Tea leaf disease dataset in this manuscript.
\r\n\tThe purpose of the book is to bring together the latest knowledge about genetic diversity by presenting the studies of some of the scientists who are engaged in development of new tools and ideas used to reveal genetic diversity, often from very different perspectives. The book should prove useful to students, researchers and experts in the area of biology, medicine and agriculture.
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He obtained a BSc from Middle East Technical University, Ankara, and a Ph.D. from the University of Leeds, England. His main research areas include the role of germin gene products during early plant development, analysis of genetic variation, polymorphisms, and the characterization and biotechnological use of halophilic archaea.",institutionString:"Istanbul University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"8",institution:{name:"Istanbul University",institutionURL:null,country:{name:"Turkey"}}}],coeditorOne:{id:"462767",title:"Dr.",name:"Sevcan",middleName:null,surname:"Aydin",slug:"sevcan-aydin",fullName:"Sevcan Aydin",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003QRfRpQAL/Profile_Picture_2022-03-24T08:49:06.jpg",biography:"Sevcan Aydın is an Associate Professor of Biotechnology Division in Department of Biology at Istanbul University in Türkiye. 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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 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, 8, 9, 10, 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, 13, 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.
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, 25, 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.
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 have collected disease photos in indoor or controlled environments. These factors have made the recognition system designed to identify diseases under natural light conditions to have certain limitations, so a new disease data set is constructed in this paper.
Tea leaf disease images were all captured using the Canon PowerShot G12 camera in the natural light environment of the tea garden in Chibi and Yichang within Hubei Province. The images were taken about 20 cm directly above the leaves with autofocus mode at resolution of 4000 × 3000 pixels. A total of 3810 disease images were collected, which contained 7 diseases, and all disease images have been identified by plant pathologists. The identification criteria used for the tea leaf diseases were based on the previously described identification schemes [27, 28]. In order to meet the requirements of the model algorithm and reduce the computational complexity of the network, all disease images are resized to 256 × 256 pixels and 750 × 750 pixels, respectively. Figure 1 shows the types of tea leaf diseases used in this experiment. Data amplification processing is performed on a smaller number of disease images so that the number of the seven diseases image is balanced. Data amplification processing improves the generalization ability of the classifier, which is more conducive to network training. Three different methods were used to alter the image input and improve classification (Figure 2). A total of 7905 tea leaf disease images were obtained after the amplification treatment (Table 1). The 80/20 ratio of training/test data is the most commonly used ratio in neural network applications. In addition, a 10% subset of the test dataset was used to validate the dataset [29].
Typical example images of tea leaf diseases used in this manuscript. (1) Red leaf spot (
Examples of data augmentation used for red leaf spot images. (a) Initial; (b) flip horizontal; (c) flip vertical; (d) rotated 180°; (e–g) randomly cropped; (h) right-rotate 90°; (i) left-rotate 90°.
Class | Training | Validation | Testing |
---|---|---|---|
(1) White spot | 941 | 118 | 117 |
(2) Bird’s-eye spot | 955 | 120 | 119 |
(3) Red leaf spot | 890 | 111 | 111 |
(4) Gray blight | 893 | 112 | 111 |
(5) Anthracnose | 880 | 110 | 110 |
(6) Brown blight | 920 | 115 | 115 |
(7) Algal leaf spot | 846 | 106 | 105 |
Total | 6325 | 792 | 788 |
Tea leaf disease dataset in this manuscript.
Traditional machine learning algorithm is a shallow architecture that contains one or two nonlinear transformation layers. It can automatically learn the underlying laws in the data and use the learned rules to make predictions. In the field of computer vision, many models can be realized by manually designing and extracting the visual characteristics of the image in advance, and the image content is converted into a quantitatively calculated information description form, after being processed by the shallow structure model.
The extraction and selection of image visual features is an important means to transform the image content into a quantitatively calculated information description form, which mainly include global features and local features. Global features refer to the overall attributes of the entire image, mainly including color features, texture features, and shape features. These features are features that can be directly observed by the eyes. Global features are pixel-level shallow features with good stability, real-time performance, and simple and easy-to-implement algorithms. However, their shortcomings are high feature dimensions, large amount of calculations, and changes in image scale, lighting, and perspective. Local features are features extracted from local areas of the image, including corners, lines, edges, and areas with special attributes. Local features are distinguishable and robust to changes in lighting, rotation, perspective, and scale, as well as low dimensions and easy implementation.
The scale-invariant feature transform (SIFT) is local feature descriptor proposed by David G. Lowe in 1999 [30]. The SIFT descriptor maintains invariance to image rotation, translation, scaling, affine transformation, perspective and brightness changes, and noise and also maintains stability. And it can be combined with other algorithms to form a new optimization algorithm, thereby increasing the operation speed.
The traditional SIFT descriptor mainly extracts stable feature points in the image, which will lead to loss of some information in the image and long calculation time. And the number of feature points extracted from each image is different, which will inevitably lead to different dimensions. Lazebnik et al. improved the number and distribution of SIFT descriptors to obtain dense SIFT [31]. The main difference between the dense SIFT descriptor and the traditional SIFT descriptor is that the sampling method is different. The SIFT descriptor constructs a scale space to detect and filter feature points. The dense SIFT algorithm applies a fixed-size rectangular window for sampling from the left to the right of the image and from the top to the bottom according to the specified step size. The center of the window is used as a key point, and an image block composed of 16 pixels around the center is divided into 4 × 4 pixel-sized units. Within each pixel, the SIFT algorithm is used to calculate the gradient histogram in 8 directions and obtain 4 × 4 × 8 = 128 dimensional feature vectors to form a DSIFT descriptor. The feature points extracted by this method are uniformly distributed, and the specifications are the same; they maintain good stability to illumination, changes in perspective, and affine transformation, scaling, and rotation.
Bag of visual word (BOVW) model was mainly applied to text classification and retrieval technology. The core idea of the bag of visual word model is to treat text as a collection of different words, ignoring the word order, grammar, and syntax of the text, and these words are discrete and independent of each other or do not depend on the presence of other words. The frequency of each word in the text is counted and is represented with histogram so that each text is represented as a vector.
Due to the successful application of the BOVW model in text retrieval, Csurka et al. introduced the BOVW model to the field of computer vision [32]. Think of an image as a document and the features of the image (usually referred to as local features) as the words that make up the image. Unlike the words in the text, there are no ready-made words in the image. We need to extract independent features from the image, which are called visual word. Similar features can be regarded as a visual word. In this way, the image can be described as an unordered set of visual words (local features). Although local features (such as SIFT) also can describe an image, each SIFT is a 128-dimensional vector, and an image contains hundreds or thousands of SIFT descriptor. The calculation amount is very large, so these vectors are clustered, and the cluster center was used to represent a visual word.
The image classification using BOVW model mainly includes the following steps:
Image feature extraction and description: Local feature vectors of the entire training set image are obtained through methods such as point-of-interest detection, dense sampling, or random sampling. Commonly used local features include SIFT descriptor and SURF descriptor.
