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

Syed Afaq Ali Shah; Hao Luo; Putu Dita Pickupana; Alexander Ekeze; Ferdous Sohel; Hamid Laga; Chengdao Li; Blakely Paynter; Penghao Wang

doi:10.1016/j.atech.2022.100036

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

Journal article

Open access

Peer reviewed

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

Syed Afaq Ali Shah, Hao Luo, Putu Dita Pickupana, Alexander Ekeze, Ferdous Sohel, Hamid Laga, Chengdao Li, Blakely Paynter and Penghao Wang

Smart Agricultural Technology, Vol.2, p.100036

2022

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

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Abstract

Barley identification

Deep learning

Feature extraction

Transfer learning

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).

Details

Title: Automatic and fast classification of barley grains from images: A deep learning approach
Authors/Creators: Syed Afaq Ali Shah - Edith Cowan University
Hao Luo - Murdoch University
Putu Dita Pickupana - Murdoch University
Alexander Ekeze - Murdoch University
Ferdous Sohel - Murdoch University
Hamid Laga - Murdoch University
Chengdao Li - Murdoch University
Blakely Paynter - Department of Primary Industries and Regional Development
Penghao Wang - Murdoch University
Publication Details: Smart Agricultural Technology, Vol.2, p.100036
Publisher: Elsevier B.V
Identifiers: 991005570082307891
Murdoch Affiliation: Centre for Biosecurity and One Health; Centre for Crop and Food Innovation; School of Information Technology; Centre for Healthy Ageing
Language: English
Resource Type: Journal article

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