Mushroom Recognition using Neural Network
An application would be beneficial if it is real time and could give its users enough information. This would be of greater advantage for mobile applications. Mushroom Recognition using Neural Network is a mobile-based application that combined the power of neural network with image processing to recognize mushroom image based on its order and family and if it is edible or inedible/poisonous. It is a multi-class classification program that recognizes mushroom image from 3 orders and 8 families defined in this research. The application used the GrabCut algorithm for image segmentation and Probabilistic Neural Network (PNN) as its classifier that trains and classifies the mushroom image. This application used 133 mushroom images as its training data and obtained an accuracy rate of 92%. This could be used as an educational tool both for Biology students and people in IT fields. It could also help mycologists identify wild mushrooms.
Keywords: Image Segmentation, Probabilistic Neural Network, Machine Learning
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ABOUT THE AUTHORS
Johaira Lidasan
A Faculty of the College of Computer Studies of Notre Dame University, Philippines and a graduate of Master of Computer Science
Martina Tagacay
Dean of the College of Computer Studies, Notre Dame University, Philippines and a graduate of Master of Information Management and Master of Business Administration
Johaira Lidasan
A Faculty of the College of Computer Studies of Notre Dame University, Philippines and a graduate of Master of Computer Science
Martina Tagacay
Dean of the College of Computer Studies, Notre Dame University, Philippines and a graduate of Master of Information Management and Master of Business Administration
