Paper Info
Title
Classification and quality evaluation of tobacco leaves based on image processing and fuzzy comprehensive evaluation.
Abstract
Most of classification, quality evaluation or grading of the flue-cured tobacco leaves are manually operated, which relies on the judgmental experience of experts, and inevitably limited by personal, physical and environmental factors. The classification and the quality evaluation are therefore subjective and experientially based. In this paper, an automatic classification method of tobacco leaves based on the digital image processing and the fuzzy sets theory is presented. A grading system based on image processing techniques was developed for automatically inspecting and grading flue-cured tobacco leaves. This system uses machine vision for the extraction and analysis of color, size, shape and surface texture. Fuzzy comprehensive evaluation provides a high level of confidence in decision making based on the fuzzy logic. The neural network is used to estimate and forecast the membership function of the features of tobacco leaves in the fuzzy sets. The experimental results of the two-level fuzzy comprehensive evaluation (FCE) show that the accuracy rate of classification is about 94% for the trained tobacco leaves, and the accuracy rate of the non-trained tobacco leaves is about 72%. We believe that the fuzzy comprehensive evaluation is a viable way for the automatic classification and quality evaluation of the tobacco leaves.
Year
DOI
Venue
2011
10.3390/s110302369
SENSORS
Keywords
Field
DocType
image analysis,tobacco leaf,fuzzy sets,fuzzy comprehensive evaluation,artificial neural network
Data mining,Machine vision,Grading (education),Fuzzy logic,Image processing,Fuzzy set,Artificial intelligence,Engineering,Artificial neural network,Digital image processing,Membership function,Machine learning
Journal
Volume
Issue
ISSN
11
3
1424-8220
Citations 
PageRank 
References 
2
0.42
7
Authors
2
Name
Order
Citations
PageRank
Fan Zhang1206.23
Xinhong Zhang220.42