Analysis of Convolutional Neural Network Using Pre-Trained Squeezenet Model for Classification of Thermal Fruit Images

Abstract

The process of classification of fruits and their manual gradation, is an age-old phenomena taking place since centuries, and has been there in almost all the fruit markets. But with the advent of handy digital cameras and improvement in the image capturing and processing systems, the age-old manual classification methods have been fast replaced by the automatic classification techniques supported by computer vision based techniques. The thermal imaging is one of the popular non-destructive methodology applied to classify fruits. In the automatic classification of images machine learning techniques are being replaced by deep learning techniques because of their higher classification accuracy rates. In the present research work, a thermal image dataset has been created for eleven different varieties of fruits and classified using pre-trained convolutional neural networks (CNN). In our work, instead of building and training a CNN from start, we have utilized a pre-built and pre-trained network via transfer learning. Parameters like sensitivity, specificity, F1-score, precision and accuracy of CNN for transfer learning of fruit classification have been tested. The results reveal that our classification system has Top-1 and Top-5 accuracy as 96.54% and 100% respectively with the training time of 9.21 minutes using SqueezeNet model.