A New Simple Vision Algorithm for Detecting the Enzymic Browning Defects in Golden Delicious Apples

TL;DR

This paper presents a simple yet effective vision algorithm combined with neural networks to accurately detect enzymic browning defects on Golden Delicious apples, achieving over 97% defect segmentation accuracy and 99% classification accuracy.

Contribution

The study introduces a novel simple vision algorithm for defect detection and combines it with neural networks, improving accuracy in identifying enzymic browning in apples.

Findings

Defect segmentation accuracy of 97.15%.

Neural network classification accuracy of 99.19%.

Effective discrimination between healthy and defective apples.

Abstract

In this work, a simple vision algorithm is designed and implemented to extract and identify the surface defects on the Golden Delicious apples caused by the enzymic browning process. 34 Golden Delicious apples were selected for the experiments, of which 17 had enzymic browning defects and the other 17 were sound. The image processing part of the proposed vision algorithm extracted the defective surface area of the apples with high accuracy of 97.15%. The area and mean of the segmented images were selected as the 2x1 feature vectors to feed into a designed artificial neural network. The analysis based on the above features indicated that the images with a mean less than 0.0065 did not belong to the defective apples; rather, they were extracted as part of the calyx and stem of the healthy apples. The classification accuracy of the neural network applied in this study was 99.19%