Forecasting severe grape downy mildew attacks using machine learning

Abstract

Grape downy mildew (GDM) is a major disease of grapevine that has an impact on both the yields of the vines and the quality of the harvested fruits. The disease is currently controlled by repetitive fungicide treatments throughout the season, especially in the Bordeaux vineyards where the average number of fungicide treatments against GDM was equal to 10.1 in 2013. Reducing the number of treatments is a major issue from both an environmental and a public health point of view. One solution would be to identify vineyards that are likely to be heavily attacked in spring and then apply fungicidal treatments only to these situations. In this perspective, we use here a dataset including 9 years of GDM observations to develop and compare several generalized linear models and machine learning algorithms predicting the probability of high incidence and severity in the Bordeaux region. The algorithms tested use the date of disease onset and/or average monthly temperatures and precipitation as input variables. The accuracy of the tested models and algorithms is assessed by year-byyear cross validation. LASSO, random forest and gradient boosting algorithms show better performance than generalized linear models. The date of onset of the disease has a greater influence on the accuracy of forecasts than weather inputs and, among weather inputs, precipitation has a greater influence than temperature. The best performing algorithm was selected to evaluate the impact of contrasted climate scenarios on GDM risk levels. Results show that risk of GDM at bunch closure decreases with reduced rainfall and increased temperatures in April-May. Our results also show that the use of fungicide treatment decision rules that take into account local characteristics would reduce the number of treatments against GDM in the Bordeaux vineyards compared to current practices by at least 50%.

Predicting symptoms of downy mildew, powdery mildew, and gray mold diseases of grapevine through machine learning

“…Bioclimatic indices were calculated starting from daily data on air temperature and precipitation, considering three different periods: (i) from November to January for the indices describing the weather conditions during overwintering (average of minimum, maximum and mean daily temperature), (ii) from November to March for monthly mean air temperature and cumulative precipitation, (iii) from April to October (monitoring period) for the bioclimatic indices describing the weather conditions in the interval from 14 to 7 days before the monitoring day or during the 7 days before the monitoring day. We considered these two time steps to identify the environmental conditions of the period during which the pathogen penetration into the host tissues was most probable (avg_14_7, avg_max_14_7, avg_ min_14_7, cum_rain_14_7) (Chen et al, 2020;Carisse et al, 2009;Barka et al, 2002).…”

“…Because these pathogens may cause severe symptoms on grapevines at the beginning of infection, control strategies have focused on early treatments, even in integrated pest management (IPM), as prevention to stop the pathogen outbreak before its establishment. Applying fungicide treatments during the growing season remains the most common practice to control these diseases, from early spring onward, with differences between years due to weather conditions and to the geographic location of the vineyard (Chen et al, 2020;Lu et al, 2020;Molitor et al, 2016). However, concerns about the negative impact of chemicals on environmental and human health have resulted in restrictions to regulate fungicide use, such as the EU directives (i.e., Directive 1107/2009/EU) (Valdés-Gómez et al, 2017).…”

“…The potential of statistical models and ML algorithms to predict the occurrence of grapevine diseases has been rarely assessed (Chen et al, 2020). Here, we investigated the ability of ML algorithms to clarify the occurrence of symptoms of these three important diseases of grapevine based on prevailing weather conditions both within and between locations and years, generating temporal-and spatial-explicit projections of the infections.…”

Identification of Diagnostic CpG Signatures in Patients with Gestational Diabetes Mellitus via Epigenome-Wide Association Study Integrated with Machine Learning

“…Machine learning is a category of information science that trains the computer to execute tasks by recognizing patterns in massive datasets and using them to determine rules or algorithms that optimize task achievement [24]. It has been reported that machine learning is beneficial to the discovery of predictive biomarkers and diagnosis of GDM [7,[25][26][27]. Support vector machine (SVM) is a practical machine learning model and has been confirmed to be useful classifiers in all kinds of fields, including face recognition, handwritten digit recognition, text classification, and bioinformatics [28].…”

Epidemiological Model based Decision Support System for the Management of Grapes Downy Mildew

“…In order to ascertain high-risk periods for primary infections and time of fungicidal sprays, several weather-driven models have been proposed in different countries (Tran Manh Sung et al, 1990;Hill, 2000;Park et al, 1997). A new machine learning algorithms model for predicting the probability of high incidence was developed by Chen et al (2020) and proposed the risk of grapes downy mildew at bunch maturity stage due to rainfall and temperatures in Bordeaux region of south western France. An advantage of this mathematical tool is the intrinsic relationship with other tools like disease progress curves and computer simulations because important components of disease dynamics can be modelled by using growth models that latter could be linked and simulated using computer programs (Mersha and Hau, 2008).…”