@misc{10481/68427,
year = {2021},
url = {http://hdl.handle.net/10481/68427},
abstract = {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.},
organization = {Project NoPest (Novel Pesticides for a Sustainable Agriculture)},
publisher = {MDPI},
keywords = {Machine vision},
keywords = {Hyperspectral imaging},
keywords = {Non-invasive phenotyping tools},
keywords = {Machine learning},
keywords = {CNN},
keywords = {Precision viticulture},
title = {Artificial Intelligence and Novel Sensing Technologies for Assessing Downy Mildew in Grapevine},
doi = {10.3390/horticulturae7050103},
author = {Hernández, Inés and Gutiérrez Salcedo, Salvador},
}