Transfer Learning-based Approach for the Detection of Fruit Freshness
Keywords:
Transfer Learning, Fruit Freshness Detection, GoogLeNet, Inception Modules, Smart Agriculture.Abstract
The detection of fruit freshness is a critical aspect of the agricultural supply chain, ensuring quality control, reducing wastage, and enhancing consumer satisfaction. This research proposes a Transfer Learning-based Approach for the Detection of Fruit Freshness utilizing the GoogLeNet architecture, a deep convolutional neural network known for its Inception modules which capture multi-scale
features effectively.
References
. Mukhiddinov, M.; Muminov, A.; Cho, J. Improved Classification Approach for Fruits and Vegetables Freshness Based on Deep Learning. Sensors 2022, 22, 8192.
. Valentino, F.; Cenggoro, T.W.; Pardamean, B. A design of deep learning experimentation for fruit freshness detection. IOP Conf. Ser. Earth Environ. Sci. 2021, 794, 012110.
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Published
2024-04-23
How to Cite
Dr. Thanveer Jahan, E. Manikeerthan, K. Sai Teja, Ch. Chandra siddhartha, Gaddala Subhash. (2024). Transfer Learning-based Approach for the Detection of Fruit Freshness. Journal of Computational Analysis and Applications (JoCAAA), 34(4), 623–636. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2344
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