Evaluation and Implementation of a Machine Vision System to Categorize Extraneous Matter In Cotton

The Cotton Trash Identification System developed at the Southwestern Cotton Ginning Research Laboratory was evaluated for identification and categorization of Extraneous Matter in cotton. The system’s categorization of trash objects in cotton images, were compared to Agricultural Marketing Service’s Extraneous Matter calls assigned by human classers.  AMS classer’s were tasked in assigning Extraneous Matter calls (Bark/Grass) in images acquired by various High Volume Instruments. Soft computing techniques were used to identify Extraneous Matter in the acquired cotton images and categorize them into bark/grass, stick, leaf, and pepper trash categories. Classer Extraneous Matter calls in the HVI images are compared with CTIS Bark/Grass and Stick categorization. Images were acquired from different HVI systems on both the Upper and Lower camera head, and human classer’s identified the Extraneous Matter by marking the objects on the acquired images. CTIS categorization of Extraneous Matter, were later compared to the Classer assigned Extraneous Matter calls. The main objective of this research is to use CTIS as an effective tool to aid classers in the identification of Extraneous Matter in cotton and, eventually, aid in the development of Extraneous Matter standards for cotton classification.