Fruit Classification and Defect Detection System Using Faster Region Convolutional Neural Network
Malaysia is still a net importer of both fresh and refined fruits and the fresh fruit export price is around USD 174 million. Various methods are presented to improve fruit and vegetable production. Using the latest technologies and knowledge-based production systems, conventional farms will b...
Saved in:
Main Author: | Aziz, Amir Aizat |
---|---|
Format: | Final Year Project |
Language: | English |
Published: |
IRC
2019
|
Subjects: | |
Online Access: | http://utpedia.utp.edu.my/20890/1/AMIR%20AIZAT_23010.pdf http://utpedia.utp.edu.my/20890/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Badminton player detection using faster region convolutional neural network
by: Rahmad, N. A., et al.
Published: (2019) -
Multi-Granularity Tooth Analysis via Faster Region-Convolutional Neural Networks for Effective Tooth Detection and Classification
by: AbuSalim, Samah, et al.
Published: (2023) -
Enhanced faster region-based convolutional neural network for oil palm tree detection
by: Liu, Xinni
Published: (2021) -
Dragon Fruit Classification using Convolutional Neural Network
by: Tan, Ying Zhi
Published: (2020) -
Retinal Microvascular Feature Extraction Using Faster Region-based Convolutional Neural Network
by: Mohammed Enamul, Hoque
Published: (2021)