Characterizing corn growth and development using computer vision

Efficient utilization of agricultural resources requires a better understanding of crop growth and development. Current modeling efforts aimed at predicting the response of plants to environmental conditions lack the ability to relate results to basic characteristics observed in the field. The ability to reliably evaluate both photometric and morphometric parameters for individual plants would not only improve existing models, but also create a database from which new models may be generated.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Agricultural Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2011

Author dc:creator

• Tarbell, Kenneth Alvin

Contributors dc:contributor

• Reid, John F.

• Agriculture, General
• Engineering, Agricultural
• Artificial Intelligence
• Biology, Plant Physiology

Statement dc:rights

• Copyright 1990 Tarbell, Kenneth Alvin

Language dc:language

eng

Identifier

AAI9114433
(UMI)AAI9114433

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/20757