Computer vision and machine learning for viticulture technology

KP Seng, LM Ang, LM Schmidtke, SY Rogiers - IEEE Access, 2018 - ieeexplore.ieee.org
This paper gives two contributions to the state-of-the-art for viticulture technology research.
First, we present a comprehensive review of computer vision, image processing, and …

A critical review on the applications of artificial neural networks in winemaking technology

OA Moldes, JC Mejuto, R Rial-Otero… - Critical reviews in …, 2017 - Taylor & Francis
Since their development in 1943, artificial neural networks were extended into applications
in many fields. Last twenty years have brought their introduction into winery, where they …

Grape maturity estimation based on seed images and neural networks

A Zuñiga, M Mora, M Oyarce, C Fredes - Engineering Applications of …, 2014 - Elsevier
The grape phenolic maturity is one of the most important parameters to determine the
optimal time for harvest. In this paper we propose an innovative methodology for the …

Predictive models for smart vineyards

FR Luttich - 2019 - scholar.sun.ac.za
We investigate the application of machine learning algorithms to the predictive analysis of
environmental datasets compiled from two distinct vineyards. These datasets include the soil …

Olive Ripening Phase Estimation based on Neural Networks

M Mora, J Aliaga, C Fredes - … (CAI 2017)-JAIIO 46-43 CLEI …, 2017 - sedici.unlp.edu.ar
Color of fruits is a relevant parameter to determine ripeness and optimal harvest time. For
olives 6 ripening phases based on skin color distribution have been defined. A widely used …

Fruit Maturity Estimation based on Color Scales

M Mora, M Oyarce, C Fredes - XX Congreso Argentino de …, 2014 - sedici.unlp.edu.ar
Color is an important parameter used to estimate fruit maturity and optimal harvest time. In
this paper a general methodology for estimating fruit maturity based on the development of a …

A Method to estimate Grape Phenolic Maturity based on Color Features

F Avila, M Mora, A Zuniga, M Oyarce… - XX Congreso Argentino …, 2014 - sedici.unlp.edu.ar
The phenolic ripeness of the grape is one of the most important parameters to determine the
optimal time for harvest. A recent line of studies proposes visual seed inspection by a trained …

[引用][C] Estimation of Grape Maturity Based on Neural Networks

A Zúniga, M Mora, M Oyarce, C Fredes - … of The Chilean Computer Science Society …, 2013