Fungus Detection using Convolutional Neural Networks

Authors

  • R. Priyadharshini  UG Scholar, Department of CSE, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India
  • G. B. Nivetha  UG Scholar, Department of CSE, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India
  • G. Kausalya  UG Scholar, Department of CSE, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India
  • P. Anantha Prabha   Assistant Professor, Department of CSE, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India

DOI:

https://doi.org//10.32628/CSEIT195218

Keywords:

Convolutional Neural Networks, Transfer Learning, Activation Function ReLU.

Abstract

The fungus is enormously important for food, human health, and the surrounding. Fungus sign and symptoms in the food, medical science and any non-specific field which is an extremely large area which will result in us the challenging task for the fungus detection. Various traditional, as well as modern computer vision techniques, were applied to meet the challenge in the early days of fungus detection. Another main challenge that has been raised is that obtaining the enormous amount of dataset which is been related to the fungus detection and the processing of it. Despite this challenge, another phase that includes the classification of dataset separately and identifying the fungus presence, owing to all these difficulties, Transfer learning has been used in the approach to get multiplying our dataset. In pursuing this idea, we present a novel fungus dataset of its kind, with the goal of an advancing the State-of-the-art in fungus classification by placing the question of fungus detection.

References

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Published

2019-04-30

Issue

Section

Research Articles

How to Cite

[1]
R. Priyadharshini, G. B. Nivetha, G. Kausalya, P. Anantha Prabha , " Fungus Detection using Convolutional Neural Networks, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 2, pp.205-209, March-April-2019. Available at doi : https://doi.org/10.32628/CSEIT195218