López Maestresalas, Ainara

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López Maestresalas

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Ainara

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Ingeniería

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IS-FOOD. Research Institute on Innovation & Sustainable Development in Food Chain

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0000-0001-6636-0752

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750902

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810716

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Open Access
Análisis espacio-temporal de los accidentes mortales con tractor en España durante el período 2010-2019
(Interempresas Media, 2023) Arazuri Garín, Silvia; Ibarrola, Alicia; Mangado Ederra, Jesús; Adin Urtasun, Aritz; Arnal Atarés, Pedro; López Maestresalas, Ainara; Jarén Ceballos, Carmen; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Ingeniería; Ingeniaritza
El sector agrario y el de la construcción son los que presentan los índices de incidencia de accidentes de trabajo mortales más altos de nuestro país, según los datos recogidos por el Instituto Nacional de Seguridad y Salud en el Trabajo (INSST) (2021) dependiente del Ministerio de Trabajo y Economía Social (Cirauqui, 2022). Si tenemos en cuenta la evolución de estos índices, el sector agrario es el único que no ha mejorado dicho índice desde la aparición de la Ley 31/1995 de prevención de riesgos laborales y su siniestralidad continúa aumentando (Fundación Mapfre 2020). Pero, ¿qué ocurre cuando el accidente lo sufren personas que no encajan en la definición legal de trabajador? Estos accidentes no son considerados 'accidente de trabajo' y, por tanto, escapan a todas las estadísticas y datos oficiales del INSST. Este suele ser el caso de muchos accidentes que sufren personas jubiladas, menores de 16 años, familiares colaboradores, etc. que no son personas vinculadas a la actividad laboral tal y como se define en la legislación. Según Arana et al. (2010) de un total de 388 accidentes mortales ocurridos en España con maquinaria agrícola durante los años 2004-2008, solamente el 61,85% de ellos tuvieron carácter oficial. Las personas mayores fueron el sector de la población con un mayor riesgo, seguidos de los niños y las personas ajenas al sector agrario. La mayoría de las muertes fueron debidas al vuelco de tractores sin estructuras de protección.
Open Access
Proyecto Agroinc: prevención del impacto ambiental de incendios provocados por cosechadoras
(Interempresas Media, 2022) Arazuri Garín, Silvia; Mangado Ederra, Jesús; López Maestresalas, Ainara; López Molina, Carlos; Angulo Muñoz, Blanca; Arnal Atarés, Pedro; Jarén Ceballos, Carmen; Ingeniería; Ingeniaritza; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Proyectos e Ingeniería Rural; Landa Ingeniaritza eta Proiektuak; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Gobierno de Navarra / Nafarroako Gobernua
Las cosechadoras de cereales, por las condiciones ambientales en las que trabajan, alta temperatura y baja humedad, tanto ambiental como del producto que están cosechando, pueden provocar accidentalmente incendios durante la época de recolección. Los daños económicos y medioambientales que estos incendios suponen pueden ser muy importantes, ya que las condiciones de propagación del fuego son óptimas. Los principales objetivos de este proyecto han sido evaluar el impacto ambiental de los incendios producidos en Navarra en los últimos años y establecer una guía de buenas prácticas para su prevención.
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
Influencia de factores de cultivo y conservación en el contenido en azúcares reductores en patata
(Universidad de Sevilla, 2023) Jarén Ceballos, Carmen; Peraza Alemán, Carlos Miguel; Mangado Ederra, Jesús; López Maestresalas, Ainara; Arazuri Garín, Silvia; Ingeniería; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD
La patata es uno de los alimentos más importante del mundo y una de las formas más habituales de consumirla es como patatas fritas. Al freírla a altas temperaturas, los azúcares reductores y la asparagina de la patata pueden dar lugar a acrilamidas, por medio de la reacción de Maillard. La acrilamida está clasificada como sustancia probablemente cancerígena para el ser humano. Por eso es importante que las patatas destinadas a fritura tengan un bajo contenido en azúcares reductores. Este contenido depende de factores genéticos, medioambientales, culturales y condiciones de almacenamiento. Por ello, en este trabajo se pretende analizar algunos de esos factores en una variedad rica en azúcares reductores como es Jaerla. Los factores analizados fueron el estrés hídrico durante el cultivo, dos temperaturas de almacenamiento (8 y 13ºC) y tiempo de almacenamiento en las anteriores temperaturas, desde 0 hasta 13 semanas. Las muestras de patatas de cada uno de los tratamientos se liofilizaron y se determinó su contenido en azúcares: glucosa, fructosa y sacarosa. Los datos fueron analizados con R-Studio. Solo se encontraron diferencias significativas en el factor temperatura de conservación para los tres azúcares, obteniéndose los valores más altos en las patatas conservadas a 8ºC.
