Convolutional neural network–based automated quality grading of cinnamon peel

Abstract

Ceylon cinnamon, endemic to Sri Lanka, holds significant culinary, medicinal, and economic value, reinforcing the country’s position as the leading global exporter of authentic cinnamon. However, current processing techniques, particularly manual peeling and quill preparation, are highly labor dependent and introduce quality inconsistencies, limiting scalability. While national efforts increasingly promote value addition and product diversification, maintaining consistent export-grade quality remains challenging. Recent research highlights the potential of technological advancement within the cinnamon value chain, with image processing applied for maturity estimation, and diameter-based grading [1], [2]. Additionally, deep learning models, particularly Convolutional Neural Networks (CNNs), have demonstrated strong performance in agricultural applications such as disease diagnosis and adulteration detection [3], [4]. Visual grading of cinnamon based on peel texture, surface defects and residual bark remains subjective and inconsistent. This study addresses this gap by proposing an objective, scalable deep learning–based automated visual inspection framework.