Spectral characterization of fungal diseases downy mildew, powdery mildew, black-foot and Petri disease on <i>Vitis vinifera</i> leaves

Pithan, Pâmela Aude; Ducati, Jorge Ricardo; Garrido, L. R.; Arruda, Diniz Carvalho de; Thum, Adriane Brill; Hoff, Rosemary

doi:10.1080/01431161.2021.1929542

Cited by 13 publications

(6 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Concerning PM, Pithan et al [59] identified distinct spectral shifts in grapevine leaves associated with various fungal diseases, and Atanassova et al [60] identified spectral differences between healthy and infected plants, noting the most significant disparities occurred in a distinct spectral region, specifically between 540 and 680 nm, diverging from the range examined in our study. Oberti et al [61] investigated the enhancement of grapevine powdery mildew detection via multispectral imaging from various angles, finding that the detection sensitivity increased significantly, from 9% at 0 • to 73% at 60 • for symptoms in early to middle stages, indicating that angle-based imaging could provide a more focused method for disease management.…”

Section: Discussionsupporting

confidence: 46%

Field-Deployed Spectroscopy from 350 to 2500 nm: A Promising Technique for Early Identification of Powdery Mildew Disease (Erysiphe necator) in Vineyards

Vélez,

Barajas,

Rubio

et al. 2024

Agronomy

View full text Add to dashboard Cite

This study explores spectroscopy in the 350 to 2500 nm range for detecting powdery mildew (Erysiphe necator) in grapevine leaves, crucial for precision agriculture and sustainable vineyard management. In a controlled experimental vineyard setting, the spectral reflectance on leaves with varying infestation levels was measured using a FieldSpec 4 spectroradiometer during July and September. A detailed assessment was conducted following the guidelines recommended by the European and Mediterranean Plant Protection Organization (EPPO) to quantify the level of infestation; categorising leaves into five distinct grades based on the percentage of leaf surface area affected. Subsequently, spectral data were collected using a contact probe with a tungsten halogen bulb connected to the spectroradiometer, taking three measurements across different areas of each leaf. Partial Least Squares Regression (PLSR) analysis yielded coefficients of determination R2 = 0.74 and 0.71, and Root Mean Square Errors (RMSEs) of 12.1% and 12.9% for calibration and validation datasets, indicating high accuracy for early disease detection. Significant spectral differences were noted between healthy and infected leaves, especially around 450 nm and 700 nm for visible light, and 1050 nm, 1425 nm, 1650 nm, and 2250 nm for the near-infrared spectrum, likely due to tissue damage, chlorophyll degradation and water loss. Finally, the Powdery Mildew Vegetation Index (PMVI) was introduced, calculated as PMVI = (R755 − R675)/(R755 + R675), where R755 and R675 are the reflectances at 755 nm (NIR) and 675 nm (red), effectively estimating disease severity (R2 = 0.7). The study demonstrates that spectroscopy, combined with PMVI, provides a reliable, non-invasive method for managing powdery mildew and promoting healthier vineyards through precision agriculture practices.

show abstract

Section: Discussionsupporting

confidence: 46%

Field-Deployed Spectroscopy from 350 to 2500 nm: A Promising Technique for Early Identification of Powdery Mildew Disease (Erysiphe necator) in Vineyards

Vélez,

Barajas,

Rubio

et al. 2024

Agronomy

View full text Add to dashboard Cite

show abstract

“…On Pinot Noir grapes from vineyards in Burgundy, the number was 6.2 × 10 3 to 2 × 10 6 CFU g −1 (Rousseaux et al, 2014). The mostly predominant genera of filamentous fungi identified include Botrytis, Penicillium, Aspergillus, Rhizopus, Plasmopara, and Uncinula (Jones & Mcmanus, 2017;Latorre et al, 2002;Lorenzini et al, 2016;Pithan et al, 2021). However, most filamentous are either pathogenic or secrete mycotoxins (Table 1).…”

Section: The Role Of Other Filamentous Fungi On Grapes and Botrytized Winesmentioning

confidence: 99%

“…Plasmopara viticola and Uncinula necator (syn. Erysiphe necator) are important grapevine fungal pathogens and causal agents of grape downy and powdery mildew, respectively (Jones & Mcmanus, 2017;Pithan et al, 2021). Resistance to P. viticola and U. necator in the grapevine is associated with the accumulation of compounds such as stilbenoids, which help to prevent the invasion of other fungi (Malacarne et al, 2011;Qiu et al, 2015).…”

Section: Fumonisinsmentioning

confidence: 99%

Roles of microbiota in the formation of botrytized grapes and wines

James

Shen

et al. 2021

CyTA - Journal of Food

View full text Add to dashboard Cite

“…Hernández and Gutiérrez [ 19 ] utilized computer vision, hyperspectral imaging, and machine learning to detect early DM disease in grapevine, and an accuracy of 81% was achieved for DM detection. Pithan and Ducati [ 20 ] investigated a series of simple ratio vegetation indices using certain selected wavelengths, and the study suggested that wavelengths shorter than 700 nm carry more information than measurements at near-infrared for fungal disease detection on Vitis vinifera leaves. Kuswidiyanto and Wang [ 21 ] proposed a field-scale system using a hyperspectral camera mounted on an unmanned aerial vehicle and a convolutional neural network to achieve early detection of downy mildew disease in Kimchi cabbage, resulting in an overall accuracy of 0.876 and a 27.07% relative error in estimating disease severity.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Using Hyperspectral Vegetation Indices to Assess Brassica Downy Mildew

Liu,

Fernandez,

Liu

et al. 2024

Sensors

View full text Add to dashboard Cite

Downy mildew caused by Hyaloperonospora brassicae is a severe disease in Brassica oleracea that significantly reduces crop yield and marketability. This study aims to evaluate different vegetation indices to assess different downy mildew infection levels in the Brassica variety Mildis using hyperspectral data. Artificial inoculation using H. brassicae sporangia suspension was conducted to induce different levels of downy mildew disease. Spectral measurements, spanning 350 nm to 1050 nm, were conducted on the leaves using an environmentally controlled setup, and the reflectance data were acquired and processed. The Successive Projections Algorithm (SPA) and signal sensitivity calculation were used to extract the most informative wavelengths that could be used to develop downy mildew indices (DMI). A total of 37 existing vegetation indices and three proposed DMIs were evaluated to indicate downy mildew (DM) infection levels. The results showed that the classification using a support vector machine achieved accuracies of 71.3%, 80.7%, and 85.3% for distinguishing healthy leaves from DM1 (early infection), DM2 (progressed infection), and DM3 (severe infection) leaves using the proposed downy mildew index. The proposed new downy mildew index potentially enables the development of an automated DM monitoring system and resistance profiling in Brassica breeding lines.

show abstract

Spectral characterization of fungal diseases downy mildew, powdery mildew, black-foot and Petri disease on Vitis vinifera leaves

Cited by 13 publications

References 24 publications

Field-Deployed Spectroscopy from 350 to 2500 nm: A Promising Technique for Early Identification of Powdery Mildew Disease (Erysiphe necator) in Vineyards

Field-Deployed Spectroscopy from 350 to 2500 nm: A Promising Technique for Early Identification of Powdery Mildew Disease (Erysiphe necator) in Vineyards

Roles of microbiota in the formation of botrytized grapes and wines

Investigation of Using Hyperspectral Vegetation Indices to Assess Brassica Downy Mildew

Contact Info

Product

Resources

About