Terroir 2020 banner
IVES 9 IVES Conference Series 9 International Terroir Conferences 9 Terroir 2020 9 History and innovation of terroir 9 Using image analysis for assessing downy mildew severity in grapevine

Using image analysis for assessing downy mildew severity in grapevine

Abstract

Aim: Downy mildew is a crucial disease in viticulture. In-field evaluation of downy mildew has been classically based on visual inspection of leaves and fruit. Nevertheless, non-invasive sensing technologies could be used for disease detection in grapevine. The aim of this study was to assess downy mildew severity in grapevine leaves using machine vision.

Methods and Results: Leaf disks of the cv Pinot Noir (Vitis vinifera L.) were placed in Petri dishes with the abaxial side up. Plasmopara viticola sporangia were collected from infected leaves in the vineyard and used for the experimental inoculation of the leaf disks in laboratory. Images of Petri dishes including different levels of downy mildew infection were taken using a digital RGB camera. Machine vision techniques were used to estimate downy mildew severity (percentage of pixels representing visual symptoms) on the leaves. The symptoms were evaluated by eight experts, visually estimating the percentage of area showing sporulation. Considering the average evaluation of the experts, the assessment obtained by the new developed algorithm based on computer vision was represented as a R2value of 0.82 and RMSE of 14.34%.

Conclusions:

The results show a strong correlation between the severity computed by machine vision and the visual assessments, opening the possibility of the automated evaluation of downy mildew severity using non-invasive sensors.

Significance and Impact of the Study: The results indicated that machine vision can be applied for assessing and quantify visual symptoms of downy mildew in grapevine

DOI:

Publication date: March 23, 2021

Issue: Terroir 2020

Type: Video

Authors

Inés Hernández1, Salvador Gutiérrez2, Sara Ceballos1, Miriam Alonso1, Umberto Calvo1, Ignacio Barrio1, Fernando Palacios1, Silvia Toffolatti3, Giuliana Maddalena3, Javier Tardaguila1*

1Televitis Research Group. University of La Rioja, 26007 Logroño, Spain
2Department of Computer Science and Engineering, University of Cádiz, 11519 Puerto Real, Spain
3Dipartimento di Scienze Agrarie e Ambientali, Università degli Studi di Milano, 20133, Milano, Italy

Contact the author

Keywords

Grapevine, downy mildew, non-invasive phenotyping tools, imaging, machine vision

Tags

IVES Conference Series | Terroir 2020

Citation

Related articles…

Effect of multi-level and multi-scale spectral data source on vineyard state assessment

Currently, the main goal of agriculture is to promote the resilience of agricultural systems in a sustainable way through the improvement of use efficiency of farm resources, increasing crop yield and quality under climate change conditions. This last is expected to drastically modify plant growth, with possible negative effects, especially in arid and semi-arid regions of Europe on the viticultural sector. In this context, the monitoring of spatial behavior of grapevine during the growing season represents an opportunity to improve the plant management, winegrowers’ incomes, and to preserve the environmental health, but it has additional costs for the farmer. Nowadays, UAS equipped with a VIS-NIR multispectral camera (blue, green, red, red-edge, and NIR) represents a good and relatively cheap solution to assess plant status spatial information (by means of a limited set of spectral vegetation indices), representing important support in precision agriculture management during the growing season. While differences between UAS-based multispectral imagery and point-based spectroscopy are well discussed in the literature, their impact on plant status estimation by vegetation indices is not completely investigated in depth. The aim of this study was to assess the performance level of UAS-based multispectral (5 bands across 450-800nm spectral region with a spatial resolution of 5cm) imagery, reconstructed high-resolution satellite (Sentinel-2A) multispectral imagery (13 bands across 400-2500 nm with spatial resolution of <2 m) through Convolutional Neural Network (CNN) approach, and point-based field spectroscopy (collecting 600 wavelengths across 400-1000 nm spectral region with a surface footprint of 1-2 cm) in a plant status estimation application, and then, using Bayesian regularization artificial neural network for leaf chlorophyll content (LCC) and plant water status (LWP) prediction. The test site is a Greco vineyard of southern Italy, where detailed and precise records on soil and atmosphere systems, in-vivo plant monitoring of eco-physiological parameters have been conducted.

Effects of graft quality on growth and grapevine-water relations

Climate change is challenging viticulture worldwide compromising its sustainability due to warmer temperatures and the increased frequency of extreme events. Grafting Vitis vinifera L.

