Abstract
Thinking about the quick progression of humankind, a critical concern is given to the nourishments that we devour. In the proposed research, we propose a fruit classification system using two popular deep learning frameworks, CNN and ResNet50. With a total data size of 28, 283 fruit images (41 different categories), both the models performed exceptionally well on the network architectures, where the test accuracy achieved by CNN and ResNet50 V2 was \(97.48\%\) and \(98.89\%\) respectively, therefore obtaining a state of the art results when compared to research findings in accordance to the same dataset. The training directory was augmented, and a validation set of \(12\%\) was set to monitor the consistency and reliability of the results achieved. A number of evaluation parameters like Precision, Sensitivity, F-Scores, and ROC were calculated to analyze the results obtained. Furthermore, we took advantage of a predictor model to visualize the results on a test set with 3, 615 images. Our code can be found in the mentioned link: https://github.com/Sourodip-ghosh123/Fruits-360.
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Bandyopadhyay, A., Ghosh, S., Bose, M., Kessi, L., Gaur, L. (2023). Supervised Neural Networks for Fruit Identification. In: Santosh, K., Goyal, A., Aouada, D., Makkar, A., Chiang, YY., Singh, S.K. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2022. Communications in Computer and Information Science, vol 1704. Springer, Cham. https://doi.org/10.1007/978-3-031-23599-3_16
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