The mechanical and algorithmic design of in-field robotic leaf sampling device

Wu, Junzhe

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https://hdl.handle.net/2142/110652 Copy

Description

Title

The mechanical and algorithmic design of in-field robotic leaf sampling device

Author(s)

Wu, Junzhe

Issue Date

2021-04-28

Director of Research (if dissertation) or Advisor (if thesis)

Chowdhary, Girish

Committee Member(s)

• Allen, Cody Michael
• Stasiewicz, Matthew Jon

Department of Study

Engineering Administration

Discipline

Agricultural & Biological Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Date of Ingest

2021-09-17T02:34:23Z

Keyword(s)

• Leaf sampling
• End effector
• Neural network
• Sensor fusion.

Abstract

Leaf samples analysis is a significant tool to acquire the actual nutrition information of crops. After that, farmers can adjust fertilization programs to prevent nutritional problems and improve the yield of crops. The traditional way for leaf sampling is manual, and researchers need to go to the field and use paper hole punchers with a catch-tube to collect leaf samples. The temperature in summer is hot, and some crop like corn is difficult for researchers to walk through, therefore the manual way of leaf sampling is not a good option. In this thesis, an automatic method of leaf sampling is presented to solve the difficulty of leaf sampling. The contributions of this thesis are the following: (1) Build the end effector of leaf sampling device to punch and store leaf samples separately, (2) Train a neural network to detect the leaves with high horizontal level, (3) Combine point cloud data from the depth camera and vison data from the camera via the sensor fusion to get the leaf rolling angle and grasp point. The method in this thesis can produce a consistent leaf rolling angle estimate quantitatively and qualitatively on multiple corn leaves, especially on leaves with multiple different angles.

Graduation Semester

2021-05

Type of Resource

Thesis

Permalink

http://hdl.handle.net/2142/110652

Copyright and License Information

Copyright 2021 Junzhe Wu

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