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A Systematic Review on the Advancements in Remote Sensing and Proximity Tools for Grapevine Disease Detection.
Sensors (Basel)
December 2024
Centre for the Research and Technology of Agro-Environmental and Biological Sciences, University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal.
Grapevines ( L.) are one of the most economically relevant crops worldwide, yet they are highly vulnerable to various diseases, causing substantial economic losses for winegrowers. This systematic review evaluates the application of remote sensing and proximal tools for vineyard disease detection, addressing current capabilities, gaps, and future directions in sensor-based field monitoring of grapevine diseases.
View Article and Find Full Text PDFSystematic Review of EEG-Based Imagined Speech Classification Methods.
Sensors (Basel)
December 2024
Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
This systematic review examines EEG-based imagined speech classification, emphasizing directional words essential for development in the brain-computer interface (BCI). This study employed a structured methodology to analyze approaches using public datasets, ensuring systematic evaluation and validation of results. This review highlights the feature extraction techniques that are pivotal to classification performance.
View Article and Find Full Text PDFTowards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data.
Sensors (Basel)
December 2024
School of Biological and Environmental Sciences, Liverpool John Moores University, James Parsons Building, Byrom Street, Liverpool L3 3AF, UK.
Camera traps offer enormous new opportunities in ecological studies, but current automated image analysis methods often lack the contextual richness needed to support impactful conservation outcomes. Integrating vision-language models into these workflows could address this gap by providing enhanced contextual understanding and enabling advanced queries across temporal and spatial dimensions. Here, we present an integrated approach that combines deep learning-based vision and language models to improve ecological reporting using data from camera traps.
View Article and Find Full Text PDFA Framework for Optimizing Deep Learning-Based Lane Detection and Steering for Autonomous Driving.
Sensors (Basel)
December 2024
School of Computing and Information Science, Anglia Ruskin University, Cambridge CB1 1PT, UK.
Improving the ability of autonomous vehicles to accurately identify and follow lanes in various contexts is crucial. This project aims to provide a novel framework for optimizing a self-driving vehicle that can detect lanes and steer accordingly. A virtual sandbox environment was developed in Unity3D that provides a semi-automated procedural road and driving generation framework for a variety of road scenarios.
View Article and Find Full Text PDFLane Detection Based on ECBAM_ASPP Model.
Sensors (Basel)
December 2024
School of Artificial Intelligence and Computer Science, Nantong University, Nantong 226019, China.
With the growing prominence of autonomous driving, the demand for accurate and efficient lane detection has increased significantly. Beyond ensuring accuracy, achieving high detection speed is crucial to maintaining real-time performance, stability, and safety. To address this challenge, this study proposes the ECBAM_ASPP model, which integrates the Efficient Convolutional Block Attention Module (ECBAM) with the Atrous Spatial Pyramid Pooling (ASPP) module.
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