Visual Dataset for Spice Recognition: Enhancing Classification of Common Spices

This research on "Spice Dataset Classification" investigates machine learning methods for accurately classifying spices from a dataset of 4,134 images. The dataset includes images of Black Cardamom, Black Pepper, Cinnamomum Tamala, Cinnamon, Clove, Fenugreek, Garlic, and Red Chili. This study aims to advance automated spice identification, benefiting food quality control and culinary science. The distribution of spice samples is as follows:

Black Cardamom: 424 samples
Black Pepper: 1100 samples
Cinnamomum Tamala: 314 samples
Cinnamon: 301 samples
Clove: 958 samples
Fenugreek: 414 samples
Garlic: 309 samples
Red Chili: 314 samples

Purpose:
The purpose of this research is to develop a reliable and accurate machine learning model for the classification of spices based on image data. By automating the recognition process, this study aims to facilitate the identification of various spices—such as Black Cardamom, Black Pepper, Cinnamomum Tamala, Cinnamon, Clove, Fenugreek, Garlic, and Red Chili thereby supporting applications in food quality control, inventory management, and culinary research. This work seeks to enhance efficiency in spice identification, reduce human error in classification tasks, and contribute to advancements in food science and automated recognition systems.