Submitted to: Proceedings of SPIE
Publication Type: Proceedings
Publication Acceptance Date: 4/20/2010
Publication Date: 4/20/2010
Citation: Yoon, S.C., Park, B., Lawrence, K.C., Windham, W.R., Heitschmidt, G.W. 2010. Development of real-time line-scan hyperspectral imaging system for online agricultural and food product inspection. Proceedings of SPIE. 7676(1). Interpretive Summary: Carcasses contaminated in poultry processing plants can harbor pathogens that can make people ill. This research is about a new development of a hyperspectral imaging system to scan the surface of poultry carcasses for fecal contaminants during poultry processing. The USDA researchers designed and developed a prototype online imaging system so that at its full capacity the system can detect tiny amounts of fecal matter (down to 5-mg spots) at the maximum line speeds (up to 200 birds per minute). The imaging system consisted of a pushbroom line-scan hyperspectral imaging camera, lighting, and application software. The most unique feature of the system was that a pushbroom line-scan hyperspectral imaging camera was refined for real-time detection of small contaminants like feces moving in high speed while monitoring whole carcass bodies. Another unique feature was that application software was written in-house for real-time camera operation and triggering/detection algorithm execution. An image-based triggering algorithm was developed to determine start and end positions of birds. The imaging system was built upon a portable platform so that it could be easily installed in commercial poultry processing plants. A speed test performed in a pilot-scale processing plant showed that the system could handle 180 birds per minute. Four types of fecal materials (duodenum, ceca, colon and ingesta) were used for the study. The performance of fecal detection was comparable to what the previous research achieved. The imaging platform showed its potential use in commercial poultry processing lines.
Technical Abstract: This paper reports a recent development of a line-scan hyperspectral imaging system for real-time multispectral imaging applications in agricultural and food industries. The hyperspectral imaging system consisted of a spectrograph, an EMCCD camera, and application software. The real-time multispectral imaging with the developed system was possible due to (1) data binning, especially a unique feature of the EMCCD sensor allowing the access to non-contiguous multispectral bands, (2) an image processing algorithm designed for real-time multispectral imaging, and (3) the design and implementation of the real-time application software. The imaging system was developed as a poultry inspection instrument determining the presence of surface feces on poultry carcasses moving at commercial poultry processing line speeds up to 180 birds per minute. The imaging system can be easily modifiable to solve other real-time inspection/sorting problems. Three wavelengths at 517 nm, 565 nm and 802 nm were selected for real-time fecal detection imaging. The fecal detection algorithm was based on dual band ratios of 565nm/517nm and 802nm/517nm followed by thresholding. The software architecture was based on a ping pong memory and a circular buffer for the multitasking of image grabbing and processing. The software was written in Microsoft Visual C++. An image-based internal triggering (i.e. polling) algorithm was developed to determine the start and end positions of birds. Twelve chickens were used for testing the imaging system at two different speeds (140 birds per minute and 180 birds per minute) in a pilot-scale processing line. Four types of fecal materials (duodenum, ceca, colon and ingesta) were used for the evaluation of the detection algorithm. The software grabbed and processed multispectral images of the dimension 118 (line scans) x 512 (height) x 3 (bands) pixels obtained from chicken carcasses moving at the speed up to 180 birds per minute (a frame rate 286 Hz). Intensity calibration, detection algorithm, displaying and saving were performed within the real-time deadlines.