Double-channel on-line automatic fruit grading system based on computer vision

2007 ◽  
Author(s):  
Junxiong Zhang ◽  
Yi Xun ◽  
Wei Li ◽  
Cong Zhang
Author(s):  
Neha Janu ◽  
Ankit Kumar

This work proposed a recognition system capable of identifying an Indian fruit from among a set, established in a database, using computer vision techniques. The investigation made it possible to compare the image color models, together with the size and shape characteristics previously used by different researcher. For the class of fruits defined in this investigation, it was determined that the characteristics that best described them were the average values of the RGB channels and the length of the major and minor axes when the image fusion technique is used, a process that allowed obtaining results with an accuracy equal to 92% in the tests carried out, finding that not always selecting a greater number of variables to form the descriptor vector allows the classifiers to deliver a more accurate response. In this sense it is important to consider that among the study variables a low dependency or correlation value.


Author(s):  
C. J. Prabhakar ◽  
S. H. Mohana

The automatic inspection of quality in fruits is becoming of paramount importance in order to decrease production costs and increase quality standards. Computer vision techniques are used in fruit industry for fruit grading, sorting, and defect detection. In this chapter, we review recent approaches for automatic inspection of quality in fruits using computer vision techniques. Particularly, we focus on the review of advances in computer vision techniques for automatic inspection of quality of apples based on surface defects. Finally, we present our approach to estimate the defects on the surface of an apple using grow-cut and multi-threshold based segmentation technique. The experimental results show that our method effectively estimates the defects on the surface of apples significantly more effectively than color based segmentation technique.


2013 ◽  
Vol 115 (1) ◽  
pp. 99-114 ◽  
Author(s):  
Soleiman Hosseinpour ◽  
Shahin Rafiee ◽  
Seyed Saeid Mohtasebi ◽  
Mortaza Aghbashlo

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