scholarly journals Classification of tomato leaf diseases using MobileNet v2

2020 ◽  
Vol 9 (2) ◽  
pp. 290
Author(s):  
Siti Zulaikha Muhammad Zaki ◽  
Mohd Asyraf Zulkifley ◽  
Marzuraikah Mohd Stofa ◽  
Nor Azwan Mohammed Kamari ◽  
Nur Ayuni Mohamed
Keyword(s):  
Deep Learning ◽  
Early Stage ◽  
Plant Diseases ◽  
Plant Management ◽  
American Continent ◽  
Vegetable Crop ◽  
Learning Classifier ◽  
Edible Fruit ◽  
Tomato Diseases

<span lang="EN-US">Tomato is a red-colored edible fruit originated from the American continent. There are a lot of plant diseases associated with tomatoes such as leaf mold, late blight, and mosaic virus. Tomato is an important vegetable crop that contributes to the world economically. Despite tremendous efforts in plant management, viral diseases are notoriously difficult to control and eradicate completely. Thus, accurate and faster detection of plant diseases is needed to mitigate the problem at the early stage. A computer vision approach is proposed to identify the disease by capturing the leaf images and detect the possibility of the diseases. A deep learning classifier is utilized to make a robust decision that covers a wide variety of leaf appearances. Compact deep learning architecture, which is MobileNet V2 has been fine-tuned to detect three types of tomato diseases. The algorithm is tested on 4,671 images from PlantVillage dataset. The results show that MobileNet V2 is able to detect the disease up to more than 90% accuracy.</span>

scholarly journals Plant Disease Identification Using Shallow Convolutional Neural Network

Agronomy ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2388
Author(s):  
Sk Mahmudul Hassan ◽  
Michal Jasinski ◽  
Zbigniew Leonowicz ◽  
Elzbieta Jasinska ◽  
Arnab Kumar Maji
Keyword(s):  
Deep Learning ◽  
Late Blight ◽  
Early Blight ◽  
Gray Leaf Spot ◽  
Plant Diseases ◽  
Automated Identification ◽  
Average Accuracy ◽  
Proposed Model ◽  
Effective Manner ◽  
Tomato Diseases

Various plant diseases are major threats to agriculture. For timely control of different plant diseases in effective manner, automated identification of diseases are highly beneficial. So far, different techniques have been used to identify the diseases in plants. Deep learning is among the most widely used techniques in recent times due to its impressive results. In this work, we have proposed two methods namely shallow VGG with RF and shallow VGG with Xgboost to identify the diseases. The proposed model is compared with other hand-crafted and deep learning-based approaches. The experiments are carried on three different plants namely corn, potato, and tomato. The considered diseases in corns are Blight, Common rust, and Gray leaf spot, diseases in potatoes are early blight and late blight, and tomato diseases are bacterial spot, early blight, and late blight. The result shows that our implemented shallow VGG with Xgboost model outperforms different deep learning models in terms of accuracy, precision, recall, f1-score, and specificity. Shallow Visual Geometric Group (VGG) with Xgboost gives the highest accuracy rate of 94.47% in corn, 98.74% in potato, and 93.91% in the tomato dataset. The models are also tested with field images of potato, corn, and tomato. Even in field image the average accuracy obtained using shallow VGG with Xgboost are 94.22%, 97.36%, and 93.14%, respectively.

scholarly journals Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Plants ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1302 ◽  
Author(s):  
Reem Ibrahim Hasan ◽  
Suhaila Mohd Yusuf ◽  
Laith Alzubaidi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Early Detection ◽  
Feature Fusion ◽  
State Of The Art ◽  
Vital Role ◽  
Considerable Improvement ◽  
Plant Diseases ◽  
Recent Emergence

Deep learning (DL) represents the golden era in the machine learning (ML) domain, and it has gradually become the leading approach in many fields. It is currently playing a vital role in the early detection and classification of plant diseases. The use of ML techniques in this field is viewed as having brought considerable improvement in cultivation productivity sectors, particularly with the recent emergence of DL, which seems to have increased accuracy levels. Recently, many DL architectures have been implemented accompanying visualisation techniques that are essential for determining symptoms and classifying plant diseases. This review investigates and analyses the most recent methods, developed over three years leading up to 2020, for training, augmentation, feature fusion and extraction, recognising and counting crops, and detecting plant diseases, including how these methods can be harnessed to feed deep classifiers and their effects on classifier accuracy.

scholarly journals Iot Device Classification in Cloud Organizations Using Deep Belief Networks