Construct a visual vocabulary: After obtaining the local feature vectors of all sample images, use the k-means algorithm to cluster the local feature vectors. The k-means algorithm is an unsupervised learning algorithm. It divides the data into different categories through an iterative process and then calculates the Euclidean distance between each data and various types of centers [33]. The smaller the distance, the higher the similarity. k represents the number of clusters, and means represents the mean of the data in the clusters. If there are k cluster centers (i.e., visual words), then the size of the visual vocabulary is also k. This manuscript selects 1000 visual words, and the size of the visual vocabulary is 1000.
Representing images by word frequency: using the vocabulary as a standard, count the number of occurrences of each visual word in the image, and each image becomes a word frequency vector corresponding to the visual word sequence in the vocabulary, that is, each image is represented by a 1000-dimensional numerical vector.
Select classifier to classify the 1000-dimensional numerical vector generated in the previous step as the input of the classification.
Support vector machines (SVMs) were proposed by Corinna Cortes and Vapnik in 1995 [34]. It is a learning method based on VC statistical theory and structural risk minimization criteria. It has advantage in solving small sample, nonlinear, and high-dimensional pattern recognition problems. The basic idea of the SVMs is to map the low-dimensional space vector to the high-dimensional space through the nonlinear transformation defined by the inner product. In this high-dimensional space, the optimal classification hyperplane is determined according to the maximum geometric distance between the support vector and the classification plane. SVMs were initially used to classify two-class problems in the analysis of linear separable cases and require smaller sample sizes and an appropriate train rule, which have led to widespread use in image classification and recognition.
With the deepening of research on support vector machines, many scholars have carried out various toolkits in order to make them suitable for specific fields. In this manuscript a linear classifier LIBLINEAR designed by Professor Lin Zhiren of the National Taiwan University is used, mainly for processing large-scale data and features [35]. LIBLINEAR can be used in the following three cases: when the number of features is much larger than the number of samples; when the number of features and samples is large; and when the number of features is much smaller than the number of samples. Because the complexity of the linear classifier is lower than the nonlinear classifier, the training operation time is greatly reduced, and the training performance of the linear and nonlinear classifiers is also comparable under a large amount of data.
The perceptron was proposed by Rosenblatt in 1958 [36]. It is an artificial neural network structure and the earliest feed-forward neural network. A single-layer perceptron contains only two layers, namely, the input layer and the output layer. Due to its limited mapping capability, it can only achieve linearly separable classification problems. A multi-layer perceptron has one or more hidden layers between the input layer and the output layer, which is mainly used for nonlinear classification and regression. The training algorithm is consistent with the traditional multilayer neural network and also uses a back-propagation algorithm.
Perceptron in this manuscript uses a three-layer structure. Because the extracted features are 1000-dimensional vectors, the input layer contains 1000 nodes, the hidden layer contains 100 nodes, and the output layer contains 7 nodes, which refer to the number of types of tea leaf disease.
The network architecture designed in this manuscript was improved based on the classic model AlexNet model, named as LeafNet. The total number of parameters (weights and deviations) of the classic AlexNet network reaches more than 60 million, the parameters of the convolution layer comprises 3.8% of the total network parameters, and the parameters of the fully connected layer comprises 96.2% of the total. Therefore, by reducing the number of LeafNet’s convolutional layer filters and the number of fully connected layer nodes, the total number of network parameters is reduced, and the computational complexity is reduced. The recognition model has a relatively simple structure and a small amount of calculation, which effectively reduces the problem of overfitting.
LeafNet consists of five convolutional layers, two fully connected layers, and a classification layer. The number of filters for the first, second, and fifth convolutional layers is half of those used in AlexNet’s filters. In addition, the number of neurons in the fully connected layer is set to 500, 100, and 7, respectively. The entire network structure is shown in in Table 2.
Layer | Parameters | Activity function |
---|---|---|
Input | 227 × 227 × 3 | − |
Convolution1(Conv1) | 24 convolution filters (11 × 11) 4 stride | ReLU |
Pooling1(Pool1) | Max pooling (3 × 3) 2 stride | − |
Convolution2(Conv2) | 64 convolution filters (5 × 5) 1 stride | ReLU |
Pooling2(Pool2) | Max pooling (3 × 3) 2 stride | − |
Convolution3(Conv3) | 96 convolution filters (3 × 3) 1 stride | ReLU |
Convolution4(Conv4) | 96 convolution filters (3 × 3) 1 stride | ReLU |
Convolution5(Conv5) | 64 convolution filters (3 × 3) 1stride | ReLU |
Pooling5(Pool5) | Max pooling (3 × 3) 2 stride | − |
Full Connect 6(fc6) | 500 nodes 1 stride | ReLU |
Full Connect 7(fc7) | 100 nodes 1 stride | ReLU |
Full Connect8(fc8) | 7 nodes 1 stride | ReLU |
Output | 1 node | Softmax |
Layer parameters for the LeafNet.
In this experiment, except for the last layer, the rectified linear unit (ReLU) activation function is selected instead of the traditional sigmoid and tanh functions. The main disadvantages of the sigmoid and tanh functions are the large amount of calculations, and when the input is large or small, the output is relatively smooth, the gradient is small, and it is not conducive to the weight update, which ultimately cause the network to fail to complete the training. ReLU is more in line with the principle of neuron signal excitation. It will make some neurons’ output 0, making the network sparse and reducing the interdependence of parameters, effectively alleviating overfitting. At the same time, ReLU has better transmission error characteristics and solves the problem of gradient disappearance, so it makes the training network converge faster.
After the nonlinear neuron output of the first two convolutional layers, a local response normalization operation is introduced. It is a normalization operation and mimics the lateral inhibition phenomenon of neurobiology. Local response normalization creates a competition mechanism for the output of local neurons. Local response normalization creates a competition mechanism for the output of local neurons, making the neurons with large responses larger, thereby enhancing the generalization ability of the model.
The first two fully connected layers have introduced the dropout operation. The dropout technique is an effective solution to overfitting via the training of only some of the randomly selected nodes rather than the entire network [37]. In this article, the dropout ratio is set to 0.5.
Softmax is the activation function of the last fully connected layer, which is mainly used in the output layer of multi-classification problems. It can make the sum of all output values equal to 1. That is, the output value of multiple classifications is converted into a relative probability, in which the category which has a high relative probability is the predicted value.
LeafNet’s training uses stochastic gradient descent (SGD) technique. The weight values of all convolutional layers and fully connected layers are initialized with a Gaussian distribution, and the bias is initialized with a constant of 1. This setting guarantees that the input of the ReLU activation function is a positive number and can also speed up the training speed of the network [25]. Because the number of samples is small, the batch size is set to 16. Batch training can improve the convergence speed of the network and keep the memory usage at a low level. The initial learning rate of all layers of the network is set to 0.1. The learning rate is reduced according to the decline of the error, and each time it is reduced to 0.1 times the original learning rate in subsequent iterations, with the minimum threshold of the learning rate set to 0.0001. The number of epochs was set as 100, while the weight of decay was set to 0.0005 and the momentum was set to 0.9 [38]. LeafNet is implemented using Matlab’s MatConvNet toolbox. The network training is performed on a Windows system, configured with a Core i7-3770K CPU, 8 GB of RAM, and accelerated training via two NVIDIA GeForce GTX 980 GPUs.
