Simple Vision System for Apple Varieties Classification

Every variety of apple has its particular physical characteristics, which are affected by different pre-harvest factors. Manual classification of these varieties by human labor has several weaknesses, such as the inconsistency, subjectivity, fatigue and different accuracy due to different level of experience of the inspector. This study was aimed to design and evaluate a simple computer-based vision system for recognizing and grading several varieties of apples based on their physical characteristics. Images of apples were taken and were used as training data with different algorithms to extract the particular characteristics of each variety, such as color and shape. The extracted Hue color channels and contour vector were recorded as the reference data and were used to recognize the similar characteristic of those images from the testing data group. The k-nearest neighbors algorithm was used to decide whether an apple belongs to a particular variety. The results show that the recognition rate based on color only was between 84–97% and it was between 5–77% it is based on the shape only. Rotating the image significantly increases the recognition rate (to be between 5 - 69% based on the shape only). Moreover, combining both color and shape characteristics significantly improves the recognition rate.