Automated Phenotyping of Corn

Date
2014-04-15
Authors
Naik, Hsiang Sing
Lee, Nigel
Major Professor
Advisor
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
Altmetrics
Authors
Research Projects
Organizational Units
Mechanical Engineering
Organizational Unit
Journal Issue
Series
Department
Mechanical Engineering
Abstract

Characteristics of corn cobs such as size, color, shape and number of kernels has allowed researchers to understand the fundamentals of the growth of a maize plant and how to change growth conditions to enable specific feature selection. However, most of these characteristics are currently being extracted manually from images. This is an extremely time-consuming process. Automating this process via image processing tools will enable fast and efficient phenotyping. We have developed an easy to use, GUI based program that allows researchers to automatically extract corn traits from images. To allow fast, efficient and accurate data extraction, this program's framework is optimized for simplicity, efficiency and utilizes RGB image feature extraction. This allows the program to accurately identify the feature of the corn, i.e. corn kernels and the cob.

Comments
Description
Keywords
Citation
DOI
Source