https://hdl.handle.net/10259/3848 2026-05-28T06:01:22Z 2026-05-28T06:01:22Z Sánchez Alonso, Ramón Rad Moradillo, Juan Carlos Cambra Baseca, Carlos Barros García, Rocío Herrero Cosío, Álvaro https://hdl.handle.net/10259/11732 2026-05-27T00:05:32Z 2025-12-01T00:00:00Z

Detecting copper-based fungicides in vineyards by means of hyperspectral imagery Sánchez Alonso, Ramón; Rad Moradillo, Juan Carlos; Cambra Baseca, Carlos; Barros García, Rocío; Herrero Cosío, Álvaro Fungal diseases affecting vineyards are commonly controlled using copper-based fungicides. Inaccurate application of these products usually leads to accumulations of copper in the soil. The use of spectral images in vineyards is a tool that can help in the correct application of fungicides to improve their efficiency and effectiveness. To do that, a solution is required to identify the copper deposited on the vine leaf. To bridge this gap, the present work compares images obtained with a hyperspectral camera (Pika L, Resonon) of vineyard leaves (Vitis vinifera L.) cv. Tempranillo treated with two copper-based products, Cuprantol duo (Syngenta, CH) and Cuprocol (Syngenta, CH). Treated leaves with both products and the corresponding blanks made with distilled water were compared. Most of the differences between treatments and products are found in the near-infrared region (700–740 nm), the green region (550 nm) and the region of (620–640 nm). Maximal spectral variations appeared in the range of 711.16–758.27 nm for wet status products, which allowed to differentiate between the areas treated with copper-based products from the blanks without product. We can conclude that using hyperspectral imagery is possible to detect leave areas treated with copper-based fungicides immediately (wet treatment) after application

2025-12-01T00:00:00Z Sánchez Alonso, Ramón Rad Moradillo, Juan Carlos Cambra Baseca, Carlos Castroviejo Fernández, Mª Pilar Barros García, Rocío Herrero Cosío, Álvaro https://hdl.handle.net/10259/11727 2026-05-27T00:05:34Z 2025-10-01T00:00:00Z

SEM-EDS and hyperspectral images of vine leaves treated with antifungal products Sánchez Alonso, Ramón; Rad Moradillo, Juan Carlos; Cambra Baseca, Carlos; Castroviejo Fernández, Mª Pilar; Barros García, Rocío; Herrero Cosío, Álvaro Scanning electron microscope, better known by its acronym as SEM, is a very useful technique for obtaining highresolution images of the surface of a sample. Hyperspectral imaging provides precise information for analysing vineyard vegetation that could help in improving pesticide application in precision viticulture technics. The present dataset is based on images of vineyard leaves, taken with both technics. The leaves of the cv. Tempranillo, proceeding from a vineyard located inside of the Cigales Denomination of Origin, in north-central Spain, were treated with two Cu-containing products: ZZ Cuprocol (70 % w/v copper oxychloride) and Cuprantol Duo (14 % w/w copper oxychloride, 14 % w/w copper hydroxide). In addition, a contact pesticide widely used in intensive and traditional viticulture based on Folpet, copper-free but containing sulphur and chlorine, has been tested in its commercial form, Vitipec Blue (Cymoxanil 6 % w/w, Folpet 37.5 % w/w, Ascenza, PT). Three dilutions were prepared, one of each compound, at the actual field application concentration of 1.33 g/L. The leaves were sampled and processed during the 2023 season. These leaves were taken from the central part of representative shoots of the vine canopy, with east and west exposures. After the application of the pesticide dilutions, images of the leaves were taken with a 300-channel hyperspectral camera (Pika L, Resonon) using a mechanical bench synchronized with the camera. Then the SEM analysis was carried after prepare the samples. Hence, such imagery is provided in the present dataset, based on the images taken from the leaves with both technics

2025-10-01T00:00:00Z Antón Maraña, Paula Baruque Zanón, Bruno Porras Alfonso, Santiago Arranz Val, Pablo https://hdl.handle.net/10259/10938 2025-10-09T00:05:34Z 2023-01-01T00:00:00Z

