Quantifying Nematodes through Images: Datasets, Models, and Baselines of Deep Learning-Reference-Cited by-同舟云学术

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Quantifying Nematodes through Images: Datasets, Models, and Baselines of Deep Learning

Published:2023-11-01 Issue: Volume: Page:2448-2455
ISSN:
Container-title:2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)
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Author:

Yuan Zhipeng¹,Musa Nasamu²,Dybal Katarzyna³,Back Matthew³,Leybourne Daniel⁴,Yang Po⁵

Affiliation:

1. University of Sheffield,Department of Computer Science,Sheffield,The United Kingdom

2. RSK ADAS Ltd High Mowthorpe,Soils, Crops and Water,Malton,The United Kingdom

3. Harper Adams University,Agriculture and Environment Department,Newport,The United Kingdom

4. University of Liverpool,Department of Evolution, Ecology and Behaviour,Liverpool,The United Kingdom

5. University of Sheffield,Department of Computer Science,Sheffield,United Kingdom

Funder

Biotechnology and Biological Sciences Research Council

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10538469/10538471/10538591.pdf?arnumber=10538591

Reference59 articles.

1. The global burden of pathogens and pests on major food crops

2. Genome sequence of the metazoan plant-parasitic nematode Meloidogyne incognita

3. PestDSS: An Integrated Decision Support System for Sustainable Pest Management in Agriculture

4. The state of the world’s land and water resources for food and agriculture: managing systems at risk;Dubois;Earthscan,2011

5. A metagenetic approach to determine the diversity and distribution of cyst nematodes at the level of the country, the field and the individual

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