A novel approach to detect and classify fruits using ShuffleNet V2

Abstract

In the proposed context, we show an identification and classification approach of organic products between 41 unique classes. We have utilized a pre-trained Convolutional Neural Network design, the ShuffleNet V2, chosen as for the proficient presentation extent of building convolutional blocks at ease, by using more feature channels. The model, when tried on the proposed dataset, accomplished a test accuracy of 96.24% accordingly making a stride further in the exploration proposed by past authors surveying the organic product detection via Convolutional learning and feature re-usability technique. The outcomes are assessed utilizing various assessment parameters, like Precision, Sensitivity, F-Score, and ROC score. Moreover, a visual of the predicted images was performed to anticipate the evaluation.