Classifying cotton bark and grass extraneous matter using image analysis

Abstract

Cotton extraneous matter (EM) and special conditions are the only cotton quality attributes still determined manually by US Department of Agriculture Agricultural Marketing Service (USDA-AMS) classers. To develop a machine EM classing system, a better understanding of what triggers a classer EM call is needed. The goal of this work was to develop new information about cotton EM, such as bark and grass, and leaf particles, using machine measurements, to aid in the development of instrumentation for cotton quality measurements. AMS classers were tasked in identifying and denoting bark/grass in large-area color images of cotton samples. Image segmentation analysis was applied to detect non-cotton items, such as leaf particles, and the classer denoted bark/grass objects were segmented manually. Further image analysis was used to measure shape and color parameters of these bark/grass objects and leaf particles in the sample images. These measurements of the bark/grass objects and leaf particles were compared and logistical regression analyses conducted to evaluate classification. For every shape and color parameter, there were significant differences between the bark/grass objects and the detected leaf particles in the images. The differences were greater for the shape parameters than for the color parameters. A classification model with shape, color, and log-transformed shape parameters consistently classified the bark/grass objects and leaf particles most accurately with 99.5% and 97.6% correct classification rate, respectively. However, classification models that were 99% correct classifying manually segmented bark/grass were only about 77% correct when applied to the machine detected bark/grass particles.