AIMC Topic: Unmanned Aerial Devices

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DASNet a dual branch multi level attention sheep counting network.

Scientific reports Jul 2, 2025
Grassland sheep counting is essential for both animal husbandry and ecological balance. Accurate population statistics help optimize livestock management and sustain grassland ecosystems. However, traditional counting methods are time-consuming and c...
Unmanned Aerial Devices Neural Networks, Computer Animals Animal Husbandry Sheep Grassland Deep Learning
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Explainable few-shot learning workflow for detecting invasive and exotic tree species.

Scientific reports Jul 2, 2025
Deep Learning methods are notorious for relying on extensive labeled datasets to train and assess their performance. This can cause difficulties in practical situations where models should be trained for new applications for which very little data is...
Algorithms Brazil Deep Learning Neural Networks, Computer Trees Introduced Species Unmanned Aerial Devices Workflow Forests
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MAVSD: A Multi-Angle View Segmentation Dataset for Detection of Solidago Canadensis L.

Scientific data May 24, 2025
Recent advancements in computer vision and deep learning have advanced automated vegetation monitoring, creating new opportunities for invasive species management. To this end, we introduce MAVSD (Multi-Angle View Segmentation Dataset), specifically ...
Deep Learning Unmanned Aerial Devices Solidago Environmental Monitoring Introduced Species
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Path planning algorithm for logistics autonomous vehicles at Cainiao stations based on multi-sensor data fusion.

PloS one May 20, 2025
Efficient path planning and obstacle avoidance in a complex and dynamic environment is one of the key challenges of unmanned vehicle logistics distribution, especially in the logistics scene of Cainiao Station, which involves crowded communities and ...
Humans Models, Theoretical Unmanned Aerial Devices Neural Networks, Computer Algorithms
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Bio-inspired motion detection models for improved UAV and bird differentiation: a novel deep learning framework.

Scientific reports May 3, 2025
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 ...
Animals Flight, Animal Birds Neural Networks, Computer Unmanned Aerial Devices Deep Learning Motion
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Estimating soil cadmium concentration using multi-source UAV imagery and machine learning techniques.

Environmental monitoring and assessment Apr 24, 2025
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...
Cadmium Unmanned Aerial Devices Remote Sensing Technology Soil Pollutants Soil Environmental Monitoring Machine Learning
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Estimating Tea Plant Physiological Parameters Using Unmanned Aerial Vehicle Imagery and Machine Learning Algorithms.

Sensors (Basel, Switzerland) Mar 21, 2025
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...
Unmanned Aerial Devices Remote Sensing Technology Photosynthesis Camellia sinensis Tea Algorithms Machine Learning Taiwan Plant Leaves
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Modeling crash avoidance behaviors in vehicle-pedestrian near-miss scenarios: Curvilinear time-to-collision and Mamba-driven deep reinforcement learning.

Accident; analysis and prevention Mar 4, 2025
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...
Humans Computer Simulation Deep Learning Algorithms Safety Reinforcement Machine Learning Environment Design Time Factors Unmanned Aerial Devices Pedestrians Models, Theoretical Accidents, Traffic Avoidance Learning
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Enhanced recognition and counting of high-coverage Amorphophallus konjac by integrating UAV RGB imagery and deep learning.

Scientific reports Feb 22, 2025
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...
Remote Sensing Technology Crops, Agricultural Unmanned Aerial Devices Image Processing, Computer-Assisted Deep Learning Agriculture Amorphophallus
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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 Feb 13, 2025
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...
Remote Sensing Technology Deep Learning Plant Diseases Unmanned Aerial Devices Image Processing, Computer-Assisted Beta vulgaris Artificial Intelligence Machine Learning
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