Currently, the application of deep learning in crop disease classification is one of the active areas of research for which an image dataset is required. Eggplant (Solanum melongena) is one of the important crops, but it is susceptible to serious diseases which hinder its production. Surprisingly, so far no dataset is available for the diseases in this crop. The unavailability of the dataset for these diseases motivated the authors to create a standard dataset in laboratory and field conditions for five major diseases. Pre-trained Visual Geometry Group 16 (VGG16) architecture has been used and the images have been converted to other color spaces namely Hue Saturation Value (HSV), YCbCr and grayscale for evaluation. Results show that the dataset created with RGB and YCbCr images in field condition was promising with a classification accuracy of 99.4%. The dataset also has been evaluated with other popular architectures and compared. In addition, VGG16 has been used as feature extractor from 8 th convolution layer and these features have been used for classifying diseases employing Multi-Class Support Vector Machine (MSVM). The analysis depicted an equivalent or in some cases produced better accuracy. Possible reasons for variation in interclass accuracy and future direction have been discussed.The revolution of the modern technologies in the recent era has facilitated its application in agriculture to improve production. One of the applications is the diagnosis of plant diseases using a digital image from a camera which in turn will assist the farmers to control its prevalence in the fields. The availability of cheap cameras and the explosive growth on the internet have made the diagnosis relatively less complex with the availability of tools and information about the disease online 1 . But still, human diagnosis is prone to errors 2 . The scope for the automatic disease classification has improved due to the accomplishment in machine learning technologies. Traditionally shallow machine learning algorithms such as neural networks, Support Vector Machine (SVM), or other algorithms were used which is a time-consuming process as it demands feature extraction from the images manually and fed as input to the algorithm for classification. But, the deep learning approaches consist of many layers of processing elements that process images and estimate features automatically for classification. There are four major types of deep learning algorithms namely Convolutional Neural Networks (CNN), autoencoder, restricted Boltzmann machines and sparse encoding, according to a study by Guo et al. 3 Of these, CNN based architectures are most widely used for image classification problems 3 . Recent trends in the use of CNN for disease classification are on the rise and many studies have reported promising results 1-17 .Training of the CNN based deep learning models from scratch is a time consuming (difficult) process and requires a large database. It is also challenging to categorize each image to a crop disease even with an expert...
The COVID-19 outbreak has catastrophically affected both public health system and world economy. Swift diagnosis of the positive cases will help in providing proper medical attention to the infected individuals and will also aid in effective tracing of their contacts to break the chain of transmission. Blending Artificial Intelligence (AI) with chest X-ray images and incorporating these models in a smartphone can be handy for the accelerated diagnosis of COVID-19. In this study, publicly available datasets of chest X-ray images have been utilized for training and testing of five pre-trained Convolutional Neural Network (CNN) models namely VGG16, MobileNetV2, Xception, NASNetMobile and InceptionResNetV2. Prior to the training of the selected models, the number of images in COVID-19 category have been increased employing traditional augmentation and Generative Adversarial Network (GAN). The performance of the five pre-trained CNN models utilizing the images generated with the two strategies have been compared. In the case of models trained using augmented images, Xception (98%) and MobileNetV2 (97.9%) turned out to be the ones with highest validation accuracy. Xception (98.1%) and VGG16 (98.6%) emerged as models with the highest validation accuracy in the models trained with synthetic GAN images. The best performing models have been further deployed in a smartphone and evaluated. The overall results suggest that VGG16 and Xception, trained with the synthetic images created using GAN, performed better compared to models trained with augmented images. Among these two models VGG16 produced an encouraging Diagnostic Odd Ratio (DOR) with higher positive likelihood and lower negative likelihood for the prediction of COVID-19.
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