Next Article in Journal
Evaluation of Air Temperature, Photoperiod and Light Intensity Conditions to Produce Cucumber Scions and Rootstocks in a Plant Factory with Artificial Lighting
Next Article in Special Issue
A Sustainable Viticulture Method Adapted to the Cold Climate Zone in China
Previous Article in Journal
In-Vivo In-Vitro Screening of Ocimum basilicum L. Ecotypes with Differential UV-B Radiation Sensitivity
Article

Artificial Intelligence and Novel Sensing Technologies for Assessing Downy Mildew in Grapevine

1
Televitis Research Group, University of La Rioja, 26006 Logroño, Spain
2
Institute of Grapevine and Wine Sciences, Consejo Superior de Investigaciones Científicas, Gobierno de La Rioja, University of La Rioja, 26007 Logroño, Spain
3
Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Massimo Bertamini
Horticulturae 2021, 7(5), 103; https://doi.org/10.3390/horticulturae7050103
Received: 16 April 2021 / Revised: 29 April 2021 / Accepted: 4 May 2021 / Published: 8 May 2021
(This article belongs to the Special Issue Advances in Viticulture Production)
Plant diseases and pests cause a large loss of world agricultural production. Downy mildew is a major disease in grapevine. Conventional techniques for plant diseases evaluations are time-consuming and require expert personnel. This work investigates novel sensing technologies and artificial intelligence applications for assessing downy mildew in grapevine under laboratory conditions. In our methodology, machine vision is applied to assess downy mildew sporulation, while hyperspectral imaging is used to explore its potential capability towards early detection of this disease. Image analysis applied to RGB leaf disc images is used to estimate downy mildew (Plamopara viticola) severity in grapevine (Vitis vinifera L. cv Tempranillo). A determination coefficient (R2) of 0.76 ** and a root mean square error (RMSE) of 20.53% are observed in the correlation between downy mildew severity by computer vision and expert’s visual assessment. Furthermore, an accuracy of 81% is achieved to detect downy mildew early using hyperspectral images. These results indicate that non-invasive sensing technologies and computer vision can be applied for assessing and quantify sporulation of downy mildew in grapevine leaves. The severity of this key disease is evaluated in grapevine under laboratory conditions. In conclusion, computer vision, hyperspectral imaging and machine learning could be applied for important disease detection in grapevine. View Full-Text
Keywords: machine vision; hyperspectral imaging; non-invasive phenotyping tools; machine learning; CNN; precision viticulture machine vision; hyperspectral imaging; non-invasive phenotyping tools; machine learning; CNN; precision viticulture
Show Figures

Figure 1

MDPI and ACS Style

Hernández, I.; Gutiérrez, S.; Ceballos, S.; Iñíguez, R.; Barrio, I.; Tardaguila, J. Artificial Intelligence and Novel Sensing Technologies for Assessing Downy Mildew in Grapevine. Horticulturae 2021, 7, 103. https://doi.org/10.3390/horticulturae7050103

AMA Style

Hernández I, Gutiérrez S, Ceballos S, Iñíguez R, Barrio I, Tardaguila J. Artificial Intelligence and Novel Sensing Technologies for Assessing Downy Mildew in Grapevine. Horticulturae. 2021; 7(5):103. https://doi.org/10.3390/horticulturae7050103

Chicago/Turabian Style

Hernández, Inés, Salvador Gutiérrez, Sara Ceballos, Rubén Iñíguez, Ignacio Barrio, and Javier Tardaguila. 2021. "Artificial Intelligence and Novel Sensing Technologies for Assessing Downy Mildew in Grapevine" Horticulturae 7, no. 5: 103. https://doi.org/10.3390/horticulturae7050103

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop