A comparative study of distinguishing apple cultivars and a clone based on features of selected fruit parts and leaves using image processing and artificial intelligence

Abstract

This study aimed to identify the most useful white-fleshed apple samples to distinguish apple cultivars and a clone. Whole apples, apple slices, seeds, and leaves belonging to ‘Free Redstar’, clone 118, ‘Ligolina’, ‘Pink Braeburn’, and ‘Pinokio’ were imaged using a digital camera. The texture parameters were extracted from images in color channels L, a, b, R, G, B, X, Y, Z, U, V, and S. The classification models were built using traditional machine learning algorithms. Models developed using selected image seed textures allowed the classification of apple cultivars and a clone with the highest average accuracy of up to 97.4%. The apple seeds ‘Free Redstar’ were distinguished with the highest accuracy, equal to 100%. Machine learning models built based on the textures of apple skin allowed for the clone and cultivar classification with slightly lower correctness, reaching 94%. Meanwhile, the average accuracies for models involving selected flesh and leave textures reached 86.4% and 88.8%, respectively. All the most efficient models for classifying individual apple fruit parts and leaves were developed using Multilayer Perceptron. However, models combining selected image textures of apple skin, slices (flesh), seeds, and leaves produced the highest average accuracy of up to 99.6% in the case of Bayes Net. Thus, it was found that including features of different parts of apple fruit and apple leaves in one model can allow for the correct distinguishing of apples in terms of cultivar and clone.