Benchmarking analysis of CNN models for bread wheat varieties-Reference-Cited by-同舟云学术

Benchmarking analysis of CNN models for bread wheat varieties

Published:2022-11-16 Issue:3 Volume:249 Page:749-758
ISSN:1438-2377
Container-title:European Food Research and Technology
language:en
Short-container-title:Eur Food Res Technol

Author:

Yasar Ali^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Industrial and Manufacturing Engineering,Biochemistry,General Chemistry,Food Science,Biotechnology

Link

https://link.springer.com/content/pdf/10.1007/s00217-022-04172-y.pdf

Reference45 articles.

1. Leff B, Ramankutty N, Foley JA (2004) Geographic distribution of major crops across the world. Global Biogeochem Cycles. https://doi.org/10.1029/2003GB002108

2. Awika JM (2011) Major cereal grains production and use around the world. Advances in cereal science implications to food processing and health promotion. ACS Publications, Oxford, pp 1–13

3. Sabanci K, Kayabasi A, Toktas A (2017) Computer vision-based method for classification of wheat grains using artificial neural network. J Sci Food Agric 97(8):2588–2593

4. Unlersen MF et al (2022) CNN–SVM hybrid model for varietal classification of wheat based on bulk samples. Europ Food Rese Technol 12:1–10

5. Vinogradov D, Evsenina M, Novikova A (2021) Improving the conditioning of wheat grain when preparing it for grinding into graded flour. IOP Conference Series: Earth and Environmental Science. IOP Publishing, Bristol

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