Tea Powder Quality Grading System Using Image Processing

Abstract

"Ensuring the quality of tea powder in the production industry is a critical facet of maintaining industry standards. This research introduces an innovative machine learning-based system dedicated to the precise assessment of tea quality. By amalgamating image processing techniques, Convolutional Neural Networks (CNN) and VGG16 Pre-trained model, the system demonstrates efficiency in categorizing tea powder into distinct grades. The study is underpinned by a diverse dataset comprising 5000 images of tea powder across five unique classes. Employing a comprehensive workflow that encompasses image preprocessing and feature extraction, model achieves an impressive accuracy rate of 95.7% in contumely created CNN model (self-composed) and 94% in the model created based on VGG16 model. The architectural design of the system facilitates the seamless integration of image acquisition, processing, and classification, contributing to its overall effectiveness. Results gleaned from this research underscore the system's proficiency in distinguishing between different grades of tea powder, marking it as a valuable tool for tea manufacturers seeking to ensure quality standards. Due to the highest accuracy performance of contumely created CNN model that choose to create the web based application of tea leaf recognising and classification. This research not only addresses the pressing need for efficient and accurate grading in the tea production process but also holds promise for broader applications in automated quality assessment across various industries. As the demand for consistent and high-quality products continues to rise, the presented system stands as a testament to the potential of advanced technologies in meeting these evolving needs."