Abstract
In the ancient age, human's main source of food was fruits, and still, it is the most preferred healthy food because they are rich in vitamins and proteins. There are many varieties of fruits that are available in the market, and they are different in shapes, colors, and textures. Choosing a quality fruit from many types of fruits is a challenging task to the common man and automated machines. In context, it is necessary to develop an efficient classification system. In the process of development of this proposed system, the 11 types of fruit images are considered from the standard dataset; fruit-360. Total 9551 fruit images belong to 11 types of classes, those are Apple, Banana, Cherry, Grape, Grape blue, Lemon, Mango, Orange, Papaya, Pomegranate, and Watermelon. For training 6685 and for testing 2866 fruit images are utilized from the dataset. The new deep convolutional neural network (DCNN) is designed and it is named; FruitNet-11. The proposed model has obtained 96.15% validation accuracy. This proposed model is compared with popular pre-trained CNN model known as alexnet, and our model has given superior recognition result.
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Raghavendra, Mallappa, S. (2022). Multi-fruit Classification Using a New FruitNet-11 Based on Deep Convolutional Neural Network. In: Dua, M., Jain, A.K., Yadav, A., Kumar, N., Siarry, P. (eds) Proceedings of the International Conference on Paradigms of Communication, Computing and Data Sciences. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-5747-4_53
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DOI: https://doi.org/10.1007/978-981-16-5747-4_53
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