Computer vision and deep learning for precision viticulture

L Mohimont, F Alin, M Rondeau, N Gaveau… - Agronomy, 2022 - mdpi.com
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 …

Vineyard yield estimation, prediction, and forecasting: A systematic literature review

A Barriguinha, M de Castro Neto, A Gil - Agronomy, 2021 - mdpi.com
Purpose—knowing in advance vineyard yield is a critical success factor so growers and
winemakers can achieve the best balance between vegetative and reproductive growth. It is …

[HTML][HTML] Deep learning and computer vision for assessing the number of actual berries in commercial vineyards

F Palacios, P Melo-Pinto, MP Diago, J Tardaguila - biosystems engineering, 2022 - Elsevier
The number of berries is one of the most relevant yield components that drives grape
production in viticulture. The goal of this work was to estimate the number of actual berries …

Early yield prediction in different grapevine varieties using computer vision and machine learning

F Palacios, MP Diago, P Melo-Pinto, J Tardaguila - Precision Agriculture, 2023 - Springer
Yield assessment is a highly relevant task for the wine industry. The goal of this work was to
develop a new algorithm for early yield prediction in different grapevine varieties using …

Terroir analysis and its complexity: This article is published in cooperation with Terclim 2022 (XIVth International Terroir Congress and 2nd ClimWine Symposium), 3-8 …

A Bonfante, L Brillante - Oeno One, 2022 - oeno-one.eu
Terroir analysis and its complexity | OENO One Quick jump to page content Main Navigation
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[HTML][HTML] Deep learning modelling for non-invasive grape bunch detection under diverse occlusion conditions

R Íñiguez, S Gutiérrez, C Poblete-Echeverría… - … and Electronics in …, 2024 - Elsevier
Accurately and automatically estimating vineyard yield is a significant challenge. This study
focuses on grape bunch counting in commercial vineyards using advanced deep learning …

Research on the Spatial Dynamic Evolution of Digital Agriculture—Evidence from China

J Meng, B Zhao, Y Song, X Lin - Sustainability, 2024 - mdpi.com
Digital agriculture serves as a pivotal means of ushering in innovative agricultural practices
and achieving sustainable agricultural development. Although agricultural digitalization has …

Phenotyping of silique morphology in oilseed rape using skeletonization with hierarchical segmentation

Z Ma, R Du, J Xie, D Sun, H Fang, L Jiang… - Plant Phenomics, 2023 - spj.science.org
Silique morphology is an important trait that determines the yield output of oilseed rape
(Brassica napus L.). Segmenting siliques and quantifying traits are challenging because of …

Efficient occlusion avoidance based on active deep sensing for harvesting robots

T Sun, W Zhang, X Gao, W Zhang, N Li… - Computers and Electronics …, 2024 - Elsevier
With the increasing shortage of agricultural labor, the development of harvesting robots is
becoming more and more urgent. Most of them require vision to locate the target, however …

Comparing a New Non-Invasive Vineyard Yield Estimation Approach Based on Image Analysis with Manual Sample-Based Methods

G Victorino, RP Braga, J Santos-Victor, CM Lopes - Agronomy, 2022 - mdpi.com
Manual vineyard yield estimation approaches are easy to use and can provide relevant
information at early stages of plant development. However, such methods are subject to …