A novel and proposed comprehensive methodology using deep convolutional neural networks for flue cured tobacco leaves classification

Abstract

In this paper, a solution is defined based on convolutional neural networks (CNN) for the grading of flue-cured tobacco leaves. A performance analysis of CNN on 120 samples of cured tobacco leaves is reduced from 1450 × 1680 Red–Green–Blue (RGB) to 256 × 256, consisting 16, 32 and 64 feature kernels for hidden layers respectively. The neural network comprised of four hidden layers where the performance of convolution and pooling on first three hidden layers and fourth layer a fully connected as in regular neural networks. Max pooling technique (MPT) is used in the proposed model to reduce the size. Classification is done on three major classes’ namely Class-1, Class-2 and Class-3 for obtaining global efficiency of 85.10% on the test set consisting about fifteen images of each cluster. A comparative study is performed on the results from the proposed model with existing models of tobacco leaf classification.