Apple Varieties Classification Using Deep Features and Machine Learning

Abstract

Having the advantages of speed, suitability and high accuracy, computer vision has been effectively utilized as a non-destructive approach to automatically recognize and classify fruits and vegetables, to meet the increased demand for food quality-sensing devices. Primarily, this study focused on classifying apple varieties using machine learning techniques. Firstly, to discern how different convolutional neural network (CNN) architectures handle different apple varieties, transfer learning approaches, using popular seven CNN architectures (VGG16, VGG19, InceptionV3, MobileNet, Xception, ResNet150V2 and DenseNet201), were adopted, taking advantage of the pre-trained models, and it was found that DenseNet201 had the highest (97.48%) classification accuracy. Secondly, using the DenseNet201, deep features were extracted and traditional Machine Learning (ML) models: support vector machine (SVM), multi-layer perceptron (MLP), random forest classifier (RFC) and K-nearest neighbor (KNN) were trained. It was observed that the classification accuracies were significantly improved and the best classification performance of 98.28% was obtained using SVM algorithms. Finally, the effect of dimensionality reduction in classification performance, deep features, principal component analysis (PCA) and ML models was investigated. MLP achieved an accuracy of 99.77%, outperforming SVM (99.08%), RFC (99.54%) and KNN (91.63%). Based on the performance measurement values obtained, our study achieved success in classifying apple varieties. Further investigation is needed to broaden the scope and usability of this technique, for an increased number of varieties, by increasing the size of the training data and the number of apple varieties.

Enhancing Apple Cultivar Classification Using Multiview Images

“…These results are in line with results from similar apple recognition approaches in the literature, where a classification accuracy of 90% and above is typically achieved (e.g., in [43,52]). However, these results are obtained partially on simpler datasets with fewer cultivars and distinct features to distinguish the classes and are therefore not directly comparable.…”

Texture and Flag Color Extraction in Backpropagation Neural Network Architecture

“…Conducted research on the classification of apple characteristics among three classifiers, CNN had the best performance with a detection accuracy of 98% for both apple varieties, followed by SVM and RF (Benmouna et al, 2022;Wu et al, 2023). This research shows that SIRI, coupled with machine learning algorithms, can be a new, versatile and effective modality for fruit defect detection (Elahi et al, 2023;Taner et al, 2024;Ukwuoma et al, 2022).…”