Objective measurement of mushroom quality relative to industry inspectors Online publication date: Mon, 11-Dec-2006
by N.J. Kusabs, L. Trigg, A.F. Bollen, G. Holmes
International Journal of Postharvest Technology and Innovation (IJPTI), Vol. 1, No. 2, 2006
Abstract: A machine learning classification system has been developed to sort mushrooms into similar quality grades to those used by human inspectors. The attributes considered for the algorithm included weight, firmness, image features and some subjective scales of the common cultivated button mushroom. Two grading systems were tested; one involving three broad quality grades and a more detailed five-grade system. Two machine learning methods were used to build quality prediction models, relative to the mushroom quality grade criteria specified by the inspectors. A head inspector was used as a reference grading expert and the mis-classification error of the models based on his/her grading for both three and five grades varied from 17 to 22%. The accuracy of the quality prediction models is at the upper level of the variation measured between mushroom industry inspectors. This machine learning classification system provided insights into the subjective decision-making regarding mushroom quality.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Postharvest Technology and Innovation (IJPTI):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook