A5089144290- Smart Agriculture and AI
- Industrial Vision Systems and Defect Detection
- Spectroscopy and Chemometric Analyses
- Remote Sensing in Agriculture
- AI in cancer detection
- Plant Pathogens and Fungal Diseases
- Species Distribution and Climate Change
- Advanced Image and Video Retrieval Techniques
- Currency Recognition and Detection
- 3D Surveying and Cultural Heritage
- Plant Pathogenic Bacteria Studies
- Radiomics and Machine Learning in Medical Imaging
- Advanced Neural Network Applications
- Remote-Sensing Image Classification
- Medical Image Segmentation Techniques
- Agricultural and Food Production Studies
- Brain Tumor Detection and Classification
- Plant Disease Management Techniques
- Plant Virus Research Studies
- Leaf Properties and Growth Measurement
- Date Palm Research Studies
- Rabbits: Nutrition, Reproduction, Health
- Optical measurement and interference techniques
- Image and Object Detection Techniques
- Food Supply Chain Traceability
Euskadiko Parke Teknologikoa
2015-2025
Boeing (Spain)
2023
Digital Research Alliance of Canada
2023
Tecnalia
2010-2022
Robotiker
2004-2010
GAIKER Technology Centre
2004-2010
Infotech Soft (United States)
2010
Universitat Autònoma de Barcelona
2007
Alzheimer's is a degenerative dementing disorder that starts with mild memory impairment and progresses to total loss of mental physical faculties. The sooner the diagnosis made, better for patient, as preventive actions treatment can be started. Although tests such Mini-Mental State Tests Examination are usually used early identification, relies on magnetic resonance imaging (MRI) brain analysis.Public initiatives OASIS (Open Access Series Imaging Studies) collection provide neuroimaging...
Weeds compete with productive crops for soil, nutrients and sunlight are therefore a major contributor to crop yield loss, which is why safer more effective herbicide products continually being developed. Digital evaluation tools automate homogenize field measurements of vital importance accelerate their development. However, the development these requires generation semantic segmentation datasets, complex, time-consuming not easily affordable task. In this paper, we present deep learning...
Plant fungal diseases are one of the most important causes crop yield losses. Therefore, plant disease identification algorithms have been seen as a useful tool to detect them at early stages mitigate their effects. Although deep-learning based can achieve high detection accuracies, they require large and manually annotated image datasets that is not always accessible, specially for rare new diseases. This study focuses on development algorithm strategy requiring few images (Few-shot...
Este artículo tiene como objetivo analizar el conocimiento, la percepción, las motivaciones y opiniones que los jóvenes hidalguenses tienen sobre fast fashion o moda rápida, determinando aspectos estimularían consumo de sostenible. Se realizó una investigación cualitativa en junio 2024, mediante aplicación entrevistas a muestra quince entre 18 25 años Hidalgo, México. El análisis se codificación temática utilizando software MAXQDA 24. Los hallazgos indican concepto está relacionado con...
The application of hyperspectral sensors in the development machine vision solutions has become increasingly popular as spectral characteristics imaged materials are better modeled domain than standard trichromatic red, green, blue data. While there is no doubt that availability detailed information opportune it opens possibility to construct robust image descriptors, also raises a substantial challenge when this high-dimensional data used real-time systems. To alleviate computational...
Performing accurate and automated semantic segmentation of vegetation is a first algorithmic step towards more complex models that can extract biological information on crop health, weed presence phenological state, among others. Traditionally, based normalized difference index (NDVI), near infrared channel (NIR) or RGB have been good indicator presence. However, these methods are not suitable for accurately segmenting showing damage, which precludes their use downstream phenotyping...
The use of digital technologies and artificial intelligence techniques for the automation some visual assessment processes in agriculture is currently a reality. Image-based, recently deep learning-based systems are being used several applications. Main challenge these applications to achieve correct performance real field conditions over images that usually acquired with mobile devices thus offer limited quality. Plagues control problem be tackled field. Pest management strategies relies on...
The advent of new hyperspectral imaging modalities made possible the implementation flexible machine vision systems that can be designed to solve a variety industrial tasks such as automatic material sorting. However design robust is far from trivial task several issues including mechanical design, development an appropriate illumination set-up, optimal interfacing between sensing and optical equipment with computer component have properly addressed in order accommodate all challenges are...
Quality control of products is everyday more and demanding. Machine vision becoming one the most efficient technologies for reliable fast different types products. The classical techniques in machine 2D are valuables lots applications, but insufficient when it necessary a three-dimensional data object to study. Classical linear 3D laser detection scanners not optimized revolution elements, since features extraction algorithm needs be each inspection zone there shadow zones where possible. In...
Estimation of damage in plants is a key issue for crop protection. Currently, experts the field manually assess plots. This time-consuming task that can be automated thanks to latest technology computer vision (CV). The use image-based systems and recently deep learning-based have provided good results several agricultural applications. These applications outperform expert evaluation controlled environments, now they are being progressively included non-controlled A novel solution based on...
Hyperspectral imaging, a rapidly evolving field, has witnessed the ascendancy of deep learning techniques, supplanting classical feature extraction and classification methods in various applications. However, many researchers employ arbitrary architectures for hyperspectral image processing, often without rigorous analysis interplay between spectral spatial information. This oversight neglects implications combining these two modalities on model performance. In this paper, we evaluate...