부산대학교 도서관

Australia has a reputation for producing a reliable supply of high-quality barley in a contaminant-free climate. As a result, Australian barley is highly sought after by malting, brewing, distilling, and feed industries worldwide. Barley is traded as a variety-specific commodity on the international market for food, brewing and distilling end-use, as the intrinsic quality of the variety determines its market value. Manual identification of barley varieties by the naked eye is challenging and time-consuming for all stakeholders, including growers, grain handlers and traders. Current industrial methods for identifying barley varieties include molecular protein weights or DNA based technology, which are not only time-consuming and costly but need specific laboratory equipment. On grain receival, there is a need for efficient and low-cost solutions for barley classification to ensure accurate and effective variety segregation. This paper proposes an efficient deep learning-based technique that can classify barley varieties from RGB images. Our proposed technique takes only four milliseconds to classify an RGB image. The proposed technique outperforms the baseline method and achieves a barley classification accuracy of 94% across 14 commercial barley varieties (some highly genetically related).