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A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions
Published on: August 5, 2020
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A Review of High-Throughput Field Phenotyping Systems: Focusing on Ground Robots
Rui Xu1, Changying Li1,2
1Bio-Sensing and Instrumentation Laboratory, College of Engineering, The University of Georgia, Athens, USA.
Plant Phenomics (Washington, D.C.)
|September 5, 2022
Summary
High-throughput field phenotyping systems, especially autonomous ground robots, automate plant data collection. This review details robotic systems, their components, navigation, and applications, highlighting challenges and future directions for efficient crop research.
Area of Science:
- Agricultural Engineering
- Robotics
- Plant Science
Background:
- Manual field phenotyping is inefficient and labor-intensive.
- High-throughput field phenotyping systems automate data collection.
- Robotic systems enable measurement of novel, fine-scale plant traits.
Purpose of the Study:
- To review the state-of-the-art in high-throughput field phenotyping systems.
- To focus specifically on autonomous ground robotic systems for phenotyping.
- To identify current challenges and future research directions in the field.
Main Methods:
- Review of non-autonomous ground phenotyping systems (tractors, carts, gantries).
- Detailed review of autonomous ground phenotyping robots, including components (platforms, sensors, manipulators, computing, software).
- Review of navigation algorithms, simulation tools, and applications in plant trait measurement and dataset collection.
Main Results:
- Overview of various non-autonomous and autonomous phenotyping systems.
- Detailed analysis of autonomous robot components and functionalities.
- Summary of navigation strategies and simulation tools for phenotyping robots.
- Examples of applications in plant trait measurement and data collection.
Conclusions:
- Autonomous ground robots are crucial for advancing high-throughput field phenotyping.
- Further research is needed to address current challenges in robotic phenotyping.
- Future directions include improved navigation, sensor integration, and data analysis for enhanced crop research.