As mentioned in [39], the classification accuracy and mean class accuracy (MCA) are used to evaluate the performance of the algorithm. CCRk is first defined as the correct classification rate for class k, as shown in Eq. (1):
Where Ck is the number of correctly identified for class k and Nk is the total number of elements in class k. Classification accuracy is then defined by Eq. (2):
Lastly, MCA is determined using Eq. (3):
In this study, the accuracy of the SVM, MLP, and CNN classifiers in determining disease states for tea leaves from images was evaluated. The results of these analyses are shown in Figure 3. Error matrices were used to evaluate the accuracy of tea leaf disease recognition classifiers (Tables 3–5). From these data, although LeafNet algorithms are significantly better than SVM and MLP algorithms, three recognition algorithms can usually correctly identify most tea leaf diseases. Traditional machine learning algorithms extract the surface features of images, and the number is limited. The ability to represent image features is not strong, resulting in a low accuracy rate for identifying diseases. However, the CNN can automatically extract the deep features of the image, which can more accurately express the features of the disease image, so its recognition accuracy is higher.
Accuracy (%) of disease classification for each of the three classification models in recognizing the seven candidate tea diseases.
White spot | Bird’s-eye spot | Red leaf spot | Gray blight | Anthracnose | Brown blight | Algal leaf spot | Sensitivity | Accuracy | MCA | |
---|---|---|---|---|---|---|---|---|---|---|
White spot | 111 | 3 | 0 | 0 | 3 | 0 | 0 | 94.87% | 90.23% | 90.16% |
Bird’s-eye spot | 1 | 117 | 0 | 0 | 0 | 0 | 1 | 98.32% | ||
Red leaf spot | 0 | 0 | 95 | 7 | 0 | 8 | 1 | 85.59% | ||
Gray blight | 0 | 0 | 4 | 96 | 3 | 7 | 1 | 86.49% | ||
Anthracnose | 5 | 0 | 1 | 6 | 97 | 1 | 0 | 88.18% | ||
Brown blight | 0 | 1 | 15 | 2 | 0 | 97 | 0 | 84.35% | ||
Algal leaf spot | 1 | 1 | 2 | 2 | 1 | 0 | 98 | 93.33% |
Error matrix showing the classification accuracy of the LeafNet algorithm.
White spot | Bird’s-eye spot | Red leaf spot | Gray blight | Anthracnose | Brown blight | Algal leaf spot | Sensitivity | Accuracy | MCA | |
---|---|---|---|---|---|---|---|---|---|---|
White spot | 79 | 11 | 0 | 2 | 19 | 1 | 5 | 67.52% | 60.91% | 60.62% |
Bird’s-eye spot | 12 | 89 | 0 | 4 | 1 | 10 | 3 | 74.79% | ||
Red leaf spot | 2 | 4 | 59 | 23 | 2 | 19 | 2 | 53.15% | ||
Gray blight | 0 | 0 | 13 | 70 | 8 | 17 | 3 | 63.06% | ||
Anthracnose | 19 | 0 | 5 | 13 | 56 | 11 | 6 | 50.91% | ||
Brown blight | 0 | 2 | 19 | 17 | 3 | 73 | 1 | 63.48% | ||
Algal leaf spot | 9 | 10 | 12 | 13 | 3 | 4 | 54 | 51.43% |
Error matrix showing the classification accuracy of the SVM algorithm.
White spot | Bird’s-eye spot | Red leaf spot | Gray blight | Anthracnose | Brown blight | Algal leaf Spot | Sensitivity | Accuracy | MCA | |
---|---|---|---|---|---|---|---|---|---|---|
White spot | 83 | 13 | 0 | 3 | 15 | 1 | 2 | 70.94% | 70.94% | 70.77% |
Bird’s-eye spot | 6 | 100 | 0 | 6 | 1 | 5 | 1 | 84.03% | ||
Red leaf spot | 1 | 1 | 80 | 17 | 0 | 11 | 1 | 72.07% | ||
Gray blight | 0 | 0 | 9 | 81 | 6 | 14 | 1 | 72.97% | ||
Anthracnose | 13 | 0 | 4 | 10 | 73 | 8 | 2 | 66.36% | ||
Brown blight | 0 | 5 | 16 | 15 | 3 | 75 | 1 | 65.22% | ||
Algal leaf spot | 6 | 5 | 9 | 10 | 4 | 4 | 67 | 63.81% |
Error matrix showing the classification accuracy of the MLP algorithm.
It can be seen from the error matrix that the recognition accuracy of MLP and SVM for the seven tea leaf diseases is 70.94% and 60.91%, respectively, and the MCA is 70.77% and 60.62%, respectively. In the two algorithms, the correct rate of the bird’s-eye spot is the highest, but there is no obvious regularity for the rest of diseases. The bird’s-eye spot is clearly distinguishable, characterized by small and dense red-brown dots, which are significantly different from other disease characteristics, so its accuracy of identification is high.
The recognition accuracy of tea leaf disease by SVM and MLP algorithm is not high, which is caused by artificial selection of features. The recognition effect of SVM and MLP algorithm largely depends on whether the artificially selected features are reasonable, and researchers usually rely on personal experience when selecting features. Although better results can be obtained using artificial feature classification, these features are specific to certain datasets. If you use the same features to analyze different data sets, the results may be very different, which is a problem inherent in these technologies.
LeafNet has the best recognition effect on the bird’s-eye spot, which may be due to the obvious plant pathological symptoms and the strong recognition ability of the LeafNet algorithm. The white spot disease was the second, while the other diseases range from 84 to 93%. Because of the similar pathological characteristics of the gray blight, red leaf spot, and brown blight, the classification accuracy of the three diseases is lowest. The symptoms of gray blight and brown blight diseases are too similar, which both exhibit annulations in their late stage and cannot be distinguished. In addition, the symptoms of white spot and bird’s-eye spot diseases both include reddish brown spots at early stages. In addition, both anthracnose and brown blight diseases are typified by waterlogged leaves during early disease stages, while symptoms are different in the later stages. Some diseases can occur in tea plants throughout the year, although some diseases occur at distinct times. Consequently, diseases diagnosed at different times may affect the accuracy of disease identification. Another factor that affects the accuracy may be that the tea leaf can be infected with two or more diseases at the same time. This is because when the tea leaf is infected by one pathogen, the leaves are suffering from physiological weakness, and the second pathogen can easily infect. Therefore, the above factors explain the main reasons for the low accuracy of the test model in some diseases.
In addition, the performance of LeafNet is compared with the method of Reference [40], which contains 10 diseases of 3 crops with a maximum accuracy of 97.3%. Therefore, the performance of LeafNet is slightly lower than Reference [40], which used currently popular transfer learning algorithm. The main advantages of this algorithm are as follows: the network can converge quickly when the data set is small; easy to implement; and shorter training time. Therefore, in the future we will continue to research on and apply transfer learning algorithms to identify more plant diseases.
CNNs have developed into mature techniques that have been increasingly applied in image recognition. The computational complexity needed for neural network analyses is considerably reduced compared to other algorithms, and it also significantly improves computing precision. Concomitantly, the high fault tolerance of CNNs allows the use of incomplete or fuzzy background images, thereby effectively enhancing the precision of image recognition.
