Popular Food Spices Classification of Bangladesh Using Transfer Learning Approach

Abstract

Classifying twenty distinct Spice varieties will be accomplished with the implementation of the transfer learning (TL) approach. Bengali cuisine is incredibly flavorful and aromatic, and the taste comes mainly due to the use of spices. Spices are the sole reason why Bengali cuisine is so popular. However, background studies observe that there is a severe lack of acceptable datasets and paperwork for popular spices used in the majority of Bangladeshi cuisine. Spice classification is significant because it helps upcoming generations by enabling the recognition of spice varieties without any prior knowledge. To resolve the Spice recognition problem, collecting a significant dataset on the specific number of images per class which includes 500 images and a total of twenty spice varieties like Clove, Black cardamom, Black pepper, Cumin, and Nutmeg etc. The dataset will then be preprocessed by applying techniques such as Image resizing, and noise reduction. The TL approach will be used to solve the challenge since it is the most efficient in terms of processing energy, execution speed, and real-time analysis. Out of all the experiments, the proposed model "MobileNetV2" outperformed all other models in efficiency, achieving an accuracy score of 99.70% in identifying spice categories. The collected dataset will frequently be helpful for further studies that classify regional spice variations in Bangladesh.