University of Texas at Arlington Uses Husky UGV to Automate Plant Phenotyping
Introduction:
Precision plant phenotyping is a crucial tool for breeders in modern agriculture, especially for upland cotton. With a significant economic impact of $123 billion annually in the United States alone, cotton production plays a vital role in global trade and the textile industry. To meet the demands of this industry, advanced technologies like robotics and computer vision are needed to accurately capture plant phenotypes. The Robotic Vision Laboratory at the University of Texas at Arlington is using the Husky UGV to automate cotton phenotyping. This technology allows for wide-scale and remote data collection, providing valuable insights that advance research in plant science.
Full Article: University of Texas at Arlington Uses Husky UGV to Automate Plant Phenotyping
Precision Plant Phenotyping Advances with Robotics and Computer Vision
Plant breeders rely on precision plant phenotyping to make informed decisions and improve crop production. Researchers at the Robotic Vision Laboratory at the University of Texas at Arlington are using advanced technologies, such as robotics and computer vision, to automate cotton phenotyping.
The Importance of Cotton Phenotyping
Upland cotton is a globally significant crop with a massive economic impact of $123 billion annually in the United States alone. With wide-scale genomic selection and breeding, advancements in cotton phenotyping are crucial for sustainable and resilient crop production.
Challenges in Plant Science
Manual crop measurements are labor-intensive, time-consuming, and prone to errors. To overcome this challenge, the researchers are using robotics to conduct field experiments in wide-scale and remote conditions. The Husky robot, equipped with high-resolution sensors, operates within cotton crop fields to capture precise plant phenotyping data.
Addressing Data Collection Challenges
Data collection must be consistently repeated throughout the growth process, which poses significant burdens when estimating plant traits on a large scale. Challenges arise from variable natural lighting, high temperatures, uncontrolled rainfall, and obstructed plant features. The researchers leverage data collected by the Husky robot to identify key plant features and develop machine learning algorithms for plant science advancements.
Custom-Designed Sensing Arm for Husky
The team mounted a custom-designed sensing arm on the Husky robot, equipped with dual stereoscopic cameras, an inertial measurement unit, and GPS sensors. Time-stamped data from various sensors is fused in real time using the NVIDIA Xavier sensor system, ensuring precise time synchronization. The addition of a stereo camera and LED light ring enables visually estimating odometry.
Deep Compression Architecture and Cotton Boll Detection
Using the data captured by Husky, the team developed a deep compression architecture to process large datasets efficiently while preserving essential features. They also created a framework to detect and count cotton bolls from infield video data. Husky’s top performance traits noted by the team were its reliability, flexibility, and runtime.
Future Endeavors and Partnerships
The team plans to enhance their research on estimating cotton yield using a stereoscopic multi-view system. They also aim to navigate the robot autonomously through dense crop rows using visual odometry and movement primitives. The ultimate goal is to identify reduced sensor payloads suitable for long-term deployments throughout the entire growing season.
Conclusion
The use of robotics and computer vision in precision plant phenotyping offers advanced technologies for gathering high-quality data and valuable insights. With automated data collection and analysis, researchers can make informed decisions to enhance crop production and contribute to sustainable agriculture. The collaboration between the Robotic Vision Laboratory and Texas universities holds promising prospects for the future of smart farming.
References:
– M.A.A. Muzaddid and W.J. Beksi, “NTrack: A Multiple-Object Tracker and Dataset for Infield Cotton Boll Counting,” IEEE Transactions on Automation Science and Engineering, 2023 (conditionally accepted).
– M.A.A Muzaddid and W.J. Beksi, “Variable Rate Compression for Raw 3D Point Clouds,” IEEE International Conference on Robotics and Automation (ICRA), pp. 8748-8755, 2022.
Summary: University of Texas at Arlington Uses Husky UGV to Automate Plant Phenotyping
Precision plant phenotyping plays a critical role in modern agriculture, particularly for crops like upland cotton. The Robotic Vision Laboratory at the University of Texas at Arlington is using advanced technologies, including robotics and computer vision, to automate cotton phenotyping. By using the Husky UGV robot, researchers are able to capture high-resolution plant phenotyping data in wide-scale and remote conditions. This data is then used to identify key plant features and develop machine learning algorithms for advancements in plant science. The team aims to enhance research on estimating cotton yield and autonomously navigating the robot through dense crop rows.
Frequently Asked Questions:
Q1: What is robotics?
A1: Robotics is a branch of technology that involves the design, construction, operation, and use of robots. It combines various fields such as mechanical engineering, electrical engineering, programming, and artificial intelligence to create machines with the ability to perform tasks autonomously or with human guidance.
Q2: How are robots programmed?
A2: Robots can be programmed using different methods, depending on the complexity of the task and the type of robot. The most common programming methods for robots include using programming languages like C++, Python, or Java, as well as visual programming interfaces that allow users to drag and drop code blocks. Additionally, some robots can learn and adapt through machine learning algorithms or be programmed through teaching by demonstration.
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Q5: What are the ethical considerations surrounding robotics?
A5: As robotics advances, there are ethical considerations that need to be addressed. Some concerns include potential job displacement due to automation, privacy and security risks related to robots collecting and analyzing personal data, and the responsibility and accountability for the actions of autonomous robots. Additionally, there is ongoing debate about the ethical implications of robots having artificial intelligence and decision-making capabilities, as well as the potential impact on human values and social interactions.