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
Predicting the spatial distribution of reducing sugars using near-infrared hyperspectral imaging and chemometrics: a study in multiple potato genotypes
(Elsevier, 2025-08-01) Peraza Alemán, Carlos Miguel; Arazuri Garín, Silvia; Jarén Ceballos, Carmen; Ruiz de Galarreta, José Ignacio; Barandalla, Leire; López Maestresalas, Ainara; Ingeniería; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD
The determination of reducing sugars in potatoes is important due to their impact on product quality during industrial processing. The significant variability of these compounds between genotypes presents a challenge to the development of accurate predictive models. This study evaluated the potential of near-infrared hyperspectral imaging (NIR-HSI) for the prediction of reducing sugars in potatoes. For this, a wide range of genotypes (n=92) from two seasons (2020-2021) was selected. Partial Least Squares Regression (PLSR) and Support Vector Machine Regression (SVMR) methods were used to build the prediction models. Furthermore, interval PLS (iPLS), recursive weighted PLS (rPLS), Genetic Algorithm (GA) and Competitive Adaptive Reweighted Sampling (CARS) were used for relevant wavelength identification to develop less computationally complex models. The best full spectrum model (SNV-PLSR) achieved coefficient of determination and root mean square error values of 0.88 and 0.053% and 0.86 and 0.057%, for calibration and external validation, respectively. Variable selection algorithms successfully reduced the dimensionality of the data without compromising the performance of the models. Robust predicted models were built with only 2.65% (CARS-PLSR) and 3.57% (iPLS-SVMR) of the total wavelengths. Finally, a pixel-wise prediction was performed on the validation set and chemical images were built to visualise the spatial distribution of reducing sugars. This study demonstrated that NIR-HSI is a feasible technique for predicting reducing sugars in several potato genotypes.
Open Access
Exploring the potential of hyperspectral imaging to detect Esca disease complex in asymptomatic grapevine leaves
(Elsevier, 2022) Pérez Roncal, Claudia; Arazuri Garín, Silvia; López Molina, Carlos; Jarén Ceballos, Carmen; Santesteban García, Gonzaga; López Maestresalas, Ainara; Ingeniaritza; Estatistika, Informatika eta Matematika; Agronomia, Bioteknologia eta Elikadura; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Ingeniería; Estadística, Informática y Matemáticas; Agronomía, Biotecnología y Alimentación; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Precise and reliable identification of specific plant diseases is a challenge within precision agriculture nowadays. This is the case of esca, a complex grapevine trunk disease, that represents a major threat to modern viticulture as it is responsible for large economic losses annually. The lack of effective control strategies and the complexity of esca disease expression make essential the identification of affected plants, before symptoms become evident, for a better management of the vineyard. This study evaluated the suitability of a near-infrared hyperspectral imaging (HSI) system to detect esca disease in asymptomatic grapevine leaves of Tempranillo red-berried cultivar. For this, 72 leaves from an experimental vineyard, naturally infected with esca, were collected and scanned with a lab-scale HSI system in the 900-1700 nm spectral range. Then, effective image processing and multivariate analysis techniques were merged to develop pixel-based classification models for the distinction of healthy, asymptomatic and symptomatic leaves. Automatic and interval partial least squares variable selection methods were tested to identify the most relevant wavelengths for the detection of esca-affected vines using partial least squares discriminant analysis and different pre-processing techniques. Three-class and two-class classifiers were carried out to differentiate healthy, asymptomatic and symptomatic leaf pixels, and healthy from asymptomatic pixels, respectively. Both variable selection methods performed similarly, achieving good classification rates in the range of 82.77-97.17% in validation datasets for either three-class or two-class classifiers. The latter results demonstrated the capability of hyperspectral imaging to distinguish two groups of seemingly identical leaves (healthy and asymptomatic). These findings would ease the annual monitoring of disease incidence in the vineyard and, therefore, better crop management and decision making.
Open Access
Fatal tractor accidents in the agricultural sector in Spain during the past decade
