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Unmanned Aerial Devices

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SugarViT-Multi-objective regression of UAV images with Vision Transformers and Deep Label Distribution Learning demonstrated on disease severity prediction in sugar beet.

PloS one
Remote sensing and artificial intelligence are pivotal technologies of precision agriculture nowadays. The efficient retrieval of large-scale field imagery combined with machine learning techniques shows success in various tasks like phenotyping, wee...

U3UNet: An accurate and reliable segmentation model for forest fire monitoring based on UAV vision.

Neural networks : the official journal of the International Neural Network Society
Forest fires pose a serious threat to the global ecological environment, and the critical steps in reducing the impact of fires are fire warning and real-time monitoring. Traditional monitoring methods, like ground observation and satellite sensing, ...

Paying more attention on backgrounds: Background-centric attention for UAV detection.

Neural networks : the official journal of the International Neural Network Society
Under the advancement of artificial intelligence, Unmanned Aerial Vehicles (UAVs) exhibit efficient flexibility in military reconnaissance, traffic monitoring, and crop analysis. However, the UAV detection faces unique challenges due to the UAV's sma...

Combining a Standardized Growth Class Assessment, UAV Sensor Data, GIS Processing, and Machine Learning Classification to Derive a Correlation with the Vigour and Canopy Volume of Grapevines.

Sensors (Basel, Switzerland)
Assessing vines' vigour is essential for vineyard management and automatization of viticulture machines, including shaking adjustments of berry harvesters during grape harvest or leaf pruning applications. To address these problems, based on a standa...

Field-scale detection of Bacterial Leaf Blight in rice based on UAV multispectral imaging and deep learning frameworks.

PloS one
Bacterial Leaf Blight (BLB) usually attacks rice in the flowering stage and can cause yield losses of up to 50% in severely infected fields. The resulting yield losses severely impact farmers, necessitating compensation from the regulatory authoritie...

Enhanced recognition and counting of high-coverage Amorphophallus konjac by integrating UAV RGB imagery and deep learning.

Scientific reports
Accurate counting of Amorphophallus konjac (Konjac) plants can offer valuable insights for agricultural management and yield prediction. While current studies have primarily focused on detecting and counting crop plants during the early stages of low...

Estimating Tea Plant Physiological Parameters Using Unmanned Aerial Vehicle Imagery and Machine Learning Algorithms.

Sensors (Basel, Switzerland)
Tea ( L.) holds agricultural economic value and forestry carbon sequestration potential, with Taiwan's annual tea production exceeding TWD 7 billion. However, climate change-induced stressors threaten tea plant growth, photosynthesis, yield, and qual...

Modeling crash avoidance behaviors in vehicle-pedestrian near-miss scenarios: Curvilinear time-to-collision and Mamba-driven deep reinforcement learning.

Accident; analysis and prevention
Interactions between vehicle-pedestrian at intersections often lead to safety-critical situations. This study aims to model the crash avoidance behaviors of vehicles during interactions with pedestrians in near-miss scenarios, contributing to the dev...

Estimating soil cadmium concentration using multi-source UAV imagery and machine learning techniques.

Environmental monitoring and assessment
Urbanization and industrialization have led to widespread soil heavy metals contamination, posing significant risks to ecosystems and human health. Conventional methods for mapping heavy metal distribution, which rely on soil sampling followed by che...

Bio-inspired motion detection models for improved UAV and bird differentiation: a novel deep learning framework.

Scientific reports
The rapid increase in Unmanned Aerial Vehicle (UAV) deployments has led to growing concerns about their detection and differentiation from birds, particularly in sensitive areas like airports. Existing detection systems often struggle to distinguish ...