Automatic Recognition of Tea Diseases Based on Deep Learning

*Jing Chen and Junying Jia*

### **Abstract**

With the rapid development of intelligent agriculture and precision agriculture, computer image processing technology has been widely used to solve various problems in the agricultural field. In particular, the advantages of convolutional neural networks (CNNs) in image classification have also been widely used in the automatic recognition and classification of plant diseases. In this paper, a deep convolutional neural network named LeafNet capable of recognizing the seven types of diseases from tea leaf disease images was established, with an accuracy of up to 90.23%, aiming to provide timely and accurate diagnostic services in the remote and topographic tea plantation in China. At the same time, the traditional machine learning algorithm is applied for comparative analysis, which extracts the dense scale-invariant feature transform (DSIFT) of the image and constructs the bag of visual word (BOVW) model to express the image based on the DSIFT descriptor. The support vector machines (SVMs) and multilayer perceptron (MLP) were used to identify tea leaf diseases, with an accuracy of 60.91 and 70.94%, respectively.

**Keywords:** tea leaf disease, deep learning, convolutional neural network, dense SIFT, bag of visual word

#### **1. Introduction**

Tea has a long history of cultivation in China, and the tea planting area and yield rank first in the world. According to statistical data, in 2016 China's 17 provinces had a total of 2.87 million hectares of tea plantation and production, and the total output of tea reached 2.4 billion tons [1]. As the main tea-producing areas in China are mainly distributed in subtropical regions, the natural environment differs due to geographical latitude and topographical conditions. The tea tree is a perennial evergreen woody plant, which grows in warm and humid growth environment. However, these regions are conducive to the breeding and reproduction of diseases. In recent years, the tea planting area has increased year by year, and the tea leaf diseases have risen continuously, which has seriously threatened the quality and yield of tea. Because the distribution of tea areas in China is mostly in high mountain areas, the infrastructure construction in these areas is relatively lagging behind, and the occurrence of tea leaf diseases is often not controlled in a timely and effective manner, resulting in huge economic losses. Therefore, being able to detect and identify diseases early in the field is an important task to ensure the sustainable development of the tea industry.

The diagnosis of plant diseases is usually based on the appearance of the disease. When the leaves of a plant are infected by a disease, the appearance of the leaves will change significantly. Each disease usually has a discernible leaf color and texture symptom, and plant diseases can be diagnosed based on these characteristics. However, farmers mainly rely on their own experience to diagnose plant diseases with their own senses. Due to the limitation of knowledge background, there are ambiguities in the diagnosis. Most tea trees in China are planted in mountainous areas, which are large, difficult to investigate in the field, and inefficient. Relying on agricultural experts to diagnose tea leaf diseases is not only time-consuming but also costly. The transportation and infrastructure conditions in these places are limited. Finally, the expert must have experience and knowledge in various disciplines and need to understand all the symptoms of the disease and the causes of the diversity of the disease. At the same time, because China's agricultural population is relatively large and the number of experts engaged in agricultural services is extremely limited, it is necessary to establish a system that can diagnose tea leaf diseases in a timely and accurate manner.

The concept of deep learning was first mentioned by Professor Geoffrey Hinton of the University of Toronto in a paper on back-propagation algorithms. The concept of "depth" was used to represent large artificial neural networks. With the introduction of deep learning, more and more researchers have begun to develop large-scale neural network systems. These deep neural network systems can take the characteristics from the original data, can work alone without human manipu-

The advantage of the deep learning is that it does not require artificial feature extraction but this is obtained automatically by the network. It can solve nonlinear separable problems and has strong generalization ability and robustness. Among them, the most widely used is the convolutional neural network, which is a deep neural network. Images can be directly used as input data, eliminating the complicated process of feature extraction and data reconstruction in traditional machine learning algorithms. At the same time, the multilayer network structure of the convolutional neural network maintains a high degree of invariance to image translation, scaling, or lighting changes [18]. At present, convolutional neural networks have been applied to the identification and diagnosis of plant diseases [24–26]. In recent years, many researchers in the world have used machine learning algorithms to build many disease recognition systems, but because the characteristics of each plant disease are different, the different machine learning methods will have different recognition effects. Hence, based on previous studies, this paper uses deep convolutional neural networks to identify and classify tea leaf diseases. At the same time, the traditional machine learning algorithm is compared with the proposed convolutional neural network, and a recognition system suitable for the tea

The existing databases on the network such as ImageNet, PlantVillage, and CIFAR-1 datasets do not have sufficient tea leaf disease images and some studies

*Typical example images of tea leaf diseases used in this manuscript. (1) Red leaf spot (*Phyllosticta theicola Petch*). (2) Algal leaf spot (*Cephaleuros virescens Kunze*). (3) Bird's-eye spot (*Cercospora theae Bredde Haan*). (4) Gray blight (*Pestalotiopsis theae Steyaert*). (5) White spot (*Phyllosticta theaefolia Hara*). (6) Anthracnose*

*(*Gloeosporium theae-sinensis Miyake*). (7) Brown blight (*Colletotrichum camelliae Massee*).*

lation, and then can use what humans have learned to learn new things.

*Automatic Recognition of Tea Diseases Based on Deep Learning*

*DOI: http://dx.doi.org/10.5772/intechopen.91953*

leaf disease is found through comparative analysis.

**2. Date acquisition**

**Figure 1.**

**141**

The current diagnostic methods of plant diseases mainly include microscope identification, molecular biology technology, and spectroscopic technology, but the first method is time-consuming and subjective. Even experienced plant pathologists may have wrong judgments, leading to inaccurate conclusion. The latter two methods are currently considered more accurate, and their main disadvantages are the high labor intensity and the requirement of specific instruments.

With the rapid development of intelligent agriculture and precision agriculture, machine learning methods and computer image processing technologies have been applied to the identification of plant diseases [2, 3], providing a new method for detecting plant diseases, which can help farmers and researchers quickly and accurately identify the types of plant diseases. The general approach based on machine learning and computer image processing technology is first to manually design and extract disease image features, namely, global features, such as color features [4], shape features [5], texture features [6], or two or more than three features [7–11], and local features, using scale-invariant feature transform (SIFT), speeded-up robust features (SURF), dense scale-invariant feature transform (dense SIFT), and pyramid histograms of visual words (PHOW) [12–14]. After extracting the features, they are identified and classified using different classifiers, such as artificial neural networks [15, 16] and support vector machines [17, 18]. Because traditional machine learning relies on features extracted manually, the resulting recognition system is not fully automated.

At present, most of the researches on tea using computer vision technology focus on tea quality detection [19], tea species identification [20], and tea leaf disease information query and management based on expert systems [21]. Because the expert system has limited knowledge and needs to be updated and maintained on a regular basis, it is also limited for noncomputer professional technicians. For some literatures, the identification of tea diseases is based on hyperspectral [22] or infrared thermal images [1]. These methods are easy to operate and have high accuracy, but the cost of the instrument is not suitable for widespread promotion.

In recent years, the popularity of the Internet has led to the explosive growth of Internet data, and the technical performance of computers and smartphones has continued to improve. These factors are the main reasons that have led to widespread attention for deep learning. Deep learning refers to the process of learning sample data through a certain training method to obtain a deep network structure containing multiple levels [23]. Deep learning is a branch of machine learning. Its essence is also a neural network, but the number of hidden layers is more than one layer, which is an extension of artificial neural networks. "Neural network" is a component of deep learning.

#### *Automatic Recognition of Tea Diseases Based on Deep Learning DOI: http://dx.doi.org/10.5772/intechopen.91953*

The concept of deep learning was first mentioned by Professor Geoffrey Hinton of the University of Toronto in a paper on back-propagation algorithms. The concept of "depth" was used to represent large artificial neural networks. With the introduction of deep learning, more and more researchers have begun to develop large-scale neural network systems. These deep neural network systems can take the characteristics from the original data, can work alone without human manipulation, and then can use what humans have learned to learn new things.

The advantage of the deep learning is that it does not require artificial feature extraction but this is obtained automatically by the network. It can solve nonlinear separable problems and has strong generalization ability and robustness. Among them, the most widely used is the convolutional neural network, which is a deep neural network. Images can be directly used as input data, eliminating the complicated process of feature extraction and data reconstruction in traditional machine learning algorithms. At the same time, the multilayer network structure of the convolutional neural network maintains a high degree of invariance to image translation, scaling, or lighting changes [18]. At present, convolutional neural networks have been applied to the identification and diagnosis of plant diseases [24–26].

In recent years, many researchers in the world have used machine learning algorithms to build many disease recognition systems, but because the characteristics of each plant disease are different, the different machine learning methods will have different recognition effects. Hence, based on previous studies, this paper uses deep convolutional neural networks to identify and classify tea leaf diseases. At the same time, the traditional machine learning algorithm is compared with the proposed convolutional neural network, and a recognition system suitable for the tea leaf disease is found through comparative analysis.