2021 ◽  
Author(s):  
H. Azath H ◽  
M. NAGESWARA GUPTHA M ◽  
L. SHAKKEERA L ◽  
M.R.M. VEERA MANICKAM M.R.M ◽  
B. LANITHA B ◽  
...  
Keyword(s):  
Deep Learning ◽  
Detection Rate ◽  
High Dimensional ◽  
Deep Belief Networks ◽  
Malicious Nodes ◽  
Learning Classifier ◽  
Device Classification ◽  
Iot Devices ◽  
Computational Resources

Abstract With the rapid increase in the usage of IoT devices, the cyber threats are increasing among the communication between the IoT devices. The challenges related to security surmounts with increasing number of IoT devices due to its functionality and heterogeneity. In recent times, deep learning algorithms are offered to resolve the constraints associated with detection of malicious devices among the networks. In this paper, we utilize deep belief network (DBN) to resolve the problems associated with identification, detection of anomaly IoT devices. Several features are extracted initially to find the malicious devices in the IoT device network that includes storage, computational resources and high dimensional features. These features extracted from the network traffic assists in achieving the classification of devices by DBN. The simulation is performed to test the accuracy and detection rate of the proposed deep learning classifier. The results show that the proposed method is effective in implementing the detection of malicious nodes in the network than existing methods.

scholarly journals Chest X-ray classification using Deep learning for automated COVID-19 screening

2020 ◽  
Author(s):  
Ankita Shelke ◽  
Madhura Inamdar ◽  
Vruddhi Shah ◽  
Amanshu Tiwari ◽  
Aafiya Hussain ◽  
...  
Keyword(s):  
Deep Learning ◽  
Early Stage ◽  
Ground Glass Opacity ◽  
Classification Model ◽  
X Rays ◽  
X Ray ◽  
The People ◽  
History Of ◽  
Chest X Ray

AbstractIn today’s world, we find ourselves struggling to fight one of the worst pandemics in the history of humanity known as COVID-2019 caused by a coronavirus. If we detect the virus at an early stage (before it enters the lower respiratory tract), the patient can be treated quickly. Once the virus reaches the lungs, we observe ground-glass opacity in the chest X-ray due to fibrosis in the lungs. Due to the significant differences between X-ray images of an infected and non-infected person, artificial intelligence techniques can be used to identify the presence and severity of the infection. We propose a classification model that can analyze the chest X-rays and help in the accurate diagnosis of COVID-19. Our methodology classifies the chest X-rays into 4 classes viz. normal, pneumonia, tuberculosis (TB), and COVID-19. Further, the X-rays indicating COVID-19 are classified on severity-basis into mild, medium, and severe. The deep learning model used for the classification of pneumonia, TB, and normal is VGG16 with an accuracy of 95.9 %. For the segregation of normal pneumonia and COVID-19, the DenseNet-161 was used with an accuracy of 98.9 %. ResNet-18 worked best for severity classification achieving accuracy up to 76 %. Our approach allows mass screening of the people using X-rays as a primary validation for COVID-19.

scholarly journals Detection of Disease in Leaf using Artificial Neural Network

2021 ◽  
Vol 9 (VI) ◽  
pp. 3771-3773
Author(s):  
Veeramaneni Shresta
Keyword(s):  
Agricultural Production ◽  
Preventive Measures ◽  
Crop Yields ◽  
Early Stage ◽  
Region Of Interest ◽  
Plant Diseases ◽  
Android Application ◽  
Leaf Spots ◽  
Processing Techniques

Leaf spots are diseased areas on leaves. Agricultural production is negatively affected by plant diseases. We have proposed a system that can help farmers identify leaf diseases and take appropriate preventive measures to increase crop yields. We take images of diseased leaves and perform various pre-processing techniques on them, to detect the edges of the leaves. The entire region of interest is divided into blocks, and then the characteristics of each block are compared with the characteristics of the image in the database. The main purpose is to identify the disease in the leaf spot of the crop as 80-90% of the plant diseases occur in the leafspot. ANN methods are used for the classification of plant diseases. Using these methods, we can accurately identify and classify various plant diseases. We will create an end-to-end Android application. It is possible to run applications on mobile devices through the use of convolution operations, special integration, and the computational efficiency of using ANN to share parameters. This project will be of great use to the farmers as it will help them to detect the plant diseases in the early stage and enhance the production of crop.

scholarly journals Deep Learning Model for Plant Disease Detection

2020 ◽  
Vol 9 (1) ◽  
pp. 750-754
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Early Detection ◽  
Plant Disease ◽  
Early Stage ◽  
Learning Model ◽  
Plant Diseases ◽  
Convolution Neural Networks ◽  
Deep Learning Model

