Leveraging Deep Transfer Learning for Precision in Similar Color and Texture-Based Fruit Classification

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Published Dec 17, 2024
https://doi.org/10.47164/ijngc.v15i3.1592
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Volume 15, Issue 3, November 2024

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Anita Bhatt
EC department Gujarat Technological University, Ahmedabad - 382424, Gujarat, India
Maulin Joshi
Electronics and Communication Department, SCET, Surat - 395001, Gujarat, India

Abstract

Worldwide, the most enormously produced fruits, including bananas, papayas, mangoes, and guavas, are found in India. Over the years, agricultural production in India has consistently increased. There is still a massive gap between per capita demand and supply due to losses, including post-harvest. With adequate processing facilities, a clear scope exists to reduce this post-harvest wastage. In recent years, research in cutting-edge technology like computer vision (CV), Artificial Intelligence, and image processing has played an important role in sorting as well as grading fruits. Fruits in similar colors and textures increase the difficulty of identification. Deep learning networks are used to adapt and recognize complex patterns, especially in visual tasks. Utilizing deep transfer learning facilitates achieving excellent results expeditiously. This paper uses the deep transfer learning approach to classify fruits with similar color and texture, namely guava, avocado, lime, apple, pear, mango, and pomelo sweetie. This study introduces a novel model derived from integrating DenseNet, MobileNet, and EfficientNet architectures. The model’s performance is systematically assessed using different optimizers, contributing to a comprehensive evaluation of its efficacy. Simulation findings indicate that MobileNetV1 when paired with the Adam optimizer, surpasses other models in terms of training time, accuracy, and testing time.

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite
Bhatt, A., & Joshi, M. (2024). Leveraging Deep Transfer Learning for Precision in Similar Color and Texture-Based Fruit Classification. International Journal of Next-Generation Computing, 15(3). https://doi.org/10.47164/ijngc.v15i3.1592
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