What question did this study set out to answer?

This research aims to compare different deep learning architectures in classifying fruit quality using transfer learning.

April 12, 2026Open Access

A Comparative Study of Deep Learning Architectures (MobileNetV2, EfficientNetB3, and InceptionV3) for Real-World Fruit Quality Classification Using Transfer Learning

Key Points

This research aims to compare different deep learning architectures in classifying fruit quality using transfer learning.
Utilized deep learning architectures: MobileNetV2, EfficientNetB3, and InceptionV3.
Applied transfer learning techniques to enhance classification accuracy.
Evaluated model performance based on fruit quality classification.
Demonstrated varying accuracy levels among the deep learning models.
MobileNetV2 showed a competitive performance in classifying fruit quality.
EfficientNetB3 outperformed other models in specific fruit categories.

Abstract

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Suneeta Devi

Dr. Rohit Kumar Singhal

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INTERNATIONAL JOURNAL OF CREATIVE RESEARCH THOUGHTS

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A Comparative Study of Deep Learning Architectures (MobileNetV2, EfficientNetB3, and InceptionV3) for Real-World Fruit Quality Classification Using Transfer Learning

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