Multi-Fruit Classification and Grading Using a Same-Domain Transfer Learning Approach

Abstract

The simultaneous classification and grading of fruits are essential yet underexplored facets of computer vision in agricultural automation. This study proposes the application of same-domain transfer learning using the EfficientNetV2 architecture to facilitate multi-fruit classification and grading. Our dual-model framework initially employs EfficientNetV2 to distinguish between six fruit types—bananas, apples, oranges, pomegranates, limes, and guavas—within the FruitNet dataset. Subsequently, the learned parameters are transferred to a second model, which focuses on grading the quality of the fruits. To address the class imbalance in the dataset, we incorporate a combination of AugMix, CutMix, and MixUp, significantly improving model generalization. Our experiments demonstrate robust performance, with classification and grading achieving an average test accuracy of 99%. These findings affirm the utility of same-domain transfer learning in enhancing grading accuracy using knowledge gained from classification tasks. The study shows promising potential for integrating this approach into machine vision systems to advance agricultural automation. Moving forward, this approach could be scaled to address broader cultivation challenges through the continued development of fine-grained visual analysis capabilities. The code is available on GitHub: MFCG

Keywords

• Convolutional neural networks
• deep learning
• fruits
• fruit classification
• fruit quality grading
• transfer learning