Dataset Information

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-by-year 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%.

SUBMITTER: Chen M

PROVIDER: S-EPMC7067461 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Forecasting severe grape downy mildew attacks using machine learning.

Chen Mathilde M Brun François F Raynal Marc M Makowski David D

PloS one 20200312 3

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 ...[more]

PMID: 32163490

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Project description:This article introduces a dataset of high-resolution colour images of grapevines. It contains 99 images acquired in the vineyard from a cruising tractor. Each image includes the full foliage of a grapevine plant. These images display a diverse range of symptoms caused by the grapevine downy mildew (Plasmopara viticola), a major fungal disease. The dataset also includes various confounding factors, i.e. anomalies that are not related to the disease. These anomalies are the natural and common phenomena affecting vineyards such as results of mechanical wounds, necroses, chemical burns or yellowing and discolorations due to nutritional or hydric deficiencies. Images were acquired in-situ on "Le Domaine de la Grande Ferrade" a public experimental facility of INRAE, in the area of Bordeaux. Acquisitions took place at early fruiting stages (BBCH 75-79) corresponding to the main sanitary pressure during growth. The acquisition device, embedded on a vine tractor, is composed of an industrial colour camera synchronised with powerful flashes. The purpose of this device is to produce a "day for night" effect that mitigates the variation of sunlight. It enables to homogenise images acquired during different weathers and time of the day and to ensure that the foreground (containing foliage) displays appropriate brightness, with minimum shadows while the background is darker. The images of the dataset were annotated manually by photo-interpretation with a careful review of expertise regarding phytopathology and physiological disorders. The annotation process consists in associating pixels with a class that defines its membership to a type of organ and its physiological state. Pixels from healthy, symptomatic or abnormal grapevine tissues were labelled into seven classes: "Limbus", "Leaf edges", "Berries", "Stems", "Foliar mildew", "Berries mildew" and "Anomalies". The annotation is achieved with the GIMP2 software as mask images where the value attributed to each pixel corresponds to one of the seven considered classes.

Project description:BACKGROUND: Downy mildew (DM), caused by pathogen Plasmopara viticola (PV) is the single most damaging disease of grapes (Vitis L.) worldwide. However, the mechanisms of the disease development in grapes are poorly understood. A method for estimating gene expression levels using Solexa sequencing of Type I restriction-endonuclease-generated cDNA fragments was used for deep sequencing the transcriptomes resulting from PV infected leaves of Vitis amurensis Rupr. cv. Zuoshan-1. Our goal is to identify genes that are involved in resistance to grape DM disease. RESULTS: Approximately 8.5 million (M) 21-nt cDNA tags were sequenced in the cDNA library derived from PV pathogen-infected leaves, and about 7.5 M were sequenced from the cDNA library constructed from the control leaves. When annotated, a total of 15,249 putative genes were identified from the Solexa sequencing tags for the infection (INF) library and 14,549 for the control (CON) library. Comparative analysis between these two cDNA libraries showed about 0.9% of the unique tags increased by at least five-fold, and about 0.6% of the unique tags decreased more than five-fold in infected leaves, while 98.5% of the unique tags showed less than five-fold difference between the two samples. The expression levels of 12 differentially expressed genes were confirmed by Real-time RT-PCR and the trends observed agreed well with the Solexa expression profiles, although the degree of change was lower in amplitude. After pathway enrichment analysis, a set of significantly enriched pathways were identified for the differentially expressed genes (DEGs), which associated with ribosome structure, photosynthesis, amino acid and sugar metabolism. CONCLUSIONS: This study presented a series of candidate genes and pathways that may contribute to DM resistance in grapes, and illustrated that the Solexa-based tag-sequencing approach was a powerful tool for gene expression comparison between control and treated samples.

Dataset Information

Forecasting severe grape downy mildew attacks using machine learning.

Publications

Forecasting severe grape downy mildew attacks using machine learning.

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