Journal
2019 4TH INTERNATIONAL CONFERENCE ON ELECTRICAL INFORMATION AND COMMUNICATION TECHNOLOGY (EICT)
Volume -, Issue -, Pages -Publisher
IEEE
DOI: 10.1109/eict48899.2019.9068805
Keywords
CNN; Artificial intelligence; Transfer Learning; Plant disease; Pattern recognition; ResNet50
Ask authors/readers for more resources
Plant diseases are a principal threat to the safety of food. In agriculture sectors, it is the greatest challenge to identify plant diseases. The state-of-the-art Convolutional Neural Network (CNN) gives excellent results to solve image classification tasks in computer vision. Transfer Learning enables us to develop a deep CNN network in a most cost effective way. In this work, a Transfer Learning based CNN model was developed for the identification of plant diseases precisely. The dataset, we have used is consists of 70295 training images and 17572 validation images holding 38 different classes of plant leaves images. We have focused mainly on ResNet50 network, a popular CNN architecture as our pre-trained model in Transfer Learning. Additionally, several Transfer Learning architectures were experimented with few other popular pre-trained models (VGG16, VGG19, AlexNet) and compared with the proposed model. The proposed model has given the best performance of 99.80 % training accuracy.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available