Feature extraction is an important step in image classification and directly affects classification accuracies. Thus, two feature extraction methods and three classifiers were compared in their abilities to identify seven tea leaf diseases in the present manuscript. These analyses revealed that LeafNet yielded the highest accuracies among SVM and MLP classification algorithms. CNNs thus have obvious advantages for identifying tea leaf diseases. Importantly, the results from the present study highlight the feasibility of applying CNNs in the identification of tea leaf diseases, which would significantly improve disease recognition for tea plant agriculture. Although the disease classification accuracy of the LeafNet was not 100%, improvements upon the present method can be implemented in future studies to improve the method and provide more efficient and accurate guidance for the control of tea leaf diseases.
In this manuscript, the expansion process of sample data is a time-consuming process, but with the continuous growth of network information resources, the number of tea tree disease images will continue to increase, so we must collect images of different morphological features in the early, middle, and late stages of each disease and continuously expand the tea tree disease data set to make the data set more detailed and comprehensive.
At present, disease recognition is based on computer system operations. However, as the performance of smartphones continues to improve, the recognition model of deep convolutional neural networks is migrated to android-based mobile applications. It can timely and accurately obtain relevant information about diseases and can provide help for the control of tea tree diseases.
We acknowledge the funding support by key R&D projects of Ningxia Hui Autonomous Region (2017BY080) and the National Natural Science Foundation of China (M1942001) and Natural Science Foundation of Inner Mongolia Autonomous Region (2019MS08168).
The authors declare no conflict of interest.
Landslide is a phenomenon that represents the downward movements of a wide range of slope-forming materials (soils/rocks) due to gravitational and other driving forces [1, 2]. Considering the characteristics of the sliding materials and mechanisms of movements they can be classified as falls, topples, slides, flows, spreads, or any mixture of these and occur either slowly or suddenly. Situated in the horn of Africa between 33 and 48°E longitude and 3.40 and 14.85°N latitude, Ethiopia is the second African nation with a population of about 115 million (www.worldometers.info) and a surface area of 1.122 million km2. The landscape constitutes highlands plateaus, dissected valleys, escarpments, gentle slopes, and flat plains. These land features are results of geodynamic processes associated with the establishment of the East African Rift System (EARS), which is a narrow North-west - South-east (NE-SW) elongated rift with thin continental lithosphere. This rift dissects Ethiopia diagonally into western and eastern plateaus that represent the Nubian and Somalian plates, respectively (Figure 1) [3, 4, 5]. Active rifting processes combined with local and global drivers (like seismicity, hydrometeorological events, and demographic factors) have created a suitable environment for the widespread effects of landslides. It occurs in the mountainous regions of Ethiopia dominantly in the North-Northwest (N-NW), central and South – Southwest (S-SW) highlands, and rift-margins, usually following intensive precipitations and brings variable impacts on life, built infrastructures, and natural environment [6, 7, 8, 9].
Generalized map of the East African Rift System (the dotted lines show boundaries of the East African Rift System, while the triangles represent volcanic centers (from Riftvolc consortium, 2013).
In this work, the distributions, probable causative factors, and impacts of landslides are described with more emphasis on infrastructures using few selected case studies. Applying different secondary sources, a landslide inventory map is compiled and relationships between the natural attributes (lithology, slope height, slope angle, rainfall, and land use-land cover) and spatial distributions of landslides are assessed. Moreover, a susceptibility zoning map is generated involving the mentioned parameters to which weights were assigned considering their significance to slope failure. Such a map serves as an input to delineate areas according to their importance to various developmental activities and also helps to identify risk potential ones that demand more evaluations and implementation of mitigation measures before major projects are supported.
Ethiopia’s land surface is characterized by wide elevation contrast that varies from about 125 m below sea level to 4550 m above mean sea level which represents the lowest point in the world, Danakil Depression, and Ras-Dashen mountains (Figure 2c). The elevation is the key determinant that defines the climatic conditions of Ethiopia. Accordingly, the country is divided into five climatic zones (Figure 2a) that locally known as
Climatic zones (a), average annual rainfall distribution (b), and simplified geological (overlain on the topographic) maps of Ethiopia (c). Sources: [
The rifting process has defined not only the geomorphology but also the geological settings of Ethiopia, which are discussed in many works [3, 6, 11, 13, 14]. Hence, the formations that underlay the Ethiopian territory differ in composition and age, which ranges from Quaternary to Precambrian (Figure 2c). The oldest Precambrian basement rocks are represented by high-grade ortho- and paragneisses and migmatites as well as low-grade volcano-sedimentary—ultramafic assemblages and granitoids [13]. These Precambrian rocks constitute part of the Pan-African Mozambique belt and are distributed in the northern, western, and southern parts of Ethiopia. These formations have undergone prolonged erosion and denudation during Paleozoic that resulted in undulated terrain over which thick Mesozoic sediments (mainly sandstone and limestone) were deposited. The Jurassic sediments cover wide areas of eastern and some places in central and northern Ethiopia. Uplifting of the Afro-Arabian block during Tertiary has resulted in the eruption of a large volume of lava through fractures and covers a substantial part of the country forming elevated terrains. During this period, sediments deposition took place that cover eastern Ethiopia. Meanwhile, the quaternary period is known for the placement of volcanic lava in areas from Afar depression up to the Lakes Region in the central main Ethiopia rift. Thick Quaternary sediments are distributed in Gambela, Borena, Metema, and few other flat lowland areas (Figure 2c).
From the demographic perspective, areas categorized as
The basic objective of this study is to examine the distributions, causative factors, and impacts of landslides and acquire a fundamental understanding enabling to develop effective mitigation measures that help to save life and the economy. Accordingly, its specific objectives are: (a) conduct inventory of landslide occurrences across the nation; (b) map links between the spatial distributions and natural attributes that trigger and/or aggravate landslides; (c) assess impacts of landslides on life and infrastructures; d) produce landslide susceptibility zoning map of Ethiopia.
The methodology used in this study comprises—(a) collection and analyses of geological, engineering geological, and geo-hazard data from published and unpublished reports and research publications [11, 15, 16, 17, 18, 19, 20, 21, 22, 23]. All data are compiled in the geographic coordinate system using WGS84 datum; (b) collection of rainfall data—the Chirps gridded data for the year 2015 available online was used after comparing it with the National Meteorological Agency (NMA) data, which was found almost alike; (c) download land use-land cover map from National Aeronautics and Space Administration (NASA) web page; (d) data about past landslides events and their impacts. This includes information about the date and time of occurrences, deaths, injuries, forced resettlements, damages to infrastructure, and possible causes; Government offices, non-governmental organizations (NGOs), private firms, research publications, mass media, and local communities, including elder people with knowledge previous events, have served as sources; (e) 30 m resolution DEM data—important inputs about slope height (elevation), slope gradient, and slope direction (aspect) are extracted. These data are closely linked to rainfall and temperature distributions, soil humidity, soli thickness, vegetation types, and density as well as hydrological features of sloppy areas that determine the scale/rates of mass movements; (f) applying a multi-class scoring system based on assigning of weights to selected parameters contributing to slope failure, produce landslide susceptibility zoning map [24, 25].
This landslide inventory has identified more than 600 locations across the nation, where landslides occurrences are clearly observed, very few of them are even known with a history of repeated events. Moreover, it reflects localities, where potential landslide risks are imminent [7, 8, 9, 15, 16, 17, 18, 19, 20, 21, 22, 23, 26, 27, 28, 29]. The distribution of inventory data well correlates with lithology, elevation, structural, rainfall, and seismicity maps. Only considering the patterns, landslides occurrences are tentatively classified into four blocks, Block A–D (Figure 3).
Landslide inventory map (left) and landscape of NE part of Ethiopia (right).
Dese and its surrounding are the most well-known areas, where recurrent landslides cause impacts on settlements, roads, and other properties (Figure 4a and b). At many places, emerging springs from near surfaces are observed which indicate shallow groundwater. So, steep terrain, undercutting of stream banks, slope erosion, and shallow groundwater are key factors that trigger/aggravate displacement of slope materials. Meanwhile, huge volcanic blocks that are almost detached from the parent rocks are observed at the southern end of the block, in Mushmado village, Say-Debir district, about 8 km from Lemi town (Figure 4c). The probability that these blocks would crumble into the valley side is very high if triggered by extreme hydrometeorological, seismic, or other events and will put life, infrastructures, and farmlands in the valley under very high rockfall risk.
Panoramic views of landslides: (a) partial settlement of house foundation, in Dese town; (b) debris slide threatening the Addis Ababa-Dese main road, Kewet district, Debresina town; (c) rockfall risk in Mushmado village, Saya-Debir district, North Shewa zone.
The landslides in the Abay gorge, between Dejen and Gohatsion main road, have long and repeated histories, and this economically vital route passes through the 40 km wide Abay (Nile) valley (Figure 5). Subsurface investigations carried out within this valley revealed the depths to the slip planes mainly vary are the range of 14–25 m [22]. Even though deaths are not reported, unofficial sources disclosed that the cost of monitoring and road maintenance exceeds 1.5 million USD/year.
View of landslide occurred in Kurar village, Dejen side (a), the same route, but on the Gohatsion side (b–d): road under maintenance in June 2010 (b), rockfall and debris slide damaged it in August 2010 (c), the site was visited in September 2019 (d).
Panoramic view of a landslide body in Welaite village, 2 km NE of Gidole town observed in March 2011 (a), and the same body observed in March 2016 (b). Note that in 2011 its width was about 40 m whereas in 2016 it expanded to about 200 m.
This recent occurrence within the deeply excavated zone (up to 25 m) started in 2009 following intensive rainfalls that saturate the subsurface. The road construction intended to connect Gidole with the Arbaminch-Konso main road has affected the toe parts of the old landslide zone and resulted in the release of shallow groundwater that triggered that landslide. To prevent mass movement slope regarding, about 250 m long retaining walls and drainage ditches were constructed. But due to the large extent of the sliding zone these measures did not change the situation, rather doubled the project cost. So, construction across the failed was abandoned in 2013.
The landslide observed in Alem village, Dodota district, in September 2019 has severely damaged a section on the Dera-Asela main road (Figure 7a). The mudflow occurred on May 28, 2018 (Figure 7a and b) following heavy rainfalls has triggered the sudden movement of a huge volume of earth mass from the head of the landslide and buried houses with 22 people in Western Arsi Zone, Tulu-Gola village, of which 14 were from the same family (May 30, 2018, the Ethiopian reporter).
Road collapse at Alem village, Dodota district, along with the Dera-Assela road (a) and mudslide that killed 22 people and domestic animals in Tulu-Gola village, Western Arsi zone (b and c).
In general, this inventory survey has provided tangible information about the spatial distribution, main causative factors, and impacts of landslides. Meanwhile, lack of well-organized records about the types and extents of damages, at this stage it is impossible to give any credible estimations of the economic and environmental losses caused by landslides. Abay A. [30] estimated the losses from 1998 to 2003 to be 135 death, 3500 displaced households, and 1.5 million USD worth of property damages. B. Abebe, et al. [8] stated that landslides that occurred between 1993 and 1998 have claimed hundreds of human lives, damaged over a hundred kilometers of asphalt roads, destroyed many houses, farmlands, and natural vegetations. Similarly, a compilation of data from mass media, newspapers, different reports, and affected communities, (including Fana Broadcasting Corporation; Ethiopian Broadcast Corporation (EBC); Walta Information Center; GSE unpublished technical reports published in 2003–2019) revealed that only between 2016 and 2020 more than 302 people and 1500 domestic animals were killed (Table 1).
Region | Landslide affected district (woredas) | Death |
---|---|---|
Tigray | Hintalo-Wajirat, Hawzen, Atsbi-Wenbera, Degua-Temnbie, Enderta, and Samri-Shart | NR |
Amhara | Harbu, Ambassel, Guba-Lafto, Kalu, Dawint, Delanta, Werebabu, Bati, Bugna, Kutaber, Dese-Zuria, Artuma-Farsina, Jille, Efratana-Gidim, Debresina, Kewet, Wagide, Mafud, Mezezo, Chefie-Golana, Dawe-Rahmedo, Gozamin, Gonch-Siso Ense, Hulet-Ej-Ense, Shebel-Berenta, Adet, Sekela, Awabel, Machakil, Dejen, Lai-Armachoho, Ebinat, Guangua, Quarit | 11 deaths |
Oromiya | Wolmera, Ambo, Guder, Were-Jarso, Kuyu, Jeldu, Tikur, Golelcha, Dodotanasire, Merti, Boset, Aseko, Sude, Dugda-Bora, Wenchi, Welesona Gora, Chela, Chole, Guba-Korcha, Chiro, Dendi, Deder, Kombolcha, Babile, Tullo, Jeju, Daro-Lebbu, Dobba, Seke-Chekorsa, Dedo, Omo-Nada, Goma, Limu-Kosa, Tiro-Afeta, Haromaya, Girawa, Gursum, Chelenko, Bedno, Horo-Guduru | 73 deaths and 20 injuries |
Southern Nations and Nationalities People (SNNP) | Aleta-Wondo, Kokir Gedebano, Ameya, Gorro, Gumer, Enemorna-ener, Soddo, Meskanena-mareko, Silti, Esara-Tocha, Ela, Marekagena, Decha, Gimbo, Aroresa, Bensa, Dale, Yiga Dera, Shebedino, Yirgachefe, Derashe, Arbaminchzuria, Amaro, Gofazuria, Basketo, Bako-Gazer, Gidole, Konso | 102 deaths in one incident |
Others | Addis Ababa | 116 deaths |
Summary of landslide inventory showing affected districts and death and injury reported from 2016 to 2020.
The landslide in different parts of the country is associated related with three distinct geological setups—(a) landslides developed within the Territory volcanic environment where saturated pyroclastic materials and clay are present as intercalations within the volcanic flows that cover a wide area of the Ethiopian highlands; (b) landslides formed within the sedimentary terrain and the presence of siltstone, shale, and marl as intercalations within the limestone sequence. These are common in the Abay (Nile) valley, in areas south of Mekele (Northern Ethiopia); (c) presence of unstable colluvial materials (silt and clay with gravel and boulder matrix) in areas of relatively gentle terrain covering different formations. Overall, the intercalation within the volcanic and sediments acts as rupture surfaces that aggravate easily displacement of landmasses whenever absorb more fluid in the rainy season.
