Location: Cotton Ginning Research
Title: Categorization of Extraneous Matter in Cotton Using Machine Vision Systems Authors
Submitted to: Bremen International Cotton Conference
Publication Type: Proceedings
Publication Acceptance Date: March 20, 2010
Publication Date: March 24, 2010
Citation: Whitelock, D.P., Siddaiah, M., Hughs, S.E., Knowlton, J.L. 2010. CATEGORIZATION OF EXTRANEOUS MATTER IN COTTON USING MACHINE VISION SYSTEMS. Proceedings of the International Cotton Conference Bremen. March 24-27,2010, Bremen Germany. p 177-185. Interpretive Summary: The price of US Cotton is based on Agricultural Marketing Service (AMS) classification. This classification is determined using High Volume Instrument machine measurements for color, length, fineness, etc. and human visual inspection for leaf grade and extraneous matter. A Cotton Trash Identification System (CTIS) developed at the USDA-ARS Southwestern Cotton Ginning Research Laboratory in Mesilla Park, New Mexico to aid the human classer in making extraneous matter calls was evaluated by comparing it to AMS classer extraneous matter calls for over 800 samples. There was good agreement by CTIS to the classer in the identifying extraneous matter, although CTIS tended to identify extraneous matter more often than the classer. Future work will focus on strengthening the agreement of CTIS to the human classer and developing a package that can be easily integrated into the current classing line as an aid to enhance the classer’s ability to make reliable and accurate cotton extraneous matter calls.
Technical Abstract: The Cotton Trash Identification System (CTIS) was developed at the Southwestern Cotton Ginning Research Laboratory to identify and categorize extraneous matter in cotton. The CTIS bark/grass categorization was evaluated with USDA-Agricultural Marketing Service (AMS) extraneous matter calls assigned by human classers for 209 cotton bale samples. AMS classers assigned extraneous matter calls on four cotton faces for each bale sample. Scanner acquired images of the same four faces at 400 DPI were analyzed to evaluate the CTIS performance. Soft computing techniques were used to identify trash objects in the acquired cotton images and categorize the objects into bark/grass, stick, leaf, and pepper trash categories. The primary goal of the study was to evaluate and calibrate CTIS categorization of extraneous matter using classer extraneous matter calls. CTIS agreed with the classer call 97% of the time when there was a classer extraneous matter call and 43% of the time when there was no classer call. CTIS may find a place as a tool to aid human classers in the classification of cotton by helping to identify extraneous matter.