Scopus Indexed Publications

Paper Details


Title
A Computer Vision System for Bangladeshi Local Mango Breed Detection using Convolutional Neural Network (CNN) Models
Author
A. S. M. Farhan Al Haque, Ahmed Al Marouf, Md. Abbas Ali Khan, Md. Riazur Rahman,
Email
farhan.cse@diu.edu.bd
Abstract
Magnifera Indica, traditionally known as mango, is a drupe found around the world in over 500 species. India has produced 19.5 million metric tons of mango in 2017. In Bangladesh, mango has been referred as the national tree and government has included endemic species of mango as geographical index (GI) of Bangladesh. Recognizing specific breeds has become a significant computer vision task. In this paper, we have proposed the convolutional neural network (CNN) based approach for detecting five mango species namely, Chosha, Fazli, Harivanga, Lengra and Rupali from 15000 different images. For better experimentation, we have applied three different models of CNN and analyzed the recognition rates with various criteria. For performance evaluation, we have utilized the classic metrics such as precision, recall, F1-score, ROC and accuracy. Among the experimented three models, the third model, outperformed in terms of accuracy with 92.80%.

Keywords
Megnifera Indica , Mango Species Detection , Computer vision , Convolutional Neural Network (CNN)
Journal or Conference Name
2019 4th International Conference on Electrical Information and Communication Technology (EICT)
Publication Year
2019
Indexing
scopus