Computer Vision Technique for Quality Grading of Cardamom Spice

In the Agriculture Industry, spices such as cardamom must be graded and their quality evaluated in order to maintain product standards and increase market value. Traditionally, cardamoms are graded by human labour, 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 cardamom and categorize them as premium, standard, substandard, and defective, based on size, shape, and texture. Researchers primarily prepared 880 images of the above 4 categories under uniform lighting conditions. Pre-processing 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 capabilities for real-time object detection and classification. The proposed method effectively differentiated between various quality grades with an accuracy of 92%, demonstrating its reliability and efficiency. A userfriendly interface was developed using the Streamlit library, enabling 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 solution can be generalized to different use cases in the agricultural industry