A Comparative Study of Convolutional Neural Networks and Vision Transformers for Fruit Classification
Article Sidebar
Convolutional Neural Networks,
Fruit Classification,
Image Classification,
Vision Transformer
Main Article Content
Malik Jawarneh
Arief Marwanto
Dedy Syamsuar
Maivi Kusnandar
Abstract
Background of study: Accurate fruit classification is vital for agricultural automation, yet traditional methods are often subjective and inefficient. Convolutional Neural Networks (CNNs) are effective but struggle with global context in fine-grained tasks. Vision Transformers (ViTs), inspired by NLP models, offer global attention mechanisms that may improve classification in complex scenarios.
Aims and scope of paper: This study compares the performance of EfficientNet-B0 (a CNN model) and ViT-B/16 (a Transformer model) on a fruit classification task involving five fruit types. The goal is to evaluate their strengths and weaknesses under controlled experimental conditions using a moderately sized dataset.
Methods: A dataset of 10,000 fruit images was preprocessed with standard augmentation techniques and split into training and validation sets. Both models were fine-tuned using pretrained weights. Performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrices.
Result: EfficientNet-B0 achieved higher overall accuracy (94%) than ViT-B/16 (92%). The CNN model performed consistently across all classes, particularly excelling in bananas and strawberries. ViT-B/16 showed superior results for strawberries but struggled with apples. Confusion matrices revealed class-specific strengths and weaknesses.
Conclusion: EfficientNet-B0 is better suited for general fruit classification due to its balanced performance, while ViT-B/16 excels in capturing fine-grained visual features. A hybrid approach may leverage both models’ strengths for enhanced performance in real-world applications.
Article Details
Copyright (c) 2025 Malik Jawarneh, Arief Marwanto, Dedy Syamsuar, Maivi Kusnandar
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
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