Sensible Solutions
Project number:
22059
Sponsor:
Bayer Crop Science
Academic year:
2021-2022
Modern agriculture relies heavily on reactive farming, in which mitigatory actions are taken based on human observation of plant conditions. Advances in technology have begun to pave the way for predictive measurements in farming, but the technology is not yet commercially available.
Greenhouse technicians at Bayer Crop Science will use the imaging system developed by this team to access real-time data in analyzing growth factors that determine the readiness of each individual plant to move to the next stage of crop development.
The system design is centered around an Intel RealSense L515 camera capable of taking high-resolution RGB images as well as generating point particle cloud data using lidar, or Light Detection and Ranging, technology. The point particle cloud provides a three-dimensional representation of the target seedlings that is used to measure plant height with a high degree of accuracy.
A Raspberry Pi module controls the Intel camera calibration and data processing, which is outputted to an LCD touchscreen graphic user interface that allows Bayer staff to store, access and manipulate collected data.
The team developed a protective wooden frame surrounding the camera, mount and imaging area. The frame is mounted to the top of a utility cart to provide mobility and ease of access around Bayer’s facility.
Greenhouse technicians at Bayer Crop Science will use the imaging system developed by this team to access real-time data in analyzing growth factors that determine the readiness of each individual plant to move to the next stage of crop development.
The system design is centered around an Intel RealSense L515 camera capable of taking high-resolution RGB images as well as generating point particle cloud data using lidar, or Light Detection and Ranging, technology. The point particle cloud provides a three-dimensional representation of the target seedlings that is used to measure plant height with a high degree of accuracy.
A Raspberry Pi module controls the Intel camera calibration and data processing, which is outputted to an LCD touchscreen graphic user interface that allows Bayer staff to store, access and manipulate collected data.
The team developed a protective wooden frame surrounding the camera, mount and imaging area. The frame is mounted to the top of a utility cart to provide mobility and ease of access around Bayer’s facility.
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