Active Vision for Agriculture
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Taeyeong Choi | |
University of California, Davis |
Project's details
Active Vision for Agriculture | |
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Students working on this project will produce a large image dataset for research at the intersection of computer vision and robotics particularly in agricultural scenarios. The goal is to utilize existing plant simulation frameworks such as “Helios” and “AgML” to generate useful data for benchmarking future studies on autonomous agents in the crop fields. More specifically, the project team will work to: - Implement an interface with “AgML” to build an automated data-generation pipeline - Visualize examples of simulated images with corresponding semantic annotations - Document descriptions of produced codes and data as well as environmental settings - Communicate with collaborators in the Department of Biological and Agricultural Engineering |
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- A dataset of synthesized plant images with various labels - Comprehensive documentation of the dataset for future use in research communities - Python program with a written manual on data generation under particular requirements |
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- Significant programming/development skill in Python is required; - Familiarity with data science-related tools–such as NumPy, Pandas, scikit-learn, Matplotlib, Jupyter Notebook, etc.–is a huge plus; - Hands-on experience with machine-learning projects in computer vision is a huge plus; - Knowledge of deep reinforcement learning or robot motion planning is a plus; Experience with TensorFlow/PyTorch is a plus |
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30-60 min weekly or more | |
Open source project | |
Attachment | Click here |
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Team members | N/A |
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