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López Molina, Carlos

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https://academica-e.unavarra.es/handle/2454/49448

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López Molina

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Carlos

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Estadística, Informática y Matemáticas

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0000-0002-0904-9834

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810097

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20201
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Start date: 2020 × End date: 2024 × Author: Arazuri Garín, Silvia × Subject: Chemometrics × Subject: Vitis vinifera L. ×

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    PublicationOpen Access
    Hyperspectral imaging to assess the presence of powdery mildew (Erysiphe necator) in cv. Carignan Noir grapevine bunches
    (MDPI, 2020) Pérez Roncal, Claudia; López Maestresalas, Ainara; López Molina, Carlos; Jarén Ceballos, Carmen; Urrestarazu Vidart, Jorge; Santesteban García, Gonzaga; Arazuri Garín, Silvia; Ingeniería; Estadística, Informática y Matemáticas; Agronomía, Biotecnología y Alimentación; Ingeniaritza; Estatistika, Informatika eta Matematika; Agronomia, Bioteknologia eta Elikadura; Gobierno de Navarra / Nafarroako Gobernua, Proyecto DECIVID (Res.104E/2017); Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa, FPI-UPNA-2017 (Res.654/2017)
    Powdery mildew is a worldwide major fungal disease for grapevine, which adversely affects both crop yield and produce quality. Disease identification is based on visible signs of a pathogen once the plant has already been infected; therefore, techniques that allow objective diagnosis of the disease are currently needed. In this study, the potential of hyperspectral imaging (HSI) technology to assess the presence of powdery mildew in grapevine bunches was evaluated. Thirty Carignan Noir grape bunches, 15 healthy and 15 infected, were analyzed using a lab-scale HSI system (900–1700 nm spectral range). Image processing was performed to extract spectral and spatial image features and then, classification models by means of Partial Least Squares Discriminant Analysis (PLS-DA) were carried out for healthy and infected pixels distinction within grape bunches. The best discrimination was achieved for the PLS-DA model with smoothing (SM), Standard Normal Variate (SNV) and mean centering (MC) pre-processing combination, reaching an accuracy of 85.33% in the cross-validation model and a satisfactory classification and spatial location of either healthy or infected pixels in the external validation. The obtained results suggested that HSI technology combined with chemometrics could be used for the detection of powdery mildew in black grapevine bunches.