Citrus Fruit Quality Classification using Support Vector Machines

Abstract

The large-scale fruit selection process is still manual or semi-automatic, mainly in small industries. This fact can lead to errors during the sorting of good fruits. Thus, this paper proposes an application using computer vision and machine learning to improve this task. The genus studied was the citrus, more specific the orange, one of the most produced fruit in Brazil. However, the methodology used can be applied on any fruit which quality can be measured by vision. The initial step was the construction of the learning space, consisting of image acquisition, pre-processing and features extraction. After the construction, the learning phase begins, consisted of the training of the support vector machine model, and then, statistical methods were used to validate the model. As the final result, it achieved the accuracy of 97.3% in fruit classify.