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Published on: February 2, 2019
Design and implementation of a high-throughput field phenotyping robot for acquiring multisensor data in wheat.
Miao Su1, Dong Zhou1, Yaze Yun1
1National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making (Ministry of Agriculture and Rural Affairs), Engineering Research Center of Smart Agriculture (Ministry of Education), Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, China.
This study introduces an adaptable phenotyping robot for enhanced crop genetic research. The robot features adjustable track width and a high-payload gimbal, improving data collection for food security initiatives.
Area of Science:
- Agricultural Engineering
- Robotics
- Plant Science
Background:
- Food security is a global challenge exacerbated by climate change and population growth.
- High-throughput phenotyping is crucial for crop genetic enhancement to address food crises.
- Existing phenotyping robots face limitations in adaptability, load capacity, and real-time data fusion.
Purpose of the Study:
- To develop an advanced phenotyping robot addressing current technological challenges.
- To enhance crop monitoring capabilities for efficient genetic improvement.
- To provide robust equipment for high-throughput phenotyping.
Main Methods:
- Designed a gantry-style robot with adjustable wheeltrack (1400-1600 mm) for diverse row spacing and environments (dry/paddy fields).
- Integrated a six-degree-of-freedom sensor gimbal with high payload for precise height (1016-2096 mm) and angle adjustments.
- Implemented an enhanced data acquisition method using sensor registration and fusion (Zhang's calibration, feature point extraction) for multi-sensor data integration.
Main Results:
- The adjustable wheeltrack allows adaptation to different row spacings, reducing crop damage.
- The high-payload gimbal ensures precise sensor positioning for detailed data capture.
- The data registration algorithm achieved a root mean square error (RMSE) below 3 pixels.
- Gimbal sensor data showed strong correlation (r² > 0.90) with handheld instrument data.
Conclusions:
- The developed phenotyping robot is practical, reliable, and fully functional.
- The robot's design overcomes limitations of fixed track widths and low payload capacities.
- This research provides essential theoretical and equipment support for advancing high-throughput phenotyping and crop improvement.
