Automated Infield Grapevine Inflorescence Segmentation Based on Deep Learning Models-Reference-Cited by-同舟云学术

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Automated Infield Grapevine Inflorescence Segmentation Based on Deep Learning Models

Published:2023-10-27 Issue: Volume: Page:
ISSN:
Container-title:IECAG 2023
language:
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Author:

Moreira Germano¹²^ORCID,Magalhães Sandro Augusto²³^ORCID,dos Santos Filipe Neves²^ORCID,Cunha Mário¹²^ORCID

Affiliation:

1. Faculty of Sciences, University of Porto, Rua do Campo Alegre s/n, 4169-007 Porto, Portugal

2. INESC TEC-Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência, Campus da FEUP, Rua Dr. Roberto Frias s/n, 4200-465 Porto, Portugal

3. Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias s/n, 4200-465 Porto, Portugal

Publisher

MDPI

Link

https://www.mdpi.com/2673-9976/27/1/35/pdf

Reference29 articles.

1. Food and Agriculture Organization of the United Nations (FAO);Mekouar;Yearb. Int. Environ. Law,2020

2. Use of multi-spectral airborne imagery to improve yield sampling in viticulture;Carrillo;Precis. Agric.,2016

3. Clingeleffer, P.R., Martin, S., Dunn, G., and Krstic, M. (2001). Crop Development, Crop Estimation and Crop Control to Secure Quality and Production of Major Wine Grape Varieties: A National Approach: Final Report to Grape and Wine Research, Grape and Wine Research and Development Corporation.

4. Review. Precision Viticulture. Research topics, challenges and opportunities in site-specific vineyard management;Casasnovas;Span. J. Agric. Res.,2009

5. Smart applications and digital technologies in viticulture: A review;Tardaguila;Smart Agric. Technol.,2021

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