Robotic 3D Plant Perception and Leaf Probing with Collision-Free Motion Planning for Automated Indoor Plant Phenotyping
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Since 1905, the Department of Agricultural Engineering, now the Department of Agricultural and Biosystems Engineering (ABE), has been a leader in providing engineering solutions to agricultural problems in the United States and the world. The department’s original mission was to mechanize agriculture. That mission has evolved to encompass a global view of the entire food production system–the wise management of natural resources in the production, processing, storage, handling, and use of food fiber and other biological products.
History
In 1905 Agricultural Engineering was recognized as a subdivision of the Department of Agronomy, and in 1907 it was recognized as a unique department. It was renamed the Department of Agricultural and Biosystems Engineering in 1990. The department merged with the Department of Industrial Education and Technology in 2004.
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1905–present
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- Department of Agricultural Engineering (1907–1990)
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- College of Agriculture and Life Sciences (parent college)
- College of Engineering (parent college)
- Department of Industrial Education and Technology, (merged, 2004)
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Abstract
Various instrumentation devices for plant physiology study such as chlorophyll fluorimeter and Raman spectrometer require leaf probing with accurate probe positioning and orientation with respect to leaf surface. In this work, we aimed to automate this process with a Kinect V2 sensor, a high-precision 2D laser profilometer, and a 6-axis robotic manipulator in a high-throughput manner. The relatively wide field of view and high resolution of Kinect V2 allowed rapid capture of the full 3D environment in front of the robot. Given the number of plants, the location and size of each plant were estimated by K-means clustering. A real-time collision-free motion planning framework based on Probabilistic Roadmap was adopted to maneuver the robotic manipulator without colliding with the plants. Each plant was scanned from top with the short-range profilometer to obtain a high-precision point cloud where potential leaf clusters were extracted by region growing segmentation. Each leaf segment was further partitioned into small patches by Voxel Cloud Connectivity Segmentation. Only the small patches with low root mean square values of plane fitting were used to compute probing poses. To evaluate probing accuracy, a square surface was scanned at various angles and its centroid was probed perpendicularly with a probing position error of 1.5 mm and a probing angle error of 0.84 degrees on average. Our growth chamber leaf probing experiment showed that the average motion planning time was 0.4 seconds and the average traveled distance of tool center point was 1 meter.
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This proceeding is published as Bao, Yin, Lie Tang, and Dylan Shah. "Robotic 3D Plant Perception and Leaf Probing with Collision-Free Motion Planning for Automated Indoor Plant Phenotyping." ASABE Annual International Meeting, Spokane, WA, July 16-19, 2017. Paper No.1700369. DOI: 10.13031/aim.201700369. Posted with permission.