Machine Vision Systems for Real-Time Plant Variability Sensing and in-Field Application

In-field variations associated with corn plant spacing, growth stage, and population can lead to a significant yield differences. Since the ability to reduce these variations is directly related to the planter performance, a machine vision-based emerged corn plant sensing system (ECS) was developed for the performance evaluation for prototype planters. With the real-time image sequencing capability, the system also achieved an average spacing measurement error of less than 10 mm.

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

2015

Author dc:creator

• Tang, Lie

Contributors dc:contributor

• Lei Tian

• Agriculture, Agronomy

Language dc:language

eng

Identifier

(MiAaPQ)AAI3044237

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/86047