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Found 8 papers, 0 papers with code
Accurate Cutting-point Estimation for Robotic Lychee Harvesting through Geometry-aware Learning
Accurately identifying lychee-picking points in unstructured orchard environments and obtaining their coordinate locations is critical to the success of lychee-picking robots.
Exploring Accurate 3D Phenotyping in Greenhouse through Neural Radiance Fields
This study investigates a learning-based phenotyping method using the Neural Radiance Field to achieve accurate in-situ phenotyping of pepper plants in greenhouse environments.
High-fidelity 3D Reconstruction of Plants using Neural Radiance Field
Recently, a promising development has emerged in the form of Neural Radiance Field (NeRF), a novel method that utilises neural density fields.
Semantic Segmentation of Fruits on Multi-sensor Fused Data in Natural Orchards
In this work, we propose a deep-learning-based segmentation method to perform accurate semantic segmentation on fused data from a LiDAR-Camera visual sensor.
Geometry-Aware Fruit Grasping Estimation for Robotic Harvesting in Orchards
It is vital for robots to recognise and localise fruits before the harvesting in natural orchards.
Real-Time Fruit Recognition and Grasping Estimation for Autonomous Apple Harvesting
In this research, a fully neural network based visual perception framework for autonomous apple harvesting is proposed.
Visual Perception and Modelling in Unstructured Orchard for Apple Harvesting Robots
This paper develops a framework of visual perception and modelling for robotic harvesting of fruits in the orchard environments.
Fruit Detection, Segmentation and 3D Visualisation of Environments in Apple Orchards
The robustness and efficiency of the DaSNet-V2 in detection and segmentation are validated by the experiments in the real-environment of apple orchard.