(MDPI, 2022) Jarén Ceballos, Carmen; Ibarrola, Alicia; Mangado Ederra, Jesús; Adin Urtasun, Aritz; Arnal Atarés, Pedro; López Maestresalas, Ainara; Ríos Eraso, Alonso; Arazuri Garín, Silvia; Ingeniería; Ingeniaritza; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Currently, there is a discrepancy between the number of occupational accidents in the agricultural sector reported by Spanish governmental sources and those actually occurring in general. This is mainly due to the official definition of ‘occupational accident’ in the current regulations. In order to be able to analyse all fatal accidents involving tractors, other sources of information must therefore be used. In this study, we have collected the news published in different media during the period 2010–2019. Statistical models that take into account the spatial and temporal dependence of the data were used to estimate the rates of fatal accidents in the provinces of Spain using the Bayesian inference technique INLA (Integrated Nested Laplace Approximation). The results obtained showed that the total number of fatal accidents in that period was 644. The crude rates of fatal accidents per province ranged from 0 to 223.5 fatal accidents per 100,000 registered tractors. In addition, the overall rate for Spain as a whole was 6.87 fatal accidents per 100,000 tractors. As in other EU countries, it was found that the regions with the highest number of accidents were also related to steep terrain, to an older tractor fleet and to horticultural crops and vineyards.
Open Access
Mapping acrylamide content in potato chips using near-infrared hyperspectral imaging and chemometrics
(Elsevier, 2025-03-14) Peraza Alemán, Carlos Miguel; López Maestresalas, Ainara; Jarén Ceballos, Carmen; Ruiz de Galarreta, José Ignacio; Barandalla, Leire; Arazuri Garín, Silvia; Ingeniería; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD
This study investigated the potential of near-infrared hyperspectral imaging (NIR-HSI) for the prediction of acrylamide content in potato chips. A total of 300 tubers from two potato varieties (Agria and Jaerla) grown in two seasons and processed under the same frying conditions were analysed. Partial Least Square Regression (PLSR) and Support Vector Machine Regression (SVMR), combined with a logarithmic transformation of the acrylamide levels, were applied to develop predictive models. The most optimal outcomes for PLSR yielded R2 p: 0.85, RMSEP: 201 μg/kg and RPD: 2.53, while for SVMR yielded R2 p: 0.80, RMSEP: 229 μg/kg and RPD: 2.22. Furthermore, the selection of significant wavelengths enabled an 87.95 % reduction in variables without affecting the model’s accuracy. Finally, spatial mapping of acrylamide content was conducted on all chips in the external validation set. This method provides both quantification and visualization capabilities, thus enhancing quality control for acrylamide identification in processed potatoes.
Open Access
Applications of sensing for disease detection
(Springer, 2021) Castro, Ana Isabel de; Pérez Roncal, Claudia; Thomasson, J. Alex; Ehsani, Reza; López Maestresalas, Ainara; Yang, Chenghai; Jarén Ceballos, Carmen; Wang, Tianyi; Cribben, Curtis; Marín Ederra, Diana; Isakeit, Thomas; Urrestarazu Vidart, Jorge; López Molina, Carlos; Wang, Xiwei; Nichols, Robert L.; Santesteban García, Gonzaga; Arazuri Garín, Silvia; Peña, José Manuel; Agronomía, Biotecnología y Alimentación; Agronomia, Bioteknologia eta Elikadura; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Ingeniería; Ingeniaritza; Gobierno de Navarra / Nafarroako Gobernua; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
The potential loss of world crop production from the effect of pests, including weeds, animal pests, pathogens and viruses has been quantifed as around 40%. In addition to the economic threat, plant diseases could have disastrous consequences for the environment. Accurate and timely disease detection requires the use of rapid and reliable techniques capable of identifying infected plants and providing the tools required to implement precision agriculture strategies. The combination of suitable remote sensing (RS) data and advanced analysis algorithms makes it possible to develop prescription maps for precision disease control. This chapter shows some case studies on the use of remote sensing technology in some of the world’s major crops; namely cotton, avocado and grapevines. In these case studies, RS has been applied to detect disease caused by fungi using different acquisition platforms at different scales, such as leaf-level hyperspectral data and canopy-level remote imagery taken from satellites, manned airplanes or helicopter, and UAVs. The results proved that remote sensing is useful, effcient and effective for identifying cotton root rot zones in cotton felds, laurel wilt-infested avocado trees and escaaffected vines, which would allow farmers to optimize inputs and feld operations, resulting in reduced yield losses and increased profts.