Classification of Strawberry Based on Maturity Rate and Size Using Machine Vision

Document Type : Research Paper

Authors

1 Biosystems Engineering Department, College of Agriculture, University of Kurdistan, Sanandaj, Iran

2 Biosystems Engineering Department,College of Agriculture, University of Kurdistan , Sanandaj, Iran

3 Biosystems Engineering Department,College of Agriculture, University of Kurdistan ,Sanandaj , Iran

Abstract

In this article a machine vision system and an artificial neural network (ANN) for classifying the strawberry based on maturity and shape features were used. First an image processing algorithm for extracting the color and shape features was investigated and then for grading the strawberry into three classes based on shape features and three classes of maturity based on colors features were done. The sensitivity analysis indicated that shape grading had highest sensitive to area, parameter, large and minor diameters features. Also a* and S color features had better correlation coefficient than other color features with total solid soluble and therefore were selected as supreme features for grading the strawberry based on maturity. Finally, results demonstrated that the ANN was able to classify with 94.04 and 95.14 total accuracy rate for shape and maturity grading. 

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References

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Iranian Journal of Biosystems Engineering
Volume 50, Issue 1
April 2019
Pages 145-154

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History

  • Receive Date: 13 February 2018
  • Revise Date: 22 July 2018
  • Accept Date: 06 August 2018

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