ICTACT Journal on Image and Video Processing

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An Improved CNN Model For Classification Of Apple Leaf Disease And Visualization Using Weighted Gradient Class Activation Map


Affiliations
1 Department of Electronic Science, Babasaheb Bhimrao Ambedkar Bihar University, India
2 Department of Electronics, Maharaja Agrasen College, India
     

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Convolutional Neural Network (CNN), a particular type of forwarding feed network composed of convolutional, pooling, and fully connected layers, has become the dominant and most widely used deep learning architecture. Significantly enhanced effectiveness of Conv Nets has made CNNs the go-to architecture model for almost every image processing-based application. CNNs automatically and adaptively learn spatial hierarchies of features with high accuracy, precision, and efficiency. This paper proposes three CNN models with 5, 6, and 7 layers with two types of classification layers at the top of the model, resulting in six kinds of models. Each model is trained on apple leaf diseases obtained with augmentation deployed on the Plant Village dataset containing images of healthy and three types of leaf diseases. The trained models are compared on training time, testing accuracy, testing time. The best performing model (6-layer based model with fully connected layer as a classifier (6FC) in our case) yields 99.14% accuracy. This best-performing model is also compared with the state of-art models such as VGG-16, InceptionV3, and MobileNetV2, trained using the transfer learning approach. After model comparison, we found our best model (6FC) outperformed the other models based on evaluated performance metrics with improvements as 3.94% gain in accuracy, 25.97% reduced parameters, and less training time (0.51hr) and testing time (20.5 sec) compared to VGG-16. Comparing precision, recall, and f1-score values are also found high (between 0.98 to 1) with our proposed model. The weighted gradient class activation map (GradCAM) technique generates a visualization of class predictions on the test dataset. The Grad-Cam visualization of results validates the prediction score attained by the proposed model.

Keywords

Convolutional Neural Network, Grad-CAM, Deep Learning, Data Augmentation, Transfer Learning
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  • An Improved CNN Model For Classification Of Apple Leaf Disease And Visualization Using Weighted Gradient Class Activation Map

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Authors

Dharmendra Kumar Mahato
Department of Electronic Science, Babasaheb Bhimrao Ambedkar Bihar University, India
Amit Pundir
Department of Electronics, Maharaja Agrasen College, India
Geetika Jain Saxena
Department of Electronics, Maharaja Agrasen College, India

Abstract


Convolutional Neural Network (CNN), a particular type of forwarding feed network composed of convolutional, pooling, and fully connected layers, has become the dominant and most widely used deep learning architecture. Significantly enhanced effectiveness of Conv Nets has made CNNs the go-to architecture model for almost every image processing-based application. CNNs automatically and adaptively learn spatial hierarchies of features with high accuracy, precision, and efficiency. This paper proposes three CNN models with 5, 6, and 7 layers with two types of classification layers at the top of the model, resulting in six kinds of models. Each model is trained on apple leaf diseases obtained with augmentation deployed on the Plant Village dataset containing images of healthy and three types of leaf diseases. The trained models are compared on training time, testing accuracy, testing time. The best performing model (6-layer based model with fully connected layer as a classifier (6FC) in our case) yields 99.14% accuracy. This best-performing model is also compared with the state of-art models such as VGG-16, InceptionV3, and MobileNetV2, trained using the transfer learning approach. After model comparison, we found our best model (6FC) outperformed the other models based on evaluated performance metrics with improvements as 3.94% gain in accuracy, 25.97% reduced parameters, and less training time (0.51hr) and testing time (20.5 sec) compared to VGG-16. Comparing precision, recall, and f1-score values are also found high (between 0.98 to 1) with our proposed model. The weighted gradient class activation map (GradCAM) technique generates a visualization of class predictions on the test dataset. The Grad-Cam visualization of results validates the prediction score attained by the proposed model.

Keywords


Convolutional Neural Network, Grad-CAM, Deep Learning, Data Augmentation, Transfer Learning

References