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番茄作物是市场上的重要主食，并且是日常食用的最常见的作物之一。 植物或农作物疾病导致生产质量和数量下降； 因此，对这些疾病的检测和分类非常必要。 感染番茄植物的疾病有很多类型，例如细菌斑，晚疫病，裁缝叶斑，番茄花叶和黄色弯曲。 早期发现植物病害可提高产量并提高其质量。 当前，智能方法已被广泛用于检测和分类这些疾病。 这种方法可以帮助农民识别类型吗？ 感染农作物的疾病 当前工作的主要目的是应用一种现代技术来识别和分类疾病。 智能技术基于使用卷积神经网络（CNN）的技术，而卷积神经网络是机器学习的一部分，可以早期发现有关植物状况的信息。 CNN方法取决于从输入图像中提取特征（例如颜色，叶子边缘等），并在此基础上确定分类。 Matlab m文件已用于构建CNN结构。 从植物村获得的数据集已用于训练网络（CNN）。 所建议的神经网络已被用于分类六种类型的番茄叶片情况（一种健康的叶片植物疾病和五种类型的叶片疾病）。 结果表明，卷积神经网络（CNN）已经实现了96.43％的分类精度。 真实图像用于验证建议的CNN技术进行检测和分类的能力，并使用5兆像素相机从真实农场中获得，因为感染该星球的大多数常
Open Access Library Journal
2020, Volume 7, e6296
ISSN Online: 2333-9721
ISSN Print: 2333-9705
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
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
mosaic and yellow curved). Early detection of plant diseases increases pro-
duction and improves its quality. Currently, intelligent approaches have
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
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
structure. A dataset obtained from plant village has been used for training the
network (CNN). The suggested neural network has been applied to
six types of tomato
leaves situation (one healthy and five types of leave
plant diseases). The results show that the convolution neural
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.
Agricultural Science, Artificial Intelligence, Computer Engineering
Convolution Neural Network (CNN), Tomato Plant Leaf Diseases, Machine
How to cite this paper:
.J. and Ahmed, M.N. (2020)
Convolution Neural Network to Detect and
Classify Tomato Plant Leaf Diseases
Access Library Journal
April 2, 2020
May 8, 2020
May 11, 2020
Copyright © 20
20 by author(s) and Open
Access Library Inc
This work is licensed under the
Commons Attribution International
License (CC BY
T. A. Salih et al.
10.4236/oalib.1106296 2 Open Access Library Journal
Learning, Early Detection
Agriculture has a major impact on the nation’s economy, in addition to being
the backbone of people’s lives. The tomato crop is one of the most important
plants, and it directly affects human life. Recently, plant diseases (such as bacte-
ria, late blight, leaf-leaf spot, tomato mosaic, and yellow curved) are wide spread
and badly affecting plant growth and causing reduction of quality and quantity
of the production . 80% - 90% of plant diseases occurred on the leaves .
The process of monitoring the farm and identifying the different types of diseas-
es that affected plants due to the farms is time consuming and requires a long
time. In addition, the determination of the type of plant disease by farmers may
be inaccurate, and as a result of this decision, the protection mechanisms
adopted may be ineffective and sometimes harmful to the plant. It is important
to find a smart technology that aims to detect and classify diseases that affect
tomato plants with high accuracy.
Deep Learning Neural Network (DLCNN) technology is widely used to detect
and classify plant leaf diseases as it achieves high-resolution. The general form of
this technique is applied to the tomato plant,
Figure 1(a), Figure 1(b). It shows
infected and healthy types of tomato plant diseases.
The tomato plant diseases became a domain interesting for many researchers
due to both wide-spread and manufacturing important requirements.
 discussed how to identify tomato leaf disease by using
deep learning convolution neural network (CNN). The paper had used many
pre-trained networks such as (AlexNet, googleNet and ResNet) with the accura-
cy of 97.19%.
Figure 1. (a) Types tomato plant leaf diseases; (b) Healthy tomato plant leaf.
T. A. Salih et al.
10.4236/oalib.1106296 3 Open Access Library Journal
 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%.
 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.
 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.
 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.
 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-
 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%.
 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
 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%.
 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%.
 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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