Jarén Ceballos, Carmen

Last Name

Jarén Ceballos

First Name

Carmen

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Ingeniería

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

ORCID

0000-0003-3103-7255

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88394

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347

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Now showing 1 - 6 of 6

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
Hyperspectral imaging using notions from type-2 fuzzy sets
(Springer, 2019) López Maestresalas, Ainara; Miguel Turullols, Laura de; López Molina, Carlos; Arazuri Garín, Silvia; Bustince Sola, Humberto; Jarén Ceballos, Carmen; Ingeniería; Ingeniaritza; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Fuzzy set theory has developed a prolific armamentarium of mathematical tools for each of the topics that has fallen within its scope. One of such topics is data comparison, for which a range of operators has been presented in the past. These operators can be used within the fuzzy set theory, but can also be ported to other scenarios in which data are provided in various representations. In this work, we elaborate on notions for type-2 fuzzy sets, specifically for the comparison of type-2 fuzzy membership degrees, to create function comparison operators. We further apply these operators to hyperspectral imaging, in which pixelwise data are provided as functions over a certain energy spectra. The performance of the functional comparison operators is put to the test in the context of in-laboratory hyperspectral image segmentation.
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
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.
Open Access
Non-destructive detection of blackspot in potatoes by Vis-NIR and SWIR hyperspectral imaging
(Elsevier, 2016) López Maestresalas, Ainara; Keresztes, Janos C.; Goodarzi, Mohammad; Arazuri Garín, Silvia; Jarén Ceballos, Carmen; Saeys, Wouter; Proyectos e Ingeniería Rural; Landa Ingeniaritza eta Proiektuak; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Blackspot is a subsurface potato damage resulting from impacts during harvesting. This type of bruising represents substantial economic losses every year. As the tubers do not show external symptoms, bruise detection in potatoes is not straightforward. Therefore, a nondestructive and accurate method capable of identifying bruised tubers is needed. Hyperspectral imaging (HSI) has been shown to be able to detect other subsurface defects such as bruises in apples. This method is nondestructive, fast and can be fully automated. Therefore, its potential for non-destructive detection of blackspot in potatoes has been investigated in this study. Two HSI setups were used, one ranging from 400 to 1000 nm, named VisibleNear Infrared (Vis-NIR) and another covering the 1000e2500 nm range, called Short Wave Infrared (SWIR). 188 samples belonging to 3 different varieties were divided in two groups. Bruises were manually induced and samples were analyzed 1, 5, 9 and 24 h after bruising. PCA, SIMCA and PLS-DA were used to build classifiers. The PLS-DA model performed better than SIMCA, achieving an overall correct classification rate above 94% for both hyperspectral setups. Furthermore, more accurate results were obtained with the SWIR setup at the tuber level (98.56 vs. 95.46% CC), allowing the identification of early bruises within 5 h after bruising. Moreover, the pixel based PLS- DA model achieved better results in the SWIR setup in terms of correctly classified samples (93.71 vs. 90.82% CC) suggesting that it is possible to detect blackspot areas in each potato tuber with high accuracy.
Open Access
A systematized review on the applications of hyperspectral imaging for quality control of potatoes
(Springer, 2024) Peraza Alemán, Carlos Miguel; López Maestresalas, Ainara; Jarén Ceballos, Carmen; Rubio Padilla, Niuton; Arazuri Garín, Silvia; Ingeniería; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
The application of hyperspectral imaging (HSI) has gained signifcant importance in the past decade, particulary in the context of food analysis, including potatoes. However, the current literature lacks a comprehensive systematic review of the application of this technique in potato cultivation. Therefore, the aim of this work was to conduct a systematized review by analysing the most relevant compounds, diseases and stress factors in potatoes using hyperspectral imaging. For this purpose, scientifc studies were retrieved through a systematic keyword search in Web of Science and Scopus databases. Studies were only included in the review if they provided at least one set of quantitative data. As a result, a total of 52 unique studies were included in the review. Eligible studies were assigned an in-house developed quality scale identifying them as high, medium or low risk. In most cases the studies were rated as low risk. Finally, a comprehensive overview of the HSI applications in potatoes was performed. It has been observed that most of the selected studies obtained better results using linear methods. In addition, a meta-analysis of studies based on regression and classifcation was attempted but was not possible as not enough studies were found for a specifc variable.