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ARS Home » Midwest Area » East Lansing, Michigan » Sugarbeet and Bean Research » Research » Publications at this Location » Publication #186490


item Lu, Renfu

Submitted to: Proceedings of SPIE
Publication Type: Proceedings
Publication Acceptance Date: 10/19/2005
Publication Date: 12/15/2005
Citation: Lu, R., Peng, Y. 2005. A laser-based multispectral imaging system for real-time detection of apple fruit firmness. Proceedings of SPIE. 5996:59960F.

Interpretive Summary: Firmness is a key parameter in determining the maturity and quality grade of apples and directly influences consumer acceptance and satisfaction with the fruit. Currently, the destructive firmness sampling method is routinely used to gauge the average firmness for a lot of apples. Due to the biological variability, the same lot of apples can vary greatly in firmness. Many nondestructive mechanical methods have been developed, but they do not correlate well or consistently with the standard destructive measurement, which is especially evident for firm fruits such as apples. Recently, our laboratory developed a novel, unconventional sensing technique that uses spectral scattering at multiple wavelengths for estimating fruit firmness, and it achieved superior results compared to other nondestructive methods. The objective of this research was to develop a laboratory prototype for rapid, real time detection of apple fruit firmness. The laboratory prototype, which mainly consisted of a custom design multi-laser unit, a multispectral imaging unit and a min-packing line, allowed us to acquire multiple spectral images from apple fruit at selected wavelengths simultaneously and rapidly. Newly developed mathematical methods were incorporated into the computer program for capturing and processing spectral scattering images for real time prediction of apple fruit firmness. The prototype was able to run at two fruit/s, and it was tested on ‘Golden Delicious’ and ‘Red Delicious’ apples. Good firmness predictions were obtained with the correlation coefficient equal to or greater than 0.85 for both cultivars. The laser-based multispectral imaging system has potential to meet the speed requirement for sorting and grading apple fruit. The system can be incorporated into existing packinghouses for sorting and grading apple fruit. The technology is also promising for other fruits and horticultural products. Successful development and transfer of the technology would enable the industry to deliver better quality fruit and thus improve profitability.

Technical Abstract: Recent research showed that spectral scattering is useful for assessing the firmness of apple fruit. This paper reports the development of a laser-based multispectral imaging prototype for real-time detection of apple fruit firmness. The prototype consisted of a common aperture multispectral imaging unit, a multi-laser unit, and a belt conveyor, which was able to capture and process spectral scattering images for up to two fruit/s. The multispectral imaging system was tested for detecting the firmness of ‘Golden Delicious’ and ‘Red Delicious’ apples when they were moving on the conveyor belt at an imaging speed of one fruit for every two seconds. The original scattering images were corrected by using the newly developed methods of removing noise pixels and incorporating fruit size into the calculation of the scattering distance and intensity. The corrected scattering images were reduced to one-dimensional scattering profiles by radial averaging. The scattering profiles were fitted with a Lorentzian distribution function of four parameters. Multi-linear regression models were developed using the four Lorentzian parameters for the four wavelengths for each apple cultivar, and the models were then used to predict the firmness of validation apples. The multispectral imaging system achieved good firmness predictions with values for the correlation coefficient of 0.85 for ‘Golden Delicious’ and 0.86 for ‘Red Delicious.’ The laser-based multispectral imaging system is fast and relatively easy to implement, and it has the potential to meet the requirement for online sorting and grading of apple fruit.