The root causes that initiated or accelerated landslide observed at various locations could be associated with the following factors—(a) presence of physically incompetent (soft) earth materials that make up slope surfaces or elevated terrains and also effects of structural discontinuities in areas; (b) intensity and duration of rainfall and effects flooding, erosion a well as groundwater level fluctuations; (c) slope heights and (elevation) and slope angles, which favor mass movements; (d) poor earthwork practices during infrastructure developments (constructions of roads, bridges, dams/reservoirs), and quarrying for mine exploitations. These works involve the removal of earth masses from one place and dumping it into another place which causes either mass deficiency or excess load or both; the effects destabilize slop balances; (e) demographic factor expressed by fast population growth that accompanied by a continuous struggle for resource share. Such struggles put too much pressure on the natural environment and aggravate slope movements; (f) passiveness to enforce code of land-use practices and make accountable those who violate norms; (g) lack of awareness (illiteracy) among rural communities about the influence of landslides in their livelihoods; (h) absence of alternative means of subsistence for rural youth community who have little access to land ownership. So, they rely on over-using of the natural environment that leads to intensive land degradation. Except the natural factors, the human-related ones seem to be fully manageable if better awareness is created, job opportunities are improved and extreme poverty is reduced, land use and land administration codes and practices are enforced, and traditional community practices on land and forest preservations are fully respected. These measures play their role to improve communities’ resilience to cope up with the impacts of landslides. The spatial associations between landslide and seismicity are explained in different works [4, 31, 32, 33]. In the Ethiopian context, the occurrences of landslides and earthquake epicenters that are practically concentrated within the rift system and surrounding plateaus are found to have very close correlations. But no instrumental records are available that justify the contribution of ground vibrations to triggering landslides.
Landslide susceptibility zoning maps are useful tools to differentiate areas that are suitable for agriculture, infrastructure development, national parks, or other purposes as well as delineate risk-prone areas that should be either protected or rehabilitated before approval of any developmental projects [24, 34, 35]. In Ethiopian landslide, mapping and risk zonation were carried out in specific hazard affected areas, mostly in the highlands and rift regions, using ground survey and remote sensing data [8, 22, 27, 28, 30, 36, 37, 38, 39, 40]. However, in this work attempt is made to produce a landslide susceptibility zoning map of the country and correlated with the inventory data acquired through extensive fieldworks mainly by the Geological Survey of Ethiopia, where the lead author has been working for a long time. The field observation data was also used for validation purposes. Thus, the parameters for analyses were selected based on the expert’s decision to which weighted values were assigned according to their contributions or influence to slope instabilities [24, 25]. The weights given to involved parameters are as follows: For lithology, elevation, and rainfall—20% each, for slope angle and land use-land cover—15% each, and for aspect—10%. Initially, each of these parameters was sub-divided into five categories, which represent the very low, low, moderate, high, and very high landslide susceptibility zones.
Then using the weighted overlay method in the ArcGIS environment, the map displayed in Figure 8 is generated. The spatial coverage of each class was calculated by multiplying the corresponding raster counts by the grid pixel sizes and dividing a single class value by the total areal coverage and then multiplying by 100%. Accordingly, about 49.1% of Ethiopia’s land surface is susceptible to landslides, of which 39% moderate, 10% high, and 0.1% very high-risk zones. Similarly, 50.9% of the territory is categorized either as very low (5.9%) or low (45%) susceptible zones (Table 2).
Landslide susceptibility zoning map of Ethiopia and known landslide occurrences.
No | Susceptible zone | Areal coverage (sq. km) | Country coverage (%) |
---|---|---|---|
1 | Very low | 66,287 | 5.9 |
2 | Low | 504,791 | 45.0 |
3 | Moderate | 437,421 | 39.0 |
4 | High | 112,152 | 10.0 |
5 | Very high | 1448 | 0.1 |
Total coverage | 1,122,104 | 100 |
Landslide susceptibility zoning.
This assessment clearly indicated that landslides are major threats to life, infrastructures, and the natural environment. Natural and human-induced factors (existences of poorly consolidated, easily erodible, saturated and soft earth materials, high slope gradients, intensive or continuous precipitations with subsequent flooding and erosion, scarcity or absence of vegetation cover in sloppy terrains, ground vibrations or seismicity, and continuous growth of population with poor land-use practices) are among the key causes that exposed about 49% of the country to landslide risks. Unfortunately, until the road sector sensed the real challenges posed by a landslide and the ever-increasing rates of fatalities and environmental losses became evident, the issue has never been taken seriously. Hence, it is quite important to proceed with landslide risk assessments to identify and prioritize areas based on their extents, frequency of occurrences, the severity of consequences, as well as nature of different elements exposed to risk. This could be possible through careful considerations of updated landslide inventory data/maps and introducing varieties of risk susceptibility models based on integrated analyses of high-resolution remote sensing and ground observation data, which represent distributions of natural and human-related factors. Ultimately, such comprehensive assessments will play a positive role to ease consequences on life, infrastructures, and the natural environment. It is important to underline that the existing trends of land-use practices are completely inadequate to manage impacts of human-induced landslides that occur very widely. Therefore, implementing zero tolerance for improper land uses through stringent monitoring and enforcement of relevant policies, guidelines, directives, and respecting important social norms must be taken as fundamental tasks of all concerned bodies.
We are very grateful to geoscientists of the Geological Survey of Ethiopia (Leta Alemayehu, Habtamu Eshetu, Yewunesh Bekele, Biruk Abel, Abaynesh Mitiku, Tekaligene Tesfaye, Yekoye Bizuye, Debebe Nida, and many others), Addis Ababa University, Ethiopian Roads Authority, National Disaster Risk Management Commission (NDRMC), and other who put tremendous efforts to travel to various parts areas of the country and collect invaluable data used in this assessment. We also extend our sincere appreciation to those who put direct or indirect contributions to this piece of work.