Phenolic composition of Tempranillo Blanco grapes changes after foliar application of urea

Our research aimed to determine the effect and efficiency of foliar application of urea on the phenolic composition of Tempranillo Blanco grapes. The field experiment was carried out in 2019 and 2020 seasons and the plot was located in D.O.Ca Rioja (North of Spain). The vineyard was Vitis vinifera L. Tempranillo Blanco and grafted on Richter-110 rootstock. The treatments were control (C), whose plants were sprayed with water and three doses of urea: plants were sprayed with urea 3 kg N/ha (U3), 6 kg N/ha (U6) and 9 kg N/ha (U9). The applications were performed in two phenological stages, pre-veraison (Pre) and veraison (Ver). Also, each of the treatments was repeated one week later. Control and treatments were performed in triplicate and arranged in a randomised block design. Grapes were harvested at optimum ripening stage. High-performance liquid chromatography was used to analyse the phenolic composition of the grapes. Finally, the results obtained from the analytical determinations – flavonols, flavanols and non-flavonoid (hydroxybenzoic acids, hydroxycinnamic acids and stilbenes) – were studied statistically by analysis of variance. The results showed that, in 2019, U6-Pre and U9-Pre treatments increased the hydroxybenzoic acid content in grapes, and also all foliar treatments applied at Pre enhanced the stilbene concentration. Moreover, U3-Ver was the only treatment that rose flavonol and stilbene contents in the Tempranillo Blanco grapes. In 2020, all treatments applied at Pre enhanced the flavonol concentration in grapes. Furthermore, U3-Pre and U9-Pre treatments increased stilbene content in grapes. Nevertheless, the hydroxybenzoic acid content was improved by U6-Ver and U9-Ver and besides, hydroxycinnamic acid concentration in grapes was increased by all treatments applied at Ver. In conclusion, the lower and highest dose of urea (U3 and U9), applied at pre-veraison, were the best treatments to improve the Tempranillo Blanco grape phenolic composition.

The modification of cultural practices in grapevine cv. Syrah, does it modify the characteristics of the musts?

The work shows the results of a year of experimentation (2020) in a Syrah variety vineyard in La Roda (Castilla-La Mancha, Spain). The trial approach was on a randomized block design with two factors: Irrigation (I) and Pruning (P).
Irrigation schedules were adjusted to apply amounts close to 1,500 m3/ha. With this provision, 2 different irrigation treatments were proposed: I1) Start of irrigation from pea-sized grape to post-harvest (providing at least 20 % of the total amount of irrigation water to be provided post-harvest); I2) Start of irrigation from pea-sized grape to harvest (usual irrigation practice in the study area). Pruning was proposed with two treatments, one at the end of January (P1), which is pruning on a conventional date; and P2) pruning carried out at the beginning of budding. In total, 4 repetitions were designed with 4 elementary plots, each one of them representing one of the proposed treatments (I1P1; I1P2; I2P1; I2P2). In total, 16 plots were worked on and each elementary plot consisted of 30 strains, distributed in 3 lines.
The productive response was evaluated with the yield results of the harvest harvested at 23 ºBrix. The qualitative response was measured in the musts through the indices of technological (acidity, pH and potassium) and phenolic maturity and aromatic compounds in free and glycosylated fractions. The treatments tested had, in general, an effect on the different variables analyzed.

A better understanding of the climate effect on anthocyanin accumulation in grapes using a machine learning approach

The current climate changes are directly threatening the balance of the vineyard at harvest time. The maturation period of the grapes is shifted to the middle of the summer, at a time when radiation and air temperature are at their maximum. In this context, the implementation of corrective practices becomes problematic. Unfortunately, our knowledge of the climate effect on the quality of different grape varieties remains very incomplete to guide these choices. During the Innovine project, original experiments were carried out on Syrah to study the combined effects of normal or high air temperature and varying degrees of exposure of the berries to the sun. Berries subjected to these different conditions were sampled and analyzed throughout the maturation period. Several quality characteristics were determined, including anthocyanin content. The objective of the experiments was to investigate which climatic determinants were most important for anthocyanin accumulation in the berries. Temperature and irradiance data, observed over time with a very thin discretization step, are called functional data in statistics. We developed the procedure SpiceFP (Sparse and Structured Procedure to Identify Combined Effects of Functional Predictors) to explain the variations of a scalar response variable (a grape berry quality variable for example) by two or three functional predictors (as temperature and irradiance) in a context of joint influence of these predictors. Particular attention was paid to the interpretability of the results. Analysis of the data using SpiceFP identified a negative impact of morning combinations of low irradiance (lower than about 100 μmol m−2 s−1 or 45 μmol m−2 s−1 depending on the advanced-delayed state of the berries) and high temperature (higher than 25oC). A slight difference associated with overnight temperature occurred between these effects identified in the morning.