Image-Processing Solution to Cotton Color Measurement Problems: Part I. Instrument Design and Construction

Abstract

Trash particles enmeshed in bulk cotton fiber samples interfere with conventional cotton color measurement accuracy. A new instrument was designed and constructed for reducing trash effects on cotton color measurement, while maintaining the traditional system of cotton color measurement. The instrument's illumination system included four quartz−tungsten−halogen lamps in aluminum elliptical reflectors. The instrument's sensor was a panchromatic video camera that acquired images through optical color filters on a rotating wheel. The sensitivities of the various measurements were rigorously considered to maximize the dynamic range over which each measurement was made. The system's camera was connected to a computer through a frame grabber. Software was written to control the filter wheel, image acquisition, color/trash computations, and data recording. Image processing was employed to differentiate trash particles from cotton in the images. Color was calculated from the image portion judged by image analysis to be cotton. The system was tested on a large number of cotton samples and performed as designed and without any software or hardware failures.

An image processing and machine learning solution to automate Egyptian cotton lint grading

“…The design is based on existing cotton color vision systems. [12][13][14][22][23][24][25][26] Images were captured using a CCD sensor 8.1 MP Fuji A850 digital camera (FUJIFILM Corporation, Minato-ku, Tokyo, Japan), which stored the images in JPEG format with dimensions of 2248 pixel  3264 pixel. The CCD sensor was mounted 11 cm vertically above the surface of the samples and the inbuilt autofocus capability of the CCD sensor ensured all images were in focus.…”

“…Previous studies have evaluated alternative cotton lint measuring systems to replace or supplement manual inspection and HVI measurement systems, which include the following: the colorimeter; 18,19 computer scanner; 20,21 charge-coupled device (CCD) camera; 12–14,22–26 single-lens reflex camera; 27 thermal camera; 28 infrared spectrometry; 29,30 microscope; 31 X-ray scanner; 32 and optical spectrometry. 16,20 A CCD is a digital camera widely used in digital photography and astronomy.…”

AI-Assisted Cotton Grading: Active and Semi-Supervised Learning to Reduce the Image-Labelling Burden

“…Optical methods are emerging as predominate alternative systems for measuring cotton lint, either as replacements for or supplements to manual inspection and HVI measurement systems. Optical methods include the colorimeter [18,19], computer scanners [20,21], charge-coupled device (CCD) cameras [11][12][13][22][23][24][25][26], single-lens reflex cameras [27], thermal cameras [28], infrared spectrometry [29,30], microscopes [31], and optical spectrometry [15,20]. Cotton data extracted from image-measuring systems have been utilised to develop machine learning classification models to successfully classify US upland cotton [18,26], Chinese upland cotton [11], and Egyptian cotton [32] into different grades.…”