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The problematic is presented, and motor impairments for upper and lower limbs are characterized. 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Researchers and students will find the chapter appealing with a preliminary description of P300 ERP. This chapter also appreciates the importance and advantages of noninvasive ERP technique. In noninvasive BCI, the P300 ERPs are extracted from brain electrical activities [electroencephalogram (EEG)] as a signature of the underlying electrophysiological mechanism of brain responses to the external or internal changes and events. As the chapter proceeds, topics are covered on more relevant scholarly works about challenges and new directions in P300 BCI. Along with these, articles with the references on the advancement of this technique will be presented to ensure that the scholarly reviews are accessible to people who are new to this field. To enhance fundamental understanding, stimulation as well as signal processing methods will be discussed from some novel works with a comparison of the associated results. 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However, despite AD being one of the most prevalent neurodegenerative diseases in aging societies, no clinically successful therapeutic strategies for its treatment or prevention have been reported to date. Studies have indicated that gut microbial alterations are linked to AD. Probiotics are living microorganisms that are known to confer health benefits to the host when ingested in adequate amounts. Certain strains of probiotics appear to influence the central nervous system (CNS) and behavior via the microbiota-gut-brain axis. Increasing evidence from preclinical and clinical studies has demonstrated that probiotics possess preventive as well as therapeutic potential for AD. It is speculated that probiotics could ameliorate the progression of AD by modulating the inflammatory process, counteracting oxidative stress, and other possible mechanisms, although further studies are needed to understand the details. 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It is enshrined in posterior cranial fossa behind the pons and medulla oblongata and separated from these structures by cavity of fourth ventricle. It is connected to brainstem by three fibre tracts known as cerebellar peduncles. Cerebellum controls the same side of body. It precisely coordinates skilled voluntary movements by controlling strength, duration and force of contraction, so that they are smooth, balanced and accurate. It is also responsible for maintaining equilibrium, muscle tone and posture of the body. This is achieved through the use of somatic sensory information in modulating the motor output from the cerebrum and brainstem. Sherrington regarded cerebellum as the head ganglion of the proprioceptive system. Dysfunction of cerebellum along with degenerative diseases of cerebellum such as spinocerebellar ataxia, multiple sclerosis, malignant tumours, etc. may culminate into disequilibrium, hypotonia, difficulty in talking, sleeping, maintaining muscular coordination and dyssynergia which at times may be life threatening. Hence, knowledge of anatomy of cerebellum is imperative for neuroanatomists and neurosurgeons.",book:{id:"9157",slug:"neurodegenerative-diseases-molecular-mechanisms-and-current-therapeutic-approaches",title:"Neurodegenerative Diseases",fullTitle:"Neurodegenerative Diseases - Molecular Mechanisms and Current Therapeutic Approaches"},signatures:"Rajani Singh",authors:[{id:"319468",title:"Dr.",name:"Rajani",middleName:null,surname:"Singh",slug:"rajani-singh",fullName:"Rajani Singh"}]},{id:"55773",title:"Event-Related Potentials for the Study of Cognition",slug:"event-related-potentials-for-the-study-of-cognition",totalDownloads:1550,totalCrossrefCites:2,totalDimensionsCites:3,abstract:"Despite the vast literature on event-related potentials (ERPs), many clinical professionals are still unaware of the huge variety of possible applications they offer. The aim of this chapter is not to show the classical use of ERPs, focused on analyzing the first steps of information processing (sensory pathways). On the contrary, this chapter will be focused on the use of these ERPs in the assessment of cognitive function. In particular, this chapter is mainly focused on the use of ERPs to better understand the neural bases of cognitive impairment from the electrical activity of the brain. Describing all the possible ERP components and their cognitive meaning is a huge endeavor, and this chapter will only be focused on three of them: contingent negative variation (CNV), mismatch negativity (MMN), and P300. To improve the reader’s knowledge about these ERPs in cognition, a specific description will be given about the stimulation required to obtain the specific component, the topography, and latency shown. Moreover, a description of the neurophysiological bases of the component, its relationship with psychological processes and neural sources will be also included. Pathological alterations suffered by the component will also be briefly described.",book:{id:"5751",slug:"event-related-potentials-and-evoked-potentials",title:"Event-Related Potentials and Evoked Potentials",fullTitle:"Event-Related Potentials and Evoked Potentials"},signatures:"Manuel Vazquez-Marrufo",authors:[{id:"189375",title:"Dr.",name:"Manuel",middleName:null,surname:"Vazquez-Marrufo",slug:"manuel-vazquez-marrufo",fullName:"Manuel Vazquez-Marrufo"}]},{id:"46092",title:"A Practical Guide to an fMRI Experiment",slug:"a-practical-guide-to-an-fmri-experiment",totalDownloads:3180,totalCrossrefCites:0,totalDimensionsCites:1,abstract:null,book:{id:"4461",slug:"advanced-brain-neuroimaging-topics-in-health-and-disease-methods-and-applications",title:"Advanced Brain Neuroimaging Topics in Health and Disease",fullTitle:"Advanced Brain Neuroimaging Topics in Health and Disease - Methods and Applications"},signatures:"Nasser Kashou",authors:[{id:"77988",title:"Dr.",name:"Nasser",middleName:"H",surname:"Kashou",slug:"nasser-kashou",fullName:"Nasser Kashou"}]}],onlineFirstChaptersFilter:{topicId:"1059",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:11,numberOfPublishedChapters:91,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:108,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:333,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:11,numberOfPublishedChapters:144,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:124,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:23,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:12,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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He worked as a post-doctoral fellow at the Public Health Research Institute (PHRI), Newark, NJ for four years before accepting a three-year faculty position at Brigham Young University-Hawaii. Dr. Engohang-Ndong is a tenured faculty member with the academic rank of Full Professor at Kent State University, Ohio, where he teaches a wide range of biological science courses and pursues his research in medical and environmental microbiology. 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He has an excellent track record in the herpesvirus field, and his group is engaged in clinical research in the field of Epstein-Barr virus diseases. He is the editor of the online Encyclopedia of Environment and he coordinates the Universal Health Coverage education program for the BioHealth Computing Schools of the European Institute of Science.",institutionString:null,institution:{name:"Grenoble Alpes University",country:{name:"France"}}},{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. Rodriguez-Morales",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},{id:"332819",title:"Dr.",name:"Chukwudi Michael",middleName:"Michael",surname:"Egbuche",slug:"chukwudi-michael-egbuche",fullName:"Chukwudi Michael Egbuche",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/332819/images/14624_n.jpg",biography:"I an Dr. Chukwudi Michael Egbuche. I am a Senior Lecturer in the Department of Parasitology and Entomology, Nnamdi Azikiwe University, Awka.",institutionString:null,institution:{name:"Nnamdi Azikiwe University",country:{name:"Nigeria"}}},{id:"284232",title:"Mr.",name:"Nikunj",middleName:"U",surname:"Tandel",slug:"nikunj-tandel",fullName:"Nikunj Tandel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/284232/images/8275_n.jpg",biography:'Mr. Nikunj Tandel has completed his Master\'s degree in Biotechnology from VIT University, India in the year of 2012. He is having 8 years of research experience especially in the field of malaria epidemiology, immunology, and nanoparticle-based drug delivery system against the infectious diseases, autoimmune disorders and cancer. He has worked for the NIH funded-International Center of Excellence in Malaria Research project "Center for the