Search Results for author:
Found 15 papers, 4 papers with code
Lincoln's Annotated Spatio-Temporal Strawberry Dataset (LAST-Straw)
Automated phenotyping of plants for breeding and plant studies promises to provide quantitative metrics on plant traits at a previously unattainable observation frequency.
Key Point-based Orientation Estimation of Strawberries for Robotic Fruit Picking
Selective robotic harvesting is a promising technological solution to address labour shortages which are affecting modern agriculture in many parts of the world.
Crop Row Switching for Vision-Based Navigation: A Comprehensive Approach for Efficient Crop Field Navigation
Vision-based mobile robot navigation systems in arable fields are mostly limited to in-row navigation.
Leaving the Lines Behind: Vision-Based Crop Row Exit for Agricultural Robot Navigation
Usage of purely vision based solutions for row switching is not well explored in existing vision based crop row navigation frameworks.
LTS-NET: End-to-end Unsupervised Learning of Long-Term 3D Stable objects
In this research, we present an end-to-end data-driven pipeline for determining the long-term stability status of objects within a given environment, specifically distinguishing between static and dynamic objects.
Collection and Evaluation of a Long-Term 4D Agri-Robotic Dataset
Long-term autonomy is one of the most demanded capabilities looked into a robot.
Vision based Crop Row Navigation under Varying Field Conditions in Arable Fields
We also present a novel crop row detection algorithm for visual servoing in crop row fields.
Statistical shape representations for temporal registration of plant components in 3D
We present a landmark-free shape compression algorithm, which allows for the extraction of 3D shape features of leaves, characterises leaf shape and curvature efficiently in few parameters, and makes the association of individual leaves in feature space possible.
Deep learning-based Crop Row Detection for Infield Navigation of Agri-Robots
The novel crop row detection algorithm was tested for crop row detection performance and the capability of visual servoing along a crop row.
Towards Infield Navigation: leveraging simulated data for crop row detection
Our method could reach the performance of a deep learning based crop row detection model trained with real-world data by using 60% less labelled real-world data.
Gaussian map predictions for 3D surface feature localisation and counting
In this paper, we propose to employ a Gaussian map representation to estimate precise location and count of 3D surface features, addressing the limitations of state-of-the-art methods based on density estimation which struggle in presence of local disturbances.
3D shape sensing and deep learning-based segmentation of strawberries
In this paper, we evaluate modern sensing technologies including stereo and time-of-flight cameras for 3D perception of shape in agriculture and study their usability for segmenting out soft fruit from background based on their shape.
Self-supervised Representation Learning for Reliable Robotic Monitoring of Fruit Anomalies
Data augmentation can be a simple yet powerful tool for autonomous robots to fully utilise available data for selfsupervised identification of atypical scenes or objects.
Towards agricultural autonomy: crop row detection under varying field conditions using deep learning
This paper presents a novel metric to evaluate the robustness of deep learning based semantic segmentation approaches for crop row detection under different field conditions encountered by a field robot.
Adaptive Selection of Informative Path Planning Strategies via Reinforcement Learning
In our previous work, we designed a systematic policy to prioritize sampling locations to lead significant accuracy improvement in spatial interpolation by using the prediction uncertainty of Gaussian Process Regression (GPR) as "attraction force" to deployed robots in path planning.
