pixel smart phone camera, the sam-

ples of images then were divided into fifty for healthy and fifty for unhealthy.

The image processing method consists of three components, contrast enhance-

ment, segmentation and feature extraction. The classification method has been

done using artificial neural network, uses multi-layer feed forward neural net-

work, then comparison between two types of network structures which are Mul-

ti-Layer Perceptron (MLP) and Radial Basis Function (RBF). RBF network per-

formance achieved result better than MLP network. The search classifies the

plant leaf images to only healthy and unhealthy, it can’t detect the type of dis-

ease.

Sabrol

et al.

[9] had discussed approach to identify and classify tomato plant

leaf. CIE XYZ color space analysis, color moment, histogram, and color cohe-

rence are used. The best classification accuracy achieved is 87.2%.

Rangarajan

et al.

[10] proposed a feasible solution to classify tomato crop dis-

eases using tow pre-trained (AlexNet and VGG16 net). The best classification

accuracy achieved with number of image 13,262 using AlexNet and VGG16 net

was 97.49%.

Coulibaly

et al.

[1] implemented a system to detect and diagnose the diseases

that infect millet crops. Their approach was used to extract leaf’s features based

on the transfer learning technique of the CNN model. A pre-trained network

VGG16 model had been used to transfer its learning ability to their suggested

neural network, where the best accuracy achieved 95%.

de Luna

et al.

[11] designed a convolution neural network to detect and clas-

sify tomato plant leaf’s diseases using transfer learning as a training mechanism

with deep learning CNN based Alexnet. This approach was used to classify four

types of tomato plant diseases. A 4932 of images is used, where it is divided into

80% for training and 20% for testing, and the achieved accuracy is 95.75%.

Mortazi

et al.

[12] had built their own net which used to detect and classified

five different types of tomato plant diseases. Their work depends on construct-

ing a network consisting of several layers and requires a short time for training

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