Classification of cured tobacco leaves by colour and plant position by means of computer processing of digital images

This dissertation investigates the machine vision grading of flue-cured Virginia tobacco by means of digital processing of tobacco leaf images. With reference to international grading standards and to modem image processing techniques, two classifiers are designed. The colour classifier uses seven features extracted from each leaf image to grade the leaf into one of five official colour classes. It does this with an expected correct classification rate of 93.5%. The plant position classifier identifies the position on the stalk from which a leaf was reaped, using ten size and shape features to classify the leaf into one of six plant position categories. It has a correct classification rate of 70%. Average colours for each colour class and archetypal shapes for each plant position category are derived from the digital leaf data. These should be of value to tobacco graders as objective representations of typical leaves within each class.

Grantor dc:publisher.institution

Department of Electrical Engineering

Year dc:date.issued

1999

Author dc:creator

• Tattersfield, George Metcalf

Advisor dc:contributor.advisor

• De Jager, Gerhard

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/11427/21167

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/21167