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Innovations in Information and Communication Technology

Advances in Computing, Communication, Automation and Biomedical Technology

Potato Leaf Disease Classification using Transfer Learning based Modified Xception Model

Rajasekaran Thangaraj and Vishnu Kumar Kaliappan, Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, India

Pandiyan P, Department of Electrical and Electronics Engineering, KPR Institute of Engineering and Technology, India

Abstract

Plant diseases are the essential thing which decreases the quantity as well quality in agricultural field. As a result, the identification and analysis of the diseases are important. The proper classification with least data in deep learning is the most challenging task. In addition, it is tough to label the data manually depending upon the selection criterion. Transfer learning algorithm helps in resolving this kind of problem by means of learning the previous task and then applying capabilities and knowledge to the new task. This work presents the convolution neural network-based model to predict and analysis the potato plant disease using plant village datasets with deep learning algorithms. Transfer learning with feature extraction model is employed to detect the potato plant disease. The results show that improved performance with an accuracy of 98.16%, precision of 98.18%, the recall value of 98.17% and the F1 score value of 98.169 %.

Keywords

Deep Learning, Transfer Learning, Modified- Xception, Potato leaf disease, Fine-Tuning.

Cite this article

Rajasekaran Thangaraj, Vishnu Kumar Kaliappan and Pandiyan P, “Potato Leaf Disease Classification using Transfer Learning based Modified Xception Model”, Innovations in Information and Communication Technology, pp. 438-442, December 2020.

Copyright

© 2020 Rajasekaran Thangaraj, Vishnu Kumar Kaliappan and Pandiyan P. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.