Artificial intelligence analysis of contributive factors in determining blackleg disease severity in canola farmlands-Reference-Cited by-同舟云学术

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Artificial intelligence analysis of contributive factors in determining blackleg disease severity in canola farmlands

Published:2023-12-15 Issue: Volume: Page:1-14
ISSN:0706-0661
Container-title:Canadian Journal of Plant Pathology
language:en
Short-container-title:Canadian Journal of Plant Pathology

Author:

Zhao Liang¹,Harding Michael W.²,Peng Gary³,Lange Ralph⁴,Walkowiak Sean⁵,Fernando W. G. Dilantha¹^ORCID

Affiliation:

1. Department of Plant Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada

2. Crop Diversification Centre South, Alberta Agriculture Forestry and Rural Economic Development, Brooks, AB T1R 1E6, Canada

3. Saskatoon Research and Development Centre, Agriculture and Agri-Food Canada, Saskatoon, SK 7N 0X2, Canada

4. Bio-Industrial Research Laboratories, InnoTech Alberta Inc, Vegreville, AB T9C 1T4, Canada

5. Grain Research Laboratory, Canadian Grain Commission, Winnipeg, MB R3T 6C5, Canada

Funder

Government of Canada

Publisher

Informa UK Limited

Subject

Plant Science,Agronomy and Crop Science

Link

https://www.tandfonline.com/doi/pdf/10.1080/07060661.2023.2290039

Reference40 articles.

1. Effects of temperature on development and progression in rape of crown canker caused by Leptosphaeria maculans

2. Canola Council of Canada. 2020. Flea beetles. Canola encyclopedia; [accessed 2022 Mar 31]. https://www.canolacouncil.org/canola-encyclopedia/insects/flea-beetles/.

3. Effects of seeding date and canola species on seedling damage by flea beetles in three ecoregions

4. Classification of hyperspectral remote-sensing data with primal SVM for small-sized training dataset problem

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