This project builds upon the previous part 4 project focused on disease detection using machine learning. A notable gap in the prior work was its sensitivity to lighting conditions. In the previous proposal, a multispectral camera was employed to identify spider mites on beans, with handheld Halogen lights. This project aims to develop a housing unit where the halogen/LED light is seamlessly integrated with the camera and a prove of concept that the current system can improve the result of disease detection.
The student involved will be responsible for gathering multispectral/NIR data from plants and annotating them. Subsequently, they will delve into the creation of a deep learning-based computer vision model geared towards disease detection on both leaves and stems. This initiative will provide hands-on experience in integrating lights with multispectral cameras. The students will find the suitable combination of lighting to have a consistent disease detection results.
Execution of this project will predominantly involve Python and/or C++ within a Robotic Operating System (ROS) framework. Additionally, there may be opportunities to leverage existing Python projects from the CARES and MaaraTech repositories, potentially streamlining aspects of the research.
Undergraduate
· Create a design for integrating Halogen/LED lights with the NIR/RGB camera
· Enhance the effectiveness of disease detection with the prototype
· Collect a dataset with different lighting conditions
None
Robotics (405.652, Lab)