This project addresses the growing need for precision agriculture techniques that can provide detailed, real-time insights into crop conditions. The use of such multispectral cameras, capable of capturing a broad spectrum of light including wavelengths beyond human vision, allows for the detection of subtle changes in plant health that are often invisible to the naked eye. The potential to employ these cameras on UAV Drones for autonomous data collection in agricultural fields further underscores its potential in transforming traditional farming practices, in the pursuit of more efficient, sustainable, and productive agriculture.
This Project includes to:
- Data Collection: Drones capture high-resolution spectral images.
- Point Cloud Formation: Images are processed into a point cloud.
- 3D reconstruction: Experiment with current SOA(state of the art) techniques for 3D reconstruction and explore new techniques such as Gaussian Splattering.
- Machine Learning Analysis: ML analysis on data point clouds/3D reconstruction.
- Pattern Recognition: Identifying health indicators like disease, moisture, nutrient level, and yield estimation among others of interest.
- Actionable Insights: Providing farmers with guidance for informed crop management.
Undergraduate
Producing a drone-based spectral imaging system holds significant promise for New Zealand's horticulture industry, such as sectors like kiwifruits which had an export value of approximately $2.6 billion as of 2023. The technology is particularly relevant for precision agriculture specifically in orchards, where accurate yield estimation and early disease detection are crucial for maintaining the quality and profitability of produce. By providing detailed analyses of health metrics like moisture, and nutrient levels, enhanced resource allocation and harvest planning can be enabled for farmers.
None
Robotics (405.652, Lab)