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Towards 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 PDFCabbageNet: Deep Learning for High-Precision Cabbage Segmentation in Complex Settings for Autonomous Harvesting Robotics.
Sensors (Basel)
December 2024
School of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China.
Reducing damage and missed harvest rates is essential for improving efficiency in unmanned cabbage harvesting. Accurate real-time segmentation of cabbage heads can significantly alleviate these issues and enhance overall harvesting performance. However, the complexity of the growing environment and the morphological variability of field-grown cabbage present major challenges to achieving precise segmentation.
View Article and Find Full Text PDFDevelopment of a Collision-Free Path Planning Method for a 6-DoF Orchard Harvesting Manipulator Using RGB-D Camera and Bi-RRT Algorithm.
Sensors (Basel)
December 2024
Institute of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8577, Japan.
With the decreasing and aging agricultural workforce, fruit harvesting robots equipped with higher degrees of freedom (DoF) manipulators are seen as a promising solution for performing harvesting operations in unstructured and complex orchard environments. In such a complex environment, guiding the end-effector from its starting position to the target fruit while avoiding obstacles poses a significant challenge for path planning in automatic harvesting. However, existing studies often rely on manually constructed environmental map models and face limitations in planning efficiency and computational cost.
View Article and Find Full Text PDFEnhance the Concrete Crack Classification Based on a Novel Multi-Stage YOLOV10-ViT Framework.
Sensors (Basel)
December 2024
College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210046, China.
Early identification of concrete cracks and multi-class detection can help to avoid future deformation or collapse in concrete structures. Available traditional detection and methodologies require enormous effort and time. To overcome such difficulties, current vision-based deep learning models can effectively detect and classify various concrete cracks.
View Article and Find Full Text PDFArrhythmia Detection by Data Fusion of ECG Scalograms and Phasograms.
Sensors (Basel)
December 2024
Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy.
The automatic detection of arrhythmia is of primary importance due to the huge number of victims caused worldwide by cardiovascular diseases. To this aim, several deep learning approaches have been recently proposed to automatically classify heartbeats in a small number of classes. Most of these approaches use convolutional neural networks (CNNs), exploiting some bi-dimensional representation of the ECG signal, such as spectrograms, scalograms, or similar.
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