Tourism Reputation Index for Assessing Perceptions on Destinations Using Collaborative Text Data Antón Maraña, Paula; Baruque Zanón, Bruno; Porras Alfonso, Santiago; Arranz Val, Pablo The tourism sector is experiencing a change in trend due to the widespread use of Web 2.0 by tourists. This phenomenon has prompted tourism agents to apply Big Data techniques and Business Intelligence () tools to find out what tourists think. In this sense, the analysis of the Electronic Word Of Mouth in Social Networks is particularly relevant, and the academic literature has faced some difficulties in extracting data, determining its meaning and linking it to the destination. The aim of this work is to is to present the results that can be generated by the design of a tourism reputation index, through the analysis of data created collaboratively on the Twitter Social Network. For this purpose, a BI tool has been created to extract data from short text messages posted by users (tweets) and process them by implementing an Extract, Transform and Load process. In this process, Natural Language Processing is carried out, focusing on Sentiment Analysis tasks, applying an automatic classification model (Multinomial Naive Bayes) and then an automatic thematic categorisation of these texts depending on the words they contain. Subsequently, the data is analysed, calculating a global online reputation index based on sub-indexes of perception on different tourism aspects. These ratings are shown to users through an interactive dashboard, which allows comparison between different queries associated with a tourist location and a temporal period. This research helps tourism agents to better understand the public perception about destinations and make appropriate and well-informed decisions in real time, facilitating the intelligent management of data that enables the creation of smart tourism destinations with competitive, unique and sustainable advantages.; El sector turístico está experimentando un cambio de tendencia ante el uso generalizado de la Web 2.0. por parte de los turistas. Este fenómeno ha impulsado a los agentes turísticos a aplicar técnicas Big Data y herramientas de Inteligencia de Negocio (IN) a la hora de conocer qué piensan y qué desean los turistas. En este sentido, toma especial relevancia el análisis del Electronic Word Of Mouth en las Redes Sociales, y la literatura académica ha encontrado algunas dificultades al extraer datos, determinar su significado y vincularlo con el destino. El objetivo de este trabajo es presentar los resultados que se pueden obtener con el diseño de un índice reputacional turístico, por medio del análisis de los datos creados de manera colaborativa en la Red Social de Twitter. Para ello, se ha creado una herramienta de IN que permite extraer datos de los mensajes cortos de texto publicados por los usuarios (tweets) y procesarlos mediante la implementación de un proceso de Extract, Transform and Load. En este proceso, se realiza un Procesamiento del Lenguaje Natural, centrado en tareas de Análisis de Sentimientos, aplicando un modelo de clasificación automática (Multinomial Naive Bayes) y, seguidamente, una categorización temática automática de estos textos en función de los términos que contengan. Posteriormente, se analizan los datos, calculando un índice de reputación online global a partir de subíndices de percepción sobre diferentes aspectos turísticos. Estos índices se muestran a los usuarios mediante un cuadro de mando interactivo, que permite comparar entre diferentes consultas asociadas a una localidad turística y a un periodo temporal. Este trabajo contribuye a que los agentes turísticos conozcan la percepción sobre los destinos y tomen decisiones adecuadas e informadas en tiempo real, facilitando una gestión inteligente de los datos que permita crear destinos turísticos inteligentes con ventajas competitivas, únicas y sostenibles.

2023-01-01T00:00:00Z Nascimento, Antonia Maiara Marques do Ruiz González, Rubén Martínez-Martínez, Víctor Medeiros, Artur Mendes Santos, Fábio Sandro dos Rêgo, Elizanilda Ramalho do Pimenta, Samy Sudré, Cláudia Pombo Bento, Cintia dos Santos Cambra Baseca, Carlos Barroso, Priscila Alves https://hdl.handle.net/10259/10875 2025-09-16T00:05:35Z 2025-07-01T00:00:00Z

Ornamental Potential Classification and Prediction for Pepper Plants (Capsicum spp.): A Comparison Using Morphological Measurements and RGB Images as Data Source Nascimento, Antonia Maiara Marques do; Ruiz González, Rubén; Martínez-Martínez, Víctor; Medeiros, Artur Mendes; Santos, Fábio Sandro dos; Rêgo, Elizanilda Ramalho do; Pimenta, Samy; Sudré, Cláudia Pombo; Bento, Cintia dos Santos; Cambra Baseca, Carlos; Barroso, Priscila Alves Anticipating the ornamental quality of plants is of significant importance for genetic breeding programs. This study investigated the potential of predicting and classifying whether ornamental pepper plants will exhibit desirable ornamental traits based on RGB images, comparing these results with an approach relying on morphological measurements. To achieve this, pepper plants from fifteen accessions were cultivated, and photographs were taken weekly throughout their growth cycle until fruit maturation. A Vision Transformer (ViT)-based model was employed to predict the suitability of the plants for ornamental purposes, and its predictions were validated against assessments conducted by eight experts. An XGBoost-based classifier was employed as well for estimations based on morphological measurements with an accuracy over 92%. The results showed that the ornamental suitability of plants can be accurately estimated and predicted up to seven weeks in advance from photos, with accuracy over 80%. Interestingly, higher-resolution RGB images did not significantly improve the accuracy of the ViT model. Furthermore, the estimation of ornamental potential using morphological measurements and RGB images yielded similar accuracy, indicating that a single photograph can effectively replace costly and time-consuming morphological measurements. As far as the authors are aware, this work is the first to forecast the ornamental potential of pepper plants (Capsicum spp.) multiple weeks ahead of time using image-based deep learning models.

2025-07-01T00:00:00Z