Open 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.
Open 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.
Open Access
Combination of spectral and textural features of hyperspectral imaging for the authentication of the diet supplied to fattening cattle
(Elsevier, 2024) León Ecay, Sara; Insausti Barrenetxea, Kizkitza; Arazuri Garín, Silvia; Goenaga Uceda, Irantzu; López Maestresalas, Ainara; Agronomía, Biotecnología y Alimentación; Agronomia, Bioteknologia eta Elikadura; Ingeniería; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
This study explored the potential of hyperspectral imaging in the near infrared region (NIR-HSI) as a non-destructive and rapid tool to discriminate among two beef fattening diets. For that purpose, a feeding trial was carried out with a total of 24 purebred Pirenaica calves. Twelve of them were fed barley and straw (BS) while 11 animals were finished on vegetable by-products (VBPR). When comparing the reference measurements of the meat coming from those animals, only the total collagen ratio expressed the feeding effect (p-value<0.05). To undertake the authentication procedure, two discrimination approaches were run: partial least squares discriminant analysis (PLS-DA) and radial basis function-support vector machine (RBF-SVM). To precisely extract spectral and textural information from the lean portion of the meat steaks, various techniques were executed, such as principal component (PC) images, competitive adaptive reweighted sampling (CARS) for selecting optimal wavelengths, and gray-level-co-occurrence matrix (GLCM). After hyperspectral imaging and the combination of their own texture features, samples were classified according to feeding diet with an overall accuracy of 72.92% for PLS-DA and 80.56% for RBF-SVM. So, the potential of using HSI technology to authenticate the meat obtained from beef supplied a diet based on circular economy techniques was made in evidence.
Open Access
Classification of beef longissimus thoracis muscle tenderness using hyperspectral imaging and chemometrics
(MDPI, 2022) León Ecay, Sara; López Maestresalas, Ainara; Murillo Arbizu, María Teresa; Beriain Apesteguía, María José; Mendizábal Aizpuru, José Antonio; Arazuri Garín, Silvia; Jarén Ceballos, Carmen; Bass, Phillip D.; Colle, Michael J.; García, David; Romano Moreno, Miguel; Insausti Barrenetxea, Kizkitza; Agronomia, Bioteknologia eta Elikadura; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Agronomía, Biotecnología y Alimentación; Ingeniería; Gobierno de Navarra / Nafarroako Gobernua Universidad Pública de Navarra / Nafarroako Unibertsitate
Nowadays, the meat industry requires non-destructive, sustainable, and rapid methods that can provide objective and accurate quality assessment with little human intervention. Therefore, the present research aimed to create a model that can classify beef samples from longissimus thoracis muscle according to their tenderness degree based on hyperspectral imaging (HSI). In order to obtain different textures, two main strategies were used: (a) aging type (wet and dry aging with or without starters) and (b) aging times (0, 7, 13, 21, and 27 days). Categorization into two groups was carried out for further chemometric analysis, encompassing group 1 (ngroup1 = 30) with samples with WBSF < 53 N whereas group 2 (ngroup2 = 28) comprised samples with WBSF values 53 N. Then, classification models were created by applying the partial least squares discriminant analysis (PLS-DA) method. The best results were achieved by combining the following pre-processing algorithms: 1st derivative + mean center, reaching 70.83% of correctly classified (CC) samples and 67.14% for cross validation (CV) and prediction, respectively. In general, it can be concluded that HSI technology combined with chemometrics has the potential to differentiate and classify meat samples according to their textural characteristics.

León Ecay, Sara

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