Automatic Detection of Flavescence Dorée Symptoms Across White Grapevine Varieties Using Deep Learning

Abstract

Flavescence dorée (FD) is a grapevine disease caused by phytoplasmas and transmitted by leafhoppers that has been spreading in European vineyards despite significant efforts to control it. In this study, we aim to develop a model for the automatic detection of FD-like symptoms (which encompass other grapevine yellows symptoms). The concept is to detect likely FD-affected grapevines so that samples can be removed for FD laboratory identification, followed by uprooting if they test positive, all to be conducted quickly and without omission, thus avoiding further contamination in the fields. Developing FD-like symptoms detection models is not simple, as it requires dealing with the complexity of field conditions and FD symptoms’ expression. To address these challenges, we use deep learning, which has already been proven effective in similar contexts. More specifically, we train a Convolutional Neural Network on image patches, and convert it into a Fully Convolutional Network to perform inference. As a result, we obtain a coarse segmentation of the likely FD-affected areas while having only trained a classifier, which is less demanding in terms of annotations. We evaluate the performance of our model trained on a white grape variety, Chardonnay, across five other grape varieties with varying FD symptoms expressions. Of the two largest test datasets, the true positive rate for Chardonnay reaches 98.48% whereas for Ugni-Blanc it drops to 8.3%, underlining the need for a multi-varietal training dataset to capture the diversity of FD symptoms. To obtain more transparent results and to better understand the model’s sensitivity, we investigate its behavior using two visualization techniques, Guided Gradient-weighted Class Activation Mapping and the Uniform Manifold Approximation and Projection. Such techniques lead to a more comprehensive analysis with greater reliability, which is essential for in-field applications, and more broadly, for all applications impacting humans and the environment.

Automated Grapevine Cultivar Identification via Leaf Imaging and Deep Convolutional Neural Networks: A Proof-of-Concept Study Employing Primary Iranian Varieties

“…Additionally, a two dimensional bud detection scheme by using a CNN for grapevines has been recently established [ 29 ]. Grapevine diseases caused by phytoplasmas have also been successfully resolved via CNNs on proximal RGB images [ 30 ], while fungal symptoms were earlier detected and successfully classified by using machine learning [ 31 ]. Ampatzidis and coworkers [ 32 ] also illustrated an innovative grapevine viral disease detection system by combining artificial intelligence and machine learning.…”

Early Identification of Root Rot Disease by Using Hyperspectral Reflectance: The Case of Pathosystem Grapevine/Armillaria

“…Several recent studies, using different sensors, have confirmed the potential of hyperspectral data to detect plant pathologies in a reliable manner in various pathosystems associated with grapevines, such as leafroll-associated virus-3 [22,28,29], grapevine trunk disease [30][31][32], Flavescence dorèe [33][34][35], and powdery mildew [36]. However, to date, there are no available data concerning grapevine root rot disease identification through hyperspectral images, thus making this work the pioneer.…”

Phenotyping grapevine red blotch virus and grapevine leafroll-associated viruses before and after symptom expression through machine-learning analysis of hyperspectral images

“…This improvement is beneficial in a vineyard setting where false negatives represent infected plants that are not detected, and thus may contribute to virus spread by insect vectors to healthy plants until correctly detected and eliminated. As described by AL-Saddik et al (2017) and Boulent et al (2020), an incorrect negative prediction that keeps an infected plant in place is far more costly than a false positive prediction, leading to the removal of a healthy plant.…”