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An Improved Deep Convolutional Neural Network for Image-Based Apple Plant Leaf Disease Detection and Identification
https://orcid.org/http://orcid.org/0000-0002-5828-8049
Early detection and accurate diagnosis of leaf diseases can be significantly helpful in controlling their spread and improving the yield and quality of production. However, it is challenging to design and develop novel models to analyze complex, noise-contaminated leaf images and infer, preferably in real time, with high accuracy and precision. A Deep Convolutional Neural Network (DCNN) model is proposed, trained and tested from scratch on the subset of the PlantVillage dataset comprising of typical Apple leaf-diseases images. The model uses image data augmentation and image annotation techniques to enhance performance and accuracy. The proposed model was compared with AlexNet, VGG-16, InceptionV3, MobileNetV2, ResNet50, and DenseNet121. It achieved the highest overall accuracy of 99.31% in disease detection with low training time. The low testing time of 5.1 ms per image makes the proposed model suitable for real-time disease detection. Furthermore, the proposed model achieved the maximum precision, recall, F-1 score values and was better than other models on various other performance parameters. The results were validated using a Grad-CAM visualization technique that significantly enhanced the reliability of the suggested model.
An Improved Deep Convolutional Neural Network for Image-Based Apple Plant Leaf Disease Detection and Identification
https://orcid.org/http://orcid.org/0000-0002-5828-8049
Early detection and accurate diagnosis of leaf diseases can be significantly helpful in controlling their spread and improving the yield and quality of production. However, it is challenging to design and develop novel models to analyze complex, noise-contaminated leaf images and infer, preferably in real time, with high accuracy and precision. A Deep Convolutional Neural Network (DCNN) model is proposed, trained and tested from scratch on the subset of the PlantVillage dataset comprising of typical Apple leaf-diseases images. The model uses image data augmentation and image annotation techniques to enhance performance and accuracy. The proposed model was compared with AlexNet, VGG-16, InceptionV3, MobileNetV2, ResNet50, and DenseNet121. It achieved the highest overall accuracy of 99.31% in disease detection with low training time. The low testing time of 5.1 ms per image makes the proposed model suitable for real-time disease detection. Furthermore, the proposed model achieved the maximum precision, recall, F-1 score values and was better than other models on various other performance parameters. The results were validated using a Grad-CAM visualization technique that significantly enhanced the reliability of the suggested model.
An Improved Deep Convolutional Neural Network for Image-Based Apple Plant Leaf Disease Detection and Identification
https://orcid.org/http://orcid.org/0000-0002-5828-8049
Early detection and accurate diagnosis of leaf diseases can be significantly helpful in controlling their spread and improving the yield and quality of production. However, it is challenging to design and develop novel models to analyze complex, noise-contaminated leaf images and infer, preferably in real time, with high accuracy and precision. A Deep Convolutional Neural Network (DCNN) model is proposed, trained and tested from scratch on the subset of the PlantVillage dataset comprising of typical Apple leaf-diseases images. The model uses image data augmentation and image annotation techniques to enhance performance and accuracy. The proposed model was compared with AlexNet, VGG-16, InceptionV3, MobileNetV2, ResNet50, and DenseNet121. It achieved the highest overall accuracy of 99.31% in disease detection with low training time. The low testing time of 5.1 ms per image makes the proposed model suitable for real-time disease detection. Furthermore, the proposed model achieved the maximum precision, recall, F-1 score values and was better than other models on various other performance parameters. The results were validated using a Grad-CAM visualization technique that significantly enhanced the reliability of the suggested model.
An Improved Deep Convolutional Neural Network for Image-Based Apple Plant Leaf Disease Detection and Identification
J. Inst. Eng. India Ser. A
Mahato, Dharmendra Kumar (author) / Pundir, Amit (author) / Saxena, Geetika Jain (author)
Journal of The Institution of Engineers (India): Series A ; 103 ; 975-987
2022-12-01
13 pages
Article (Journal)
Electronic Resource
English
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