Automatic and fast classification of barley grains from images: A deep learning approach

Document Type

Article

Publication Date

1-16-2022

Journal Title

Smart Agricultural Technology

ISSN

eISSN 2772-3755

Keywords

Barley identification, Deep learning, Transfer learning, Feature extraction

Disciplines

Agribusiness | Agricultural Economics | Agricultural Science | Agronomy and Crop Sciences | Food Science | Management Sciences and Quantitative Methods | Natural Resources Management and Policy | Operations and Supply Chain Management | Plant Biology

Abstract

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).

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Digital Object Identifier (DOI)

https://doi.org/10.1016/j.atech.2022.100036