Comunicaciones y ponencias de congresos DING - INGS Biltzarretako komunikazioak eta txostenak

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Browsing Comunicaciones y ponencias de congresos DING - INGS Biltzarretako komunikazioak eta txostenak by Department/Institute "Agronomía, Biotecnología y Alimentación"

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    PublicationOpen Access
    Early detection of Esca disease in grapevines using in-field hyperspectral proximal sensing
    (Hellenic Society of Agricultural Engineers, 2025) López Maestresalas, Ainara; Ruiz de Gauna González, Jon; Jarén Ceballos, Carmen; León Ecay, Sara; Arazuri Garín, Silvia; Agronomía, Biotecnología y Alimentación; Agronomia, Bioteknologia eta Elikadura; Ingeniería; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD
    Esca is one of the most destructive vine diseases in the world. It causes significant economic losses, mainly due to reduced grape yield and quality. Currently, the approved methods of controlling esca include preventive methods such as the use of fungicides on plant wounds or the use of planting systems that do not require intensive pruning, among others. It is therefore advisable to monitor the crop to identify those vines that are susceptible to the disease. For this reason, in this study a proximal hyperspectral camera was used for early detection of esca presence in asymptomatic grapevine leaves. Images of 11 vines of the Tempranillo variety grown in Etxauri (Navarre, Spain) were analysed. Hyperspectral images were acquired using a Specim IQ snapshot camera, mounted on a tripod, working in the range of 400¿1000 nm with a spectral resolution of 7 nm (204 bands), and an image resolution of 512 × 512 pixel including an RGB camera (5 Mpix). The images were taken under natural ambient light conditions on August 21, 2023. From the 11 vines selected, 9 showed visual symptoms of esca and the remaining 2 were asymptomatic to the naked eye. A total of 200 pixels were randomly selected from the dataset, 100 from asymptomatic leaves of asymptomatic vines (class 1) and 100 from asymptomatic leaves of symptomatic vines (class 2). Partial Least Square Discriminant Analysis (PLS-DA) was performed to classify the leaves into the two classes. Classification rates of 97% were achieved in the cross-validation dataset. Models were externally validated at pixel-level using one image of an asymptomatic vine and another of a symptomatic vine. The visualisation of the images confirmed the correct classification of the pixels into the two classes, indicating that by using proximal hyperspectral sensing an early identification of the disease is possible.
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    PublicationOpen Access
    Identificación de síntomas previsuales de salinidad mediante imágenes hiperespectrales infrarrojas en vid
    (Sociedad Española de Ciencias Hortícolas, 2022) Arazuri Garín, Silvia; Pérez Roncal, Claudia; Jarén Ceballos, Carmen; Santesteban García, Gonzaga; Marín Ederra, Diana; Miranda Jiménez, Carlos; López Maestresalas, Ainara; Agronomía, Biotecnología y Alimentación; Agronomia, Bioteknologia eta Elikadura; Ingeniería; Ingeniaritza; Institute for Multidisciplinary Research in Applied Biology - IMAB; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD
    Los niveles altos de salinidad que se pueden producir en un viñedo, asociados generalmente al uso de aguas de baja calidad genera un tipo de estrés abiótico que limita la producción de la uva y afecta a la calidad de los vinos. Teniendo en cuenta la importancia de la monitorización de los cultivos en la toma de decisiones para una buena gestión del viñedo, se plantea como objetivo de este trabajo la identificación previsual de síntomas de estrés abiótico en viña por medio de la tecnología de imágenes hiperespectrales en el infrarrojo cercano (HSI-NIR). Para llevar a cabo este objetivo, se realizó un ensayo en maceta en la Finca de Prácticas de la Escuela Técnica Superior de Ingeniería Agronómica y Biociencias de la UPNA. El ensayo se realizó con plantas de un año de la variedad Monastrell sobre dos portainjertos 110R y 1103P. Se establecieron dos tratamientos: control (regado con agua de riego no salina) y salinidad (agua de riego con una concentración de sal común de 1,6 g/l). Entre finales de agosto y principios de septiembre se realizaron tres muestreos de hojas, analizando un total de 600 hojas (100 hojas/tratamiento y día). Las imágenes se tomaron con una cámara hiperespectral Xeva 1.7-320-100Hz, con rango espectral 900-1700nm. Una vez procesadas las imágenes se realizó una clasificación mediante un análisis discriminante por mínimos cuadrados parciales (PLS-DA) obteniéndose un porcentaje de muestras correctamente clasificadas en su grupo de origen (control o salinidad) del 82 % el primer día de muestreo, y del 87 % a partir del segundo día. A partir de estos datos podemos concluir que es posible identificar, mediante la tecnología HSI-NIR, síntomas en plantas sometidas a un tratamiento de riego con agua salina antes de que aparezcan síntomas en las hojas.
  • Thumbnail Image
    PublicationOpen Access
    On-site identification of esca-affected vines using hyperspectral imaging
    (Hellenic Society of Agricultural Engineers, 2025) León Ecay, Sara; Ruiz de Gauna González, Jon; López Maestresalas, Ainara; Jarén Ceballos, Carmen; Arazuri Garín, Silvia; Ingeniería; Ingeniaritza; Agronomía, Biotecnología y Alimentación; Agronomia, Bioteknologia eta Elikadura; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD
    Esca represents one of the greatest threats to modern viticulture as it causes large annual economic losses. At present, there is a lack of effective strategies for disease control, so a technique capable of detecting affected vines would allow annual monitoring of disease incidence in the vineyard leading to a better crop management and decision making. This study evaluates close-range hyperspectral imaging for the detection of esca naturally infected vines. Images of 11 vines of the Tempranillo variety grown on plots in Bodegas Otazu, in Etxauri (Navarre, Spain) were acquired. A Specim IQ snapshot hyperspectral camera was used to record the images on August, 21 2023 on the field under natural light conditions. The camera has a spectral resolution of 7 nm (204 wavelengths) and a spatial resolution of 512 x 512 in the 400 ¿ 1000 nm spectral range (Vis-NIR). An individual image was acquired for each vine, of which 9 were symptomatic and 2 asymptomatic. Three classes were analysed: asymptomatic leaves of asymptomatic vines (Class 1), asymptomatic leaves of symptomatic vines (Class 2) and asymptomatic areas of symptomatic leaves of symptomatic vines (Class 3). A total of 300 pixels were randomly selected, 100 per class, for further analysis. Partial Least Square Discriminant Analysis (PLSDA) was used to classify the pixels into the three categories. An accuracy of 86% was achieved in the cross-validation dataset. Models were externally validated using an image of an asymptomatic vine and an image of a symptomatic vine. The visualisation of the images showed that the majority of the pixels of the asymptomatic vine image were classified as class 1, while most of the pixels of the symptomatic vine image were classified as either class 2 or class 3. Hence, this study demonstrated the potential of close-range HSI for the on-site detection of esca.