Open AccessFungus Detection using Convolutional Neural Networks
Author(s) -
R. Priyadharshini,
G. Nivetha,
G. Kausalya,
P. Anantha Prabha
Publication year - 2019
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit195218
Subject(s) - fungus , computer science , convolutional neural network , artificial intelligence , field (mathematics) , task (project management) , machine learning , data science , biology , mathematics , engineering , botany , systems engineering , pure mathematics
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.