study of complex malaria in India (CSCMi)" in collaboration with New York University. The preliminary objectives of the study are to understand and develop the evidence-based tools and interventions for the control and prevention of malaria in different sites of the INDIA. Alongside, with the help of next-generation genomics study, the team has studied the antimalarial drug resistance in India. Further, he has extended his research in the development of Humanized mice for the study of liver-stage malaria and identification of molecular marker(s) for the Artemisinin resistance. At present, his research focuses on understanding the role of B cells in the activation of CD8+ T cells in malaria. Received the CSIR-SRF (Senior Research Fellow) award-2018, FIMSA (Federation of Immunological Societies of Asia-Oceania) Travel Bursary award to attend the IUIS-IIS-FIMSA Immunology course-2019',institutionString:"Nirma University",institution:{name:"Nirma University",country:{name:"India"}}},{id:"334383",title:"Ph.D.",name:"Simone",middleName:"Ulrich",surname:"Ulrich Picoli",slug:"simone-ulrich-picoli",fullName:"Simone Ulrich Picoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334383/images/15919_n.jpg",biography:"Graduated in Pharmacy from Universidade Luterana do Brasil (1999), Master in Agricultural and Environmental Microbiology from Federal University of Rio Grande do Sul (2002), Specialization in Clinical Microbiology from Universidade de São Paulo, USP (2007) and PhD in Sciences in Gastroenterology and Hepatology (2012). She is currently an Adjunct Professor at Feevale University in Medicine and Biomedicine courses and a permanent professor of the Academic Master\\'s Degree in Virology. She has experience in the field of Microbiology, with an emphasis on Bacteriology, working mainly on the following topics: bacteriophages, bacterial resistance, clinical microbiology and food microbiology.",institutionString:null,institution:{name:"Universidade Feevale",country:{name:"Brazil"}}},{id:"229220",title:"Dr.",name:"Amjad",middleName:"Islam",surname:"Aqib",slug:"amjad-aqib",fullName:"Amjad Aqib",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229220/images/system/229220.png",biography:"Dr. Amjad Islam Aqib obtained a DVM and MSc (Hons) from University of Agriculture Faisalabad (UAF), Pakistan, and a PhD from the University of Veterinary and Animal Sciences Lahore, Pakistan. Dr. Aqib joined the Department of Clinical Medicine and Surgery at UAF for one year as an assistant professor where he developed a research laboratory designated for pathogenic bacteria. Since 2018, he has been Assistant Professor/Officer in-charge, Department of Medicine, Manager Research Operations and Development-ORIC, and President One Health Club at Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Pakistan. He has nearly 100 publications to his credit. His research interests include epidemiological patterns and molecular analysis of antimicrobial resistance and modulation and vaccine development against animal pathogens of public health concern.",institutionString:"Cholistan University of Veterinary and Animal Sciences",institution:{name:"University of Agriculture Faisalabad",country:{name:"Pakistan"}}},{id:"333753",title:"Dr.",name:"Rais",middleName:null,surname:"Ahmed",slug:"rais-ahmed",fullName:"Rais Ahmed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333753/images/20168_n.jpg",biography:null,institutionString:null,institution:{name:"University of Agriculture Faisalabad",country:{name:"Pakistan"}}},{id:"62900",title:"Prof.",name:"Fethi",middleName:null,surname:"Derbel",slug:"fethi-derbel",fullName:"Fethi Derbel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62900/images/system/62900.jpeg",biography:"Professor Fethi Derbel was born in 1960 in Tunisia. He received his medical degree from the Sousse Faculty of Medicine at Sousse, University of Sousse, Tunisia. He completed his surgical residency in General Surgery at the University Hospital Farhat Hached of Sousse and was a member of the Unit of Liver Transplantation in the University of Rennes, France. He then worked in the Department of Surgery at the Sahloul University Hospital in Sousse. Professor Derbel is presently working at the Clinique les Oliviers, Sousse, Tunisia. His hospital activities are mostly concerned with laparoscopic, colorectal, pancreatic, hepatobiliary, and gastric surgery. He is also very interested in hernia surgery and performs ventral hernia repairs and inguinal hernia repairs. He has been a member of the GREPA and Tunisian Hernia Society (THS). During his residency, he managed patients suffering from diabetic foot, and he was very interested in this pathology. For this reason, he decided to coordinate a book project dealing with the diabetic foot. Professor Derbel has published many articles in journals and collaborates intensively with IntechOpen Access Publisher as an editor.",institutionString:"Clinique les Oliviers",institution:null},{id:"300144",title:"Dr.",name:"Meriem",middleName:null,surname:"Braiki",slug:"meriem-braiki",fullName:"Meriem Braiki",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/300144/images/system/300144.jpg",biography:"Dr. Meriem Braiki is a specialist in pediatric surgeon from Tunisia. She was born in 1985. She received her medical degree from the University of Medicine at Sousse, Tunisia. She achieved her surgical residency training periods in Pediatric Surgery departments at University Hospitals in Monastir, Tunis and France.\r\nShe is currently working at the Pediatric surgery department, Sidi Bouzid Hospital, Tunisia. Her hospital activities are mostly concerned with laparoscopic, parietal, urological and digestive surgery. She has published several articles in diffrent journals.",institutionString:"Sidi Bouzid Regional Hospital",institution:null},{id:"229481",title:"Dr.",name:"Erika M.",middleName:"Martins",surname:"de Carvalho",slug:"erika-m.-de-carvalho",fullName:"Erika M. de Carvalho",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229481/images/6397_n.jpg",biography:null,institutionString:null,institution:{name:"Oswaldo Cruz Foundation",country:{name:"Brazil"}}},{id:"186537",title:"Prof.",name:"Tonay",middleName:null,surname:"Inceboz",slug:"tonay-inceboz",fullName:"Tonay Inceboz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/186537/images/system/186537.jfif",biography:"I was graduated from Ege University of Medical Faculty (Turkey) in 1988 and completed his Med. PhD degree in Medical Parasitology at the same university. I became an Associate Professor in 2008 and Professor in 2014. I am currently working as a Professor at the Department of Medical Parasitology at Dokuz Eylul University, Izmir, Turkey.\n\nI have given many lectures, presentations in different academic meetings. I have more than 60 articles in peer-reviewed journals, 18 book chapters, 1 book editorship.\n\nMy research interests are Echinococcus granulosus, Echinococcus multilocularis (diagnosis, life cycle, in vitro and in vivo cultivation), and Trichomonas vaginalis (diagnosis, PCR, and in vitro cultivation).",institutionString:"Dokuz Eylül University",institution:{name:"Dokuz Eylül University",country:{name:"Turkey"}}},{id:"71812",title:"Prof.",name:"Hanem Fathy",middleName:"Fathy",surname:"Khater",slug:"hanem-fathy-khater",fullName:"Hanem Fathy Khater",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/71812/images/1167_n.jpg",biography:"Prof. Khater is a Professor of Parasitology at Benha University, Egypt. She studied for her doctoral degree, at the Department of Entomology, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, USA. She has completed her Ph.D. degrees in Parasitology in Egypt, from where she got the award for “the best scientific Ph.D. dissertation”. She worked at the School of Biological Sciences, Bristol, England, the UK in controlling insects of medical and veterinary importance as a grant from Newton Mosharafa, the British Council. 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She was a recipient of a Commonwealth fellowship supported by British Council tenable at the Centre for Entomology and Parasitology (CAEP), Keele University, United Kingdom between 2004 and 2005. She was awarded an Honorary Visiting Research Fellow at the same university from 2005 to 2007. \nShe has been an external examiner to the Department of Veterinary Microbiology and Parasitology, University of Ibadan, MSc programme between 2010 and 2012. She is a member of the Nigerian Society of Experimental Biology (NISEB), Parasitology and Public Health Society of Nigeria (PPSN), Science Association of Nigeria (SAN), Zoological Society of Nigeria (ZSN), and is Vice Chairperson of the Organisation of Women in Science (OWSG), LASU chapter. She served as Head of Department of Zoology and Environmental Biology, Lagos State University from 2007 to 2010 and 2014 to 2016. 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