Food is one of the basic needs of human being. We know that the population is rising enormously.so it is more important to feed such a huge population. But nowadays plants are largely affected with various types of diseases. If proper care should not be taken then it will show effect on quality of food products, quantity and finally on productivity of crops.. so, Early detection of plant disease is very essential, but it is very hard to farmers to monitor the crops manually it takes more processing time, huge amount of work, expensive and need expertised persons. Automatic detection of plant diseases helps the farmers to monitor the large fields easily,because our approach of using convolution neural networks provides a chance to discover diseases at the very early stage. By using Image Processing and machine learning models we can detect the plant diseases automatically but the accuracy is very less, early detection is also a major challenge. With the modern advanced developments in deep learning, in our project we have implemented the convolution neural networks(CNN) which comprises of different layers,by using those layers we can automatically detect and classify the diseases present in the plants. High Classification accuracy and more processing speed are the main advantages of our approach. After training the model on color, grayscale and segmented datasets our deep learning model will be capable of classifying a large number of different diseases and our project gives us the name of the disease that the plant has with its confidence level and also provides remedies for corresponding diseases

scholarly journals Evaluation of scratch and pre-trained convolutional neural networks for the classification of Tomato plant diseases

2021 ◽  
Vol 10 (2) ◽  
pp. 467
Author(s):  
Mohammad Amimul Ihsan Aquil ◽  
Wan Hussain Wan Ishak
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Tomato Plant ◽  
State Of The Art ◽  
Plant Diseases ◽  
Fine Tuning ◽  
Convolutional Network ◽  
Tomato Diseases

<span id="docs-internal-guid-01580d49-7fff-6f2a-70d1-7893ec0a6e14"><span>Plant diseases are a major cause of destruction and death of most plants and especially trees. However, with the help of early detection, this issue can be solved and treated appropriately. A timely and accurate diagnosis is critical in maintaining the quality of crops. Recent innovations in the field of deep learning (DL), especially in convolutional neural networks (CNNs) have achieved great breakthroughs across different applications such as the classification of plant diseases. This study aims to evaluate scratch and pre-trained CNNs in the classification of tomato plant diseases by comparing some of the state-of-the-art architectures including densely connected convolutional network (Densenet) 120, residual network (ResNet) 101, ResNet 50, ReseNet 30, ResNet 18, squeezenet and Vgg.net. The comparison was then evaluated using a multiclass statistical analysis based on the F-Score, specificity, sensitivity, precision, and accuracy. The dataset used for the experiments was drawn from 9 classes of tomato diseases and a healthy class from PlantVillage. The findings show that the pretrained Densenet-120 performed excellently with 99.68% precision, 99.84% F-1 score, and 99.81% accuracy, which is higher compared to its non-trained based model showing the effectiveness of using a combination of a CNN model with fine-tuning adjustment in classifying crop diseases.</span></span>

scholarly journals Deep learning classification of early normal-tension glaucoma and glaucoma suspects using Bruch’s membrane opening-minimum rim width and RNFL

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Sat byul Seo ◽  
Hyun-kyung Cho
Keyword(s):  
Deep Learning ◽  
Early Stage ◽  
Normal Tension Glaucoma ◽  
Learning Model ◽  
Structural Parameter ◽  
Bruch's Membrane ◽  
Bruch’S Membrane ◽  
Bruch’S Membrane Opening ◽  
Deep Learning Model

Abstract We aimed to classify early normal-tension glaucoma (NTG) and glaucoma suspect (GS) using Bruch’s membrane opening-minimum rim width (BMO-MRW), peripapillary retinal nerve fiber layer (RNFL), and the color classification of RNFL based on a deep-learning model. Discriminating early-stage glaucoma and GS is challenging and a deep-learning model may be helpful to clinicians. NTG accounts for an average 77% of open-angle glaucoma in Asians. BMO-MRW is a new structural parameter that has advantages in assessing neuroretinal rim tissue more accurately than conventional parameters. A dataset consisted of 229 eyes out of 277 GS and 168 eyes of 285 patients with early NTG. A deep-learning algorithm was developed to discriminate between GS and early NTG using a training set, and its accuracy was validated in the testing dataset using the area under the curve (AUC) of the receiver operating characteristic curve (ROC). The deep neural network model (DNN) achieved highest diagnostic performance, with an AUC of 0.966 (95%confidence interval 0.929–1.000) in classifying either GS or early NTG, while AUCs of 0.927–0.947 were obtained by other machine-learning models. The performance of the DNN model considering all three OCT-based parameters was the highest (AUC 0.966) compared to the combinations of just two parameters. As a single parameter, BMO-MRW (0.959) performed better than RNFL alone (0.914).

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