Computer Vision and Deep Learning for Precision Viticulture

During the last decades, researchers have developed novel computing methods to help viticulturists solve their problems, primarily those linked to yield estimation of their crops. This article aims to summarize the existing research associated with computer vision and viticulture. It focuses on appr...

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Veröffentlicht in:Agronomy (Basel) Jg. 12; H. 10; S. 2463
Hauptverfasser: Mohimont, Lucas, Alin, François, Rondeau, Marine, Gaveau, Nathalie, Steffenel, Luiz Angelo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.10.2022
MDPI
Schlagworte:
Agricultural production
Algorithms
Artificial intelligence
Automation
Classification
Color imagery
Computational linguistics
Computer Science
Computer vision
Data mining
Decision making
Deep learning
Digital cameras
Distributed, Parallel, and Cluster Computing
Flowers
Fruits
Grapes
Grapevines
Image analysis
Image processing
Innovations
Language processing
Logic programming
Machine learning
Machine vision
Natural language interfaces
Natural language processing
Neural networks
Sensors
Variables
Viticulture
yield modeling
France
Germany
yield modeling
viticulture
computer vision
ISSN:2073-4395, 2073-4395
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:During the last decades, researchers have developed novel computing methods to help viticulturists solve their problems, primarily those linked to yield estimation of their crops. This article aims to summarize the existing research associated with computer vision and viticulture. It focuses on approaches that use RGB images directly obtained from parcels, ranging from classic image analysis methods to Machine Learning, including novel Deep Learning techniques. We intend to produce a complete analysis accessible to everyone, including non-specialized readers, to discuss the recent progress of artificial intelligence (AI) in viticulture. To this purpose, we present work focusing on detecting grapevine flowers, grapes, and berries in the first sections of this article. In the last sections, we present different methods for yield estimation and the problems that arise with this task.
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ISSN:2073-4395
2073-4395
DOI:10.3390/agronomy12102463