Computer Vision Technique for Quality Grading of Cardamom Spice

Abstract

In the agriculture industry, spices such as cardamom must be graded and their quality evaluated to maintain product standards and increase market value. Traditionally, cardamoms are graded manually, but this process is time-consuming, inconsistent, and prone to human error due to fatigue and subjectivity. This study proposes computer vision-based techniques to detect and categorize cardamom into four grades—premium, standard, substandard, and defective—based on size, shape, and texture. Researchers prepared 880 images representing these categories under uniform lighting conditions. Preprocessing techniques, including resizing, noise addition, and augmentation, were applied to enhance the dataset for robust model training. YOLOv8 was utilized for the detection and grading of cardamoms due to its capability for real-time object detection and classification. The proposed method effectively differentiated between quality grades with an accuracy of 92%, demonstrating reliability and efficiency. A user-friendly interface was developed using the Streamlit library, allowing users to upload images and obtain grading results instantly. This system offers a practical and scalable solution for improving quality control processes in the agricultural sector. Future work aims to incorporate larger datasets, texture analysis, and integration with automated machinery to further enhance its applicability in industrial settings. The proposed solution can also be generalized to other use cases in the agricultural industry.