Paper Info
Title
Deep Learning Based Improved Classification System for Designing Tomato Harvesting Robot.
Abstract
Maturity level-based classification system plays an essential role in the design of tomato harvesting robot. Traditional knowledge-based systems are unable to meet the current production management requirements of precision picking, because they are time-consuming and have low accuracy. Our research proposes an improved deep learning-based classification method that improves the accuracy and scalability of tomato ripeness with a small amount of training data. This study was on the relationship between different dataset augmentation methods and prediction results of final classification task. We implemented classification systems based on convolutional neural network (CNN), by training and validating the model on different augmented datasets and tried to choose an optimal augmentation method for datasets. The experimental results showed an average accuracy of 91.9% with a less than 0.01-s prediction time. Compared to the existing methods, our solution achieved better prediction results both in terms of accuracy and time consumption. Moreover, this is a versatile method and can be extended to other related fields.
Year
DOI
Venue
2018
10.1109/ACCESS.2018.2879324
IEEE ACCESS
Keywords
Field
DocType
Convolutional neural network,classification,data augmentation,tomato harvesting robot,deep learning
Training set,Production manager,Machine vision,Task analysis,Convolutional neural network,Computer science,Artificial intelligence,Deep learning,Robot,Machine learning,Distributed computing,Scalability
Journal
Volume
ISSN
Citations 
6
2169-3536
1
PageRank 
References 
Authors
0.43
0
6
Name
Order
Citations
PageRank
Li Zhang142.18
Jingdun Jia210.43
Guan Gui3641102.53
Xia Hao410.77
Wanlin Gao583.35
Minjuan Wang630441.52