TRANSFER LEARNING APPROACH FOR CLASSIFICATION OF WIDELY USED SPICES

Abstract

People around the world relish variety of food that are flavourful. Spices add flavours to the food without adding any fat or calories. People have used spices for many centuries and are an integral part of our food. In addition to aroma, spices also have anti-bacterial, anti-inflammatory properties and other health-promoting properties. Recognizing spices from images is a challenging problem for a machine as they come in varying sizes and shapes, different colours, high visual similarity, and texture. The classification of spices presents useful applications in the field of Artificial Intelligence-driven food industry, e-commerce, and health care. In the billion-dollar spice industry, image classification of spices finds applications ranging from receiving, processing, labelling, and packaging them. As there is no dataset currently available for spices, in this work, a Spice10 dataset with 2000 images of spices is first created. This study aims to find out whether the accurate classification of spices is possible using computer vision technology. Instead of building models from scratch, a pre-trained transfer learning approach has been implemented in this work to classify the commonly used spices. The images in the dataset are of different sizes and have to be resized and pre-processed before using it with the transfer learning approach. Few different pre-trained networks are modified and used for the image classification of spices. The best classification average accuracy obtained by the VGG16 model is nearly 93.06% which is better than the other models. The high accuracy of the VGG16 model indicates it can be successfully used for the classification of spices.