All publications from your selection

Download RAW file: https://library.csbe-scgab.ca/docs/journal/40/40_4_293_raw.pdf
DOI:
Description: Algorithms were developed to classify dockage components from Canada Western Red Spring (CWRS) wheat and other cereal grains (e.g. durum wheat, barley, rye, and oats) based on morphological and colour features. The dockage classes used were: wheat heads, chaff, wild oats, canola, wild buckwheat, flax, and broken-wheat pieces. The wheat head dockage class was subdivided into single and multiple wheat heads, the subdivision improved the classification accuracy for wheat heads. The developed algorithms were tested on images taken with an area scan camera. Training and test data sets were established to evaluate the classification accuracies based on the extracted features. Morphology, colour, and morphology-colour models were evaluated for classifying the dockage components. Mean accuracies of 89.4% for the morphology model, 71.4% for the colour model, and 93.2% for morphology-colour model were obtained when tested on the independent test data sets using the holdout non-parametric classifier.

Keywords:
Résumé:
Mots-clés:
Citation: M. Nair and D.S. Jayas 1998. DOCKAGE IDE 'TiFICATION IN WHEAT USING MACHINE VISION. Canadian Agricultural Engineering 40(4):293-298.
Volume: 40
Issue: 4
Pages -
Contributor:
Date: 1998
Technical field:
Conference name:
Session name:

Other information:
Type: Text.Article
Format: PDF
Publication type:
Source:
Relation:
Coverage: Canada
Language 1: en
Language 2:
Rights: Canadian Society for Bioengineering
Notes:
Other files: