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ARS Home » Midwest Area » East Lansing, Michigan » Sugarbeet and Bean Research » Research » Publications at this Location » Publication #267772
Title: Multi-sensor Data Fusion for Improved Prediction of Apple Fruit Firmness and Soluble Solids Content

Author
item Mendoza, Fernando
item Lu, Renfu
item CEN, HAIYAN - Michigan State University

Submitted to: Proceedings of SPIE
Publication Type: Proceedings
Publication Acceptance Date: 4/30/2011
Publication Date: 4/25/2011
Citation: Mendoza, F., Lu, R., Cen, H. 2011. Multi-sensor Data Fusion for Improved Prediction of Apple Fruit Firmness and Soluble Solids Content. Proceedings of SPIE. April 25-29, 2011, Orlando, Florida. Paper No. 8027-0L, 14 p.

Interpretive Summary: Firmness and soluble solids content (SSC) are important quality parameters for apples. Different sensors have been developed in recent years for nondestructive assessment of firmness and SSC of apples. Since each sensor is based on a different principle, combination of them could provide more complete and complementary information for developing accurate and robust prediction models. This research, for the first time, evaluated and compared four sensing techniques (i.e., acoustic firmness sensing, bioyield firmness test, near-infrared spectroscopy, and online spectral scattering), coupled with the data fusion approach, for prediction of apple firmness and SSC. A total of 6,535 apples for 'Jonagold', 'Golden Delicious' , and 'Delicious' harvested in 2009 and 2010 were used for analysis. While each sensor showed different abilities to predict firmness and SSC in apples, the fused or integrated systems were more powerful than the individual sensors for quality prediction of apples. Significant improvements and more consistent predictions for flesh firmness were found for the two years’ data, whereas significant improvements in SSC prediction were only obtained for 2009. This research demonstrated that integration of different sensors can improve the prediction of apple firmness and SSC. The sensor fusion approach is useful for the development of a more accurate and robust system for online sorting and grading of apples.

Technical Abstract: Several nondestructive technologies have been developed for assessing the firmness and soluble solids content (SSC) of apples. Each of these technologies has its merits and limitations in predicting these quality parameters. With the concept of multi-sensor data fusion, different sensors would work synergistically and complementarily to improve the quality prediction of apples. In this research, four sensing systems (i.e., an acoustic sensor, a bioyield firmness tester, a miniature near-infrared (NIR) spectrometer, and an online hyperspectral scattering system) were tested and combined for nondestructive prediction of firmness and SSC of 'Jonagold' (JG), 'Golden Delicious' (GD), and ‘Delicious' (RD) apples. A total of 6,535 apples harvested in 2009 and 2010 were used for analysis. Each of the four sensors showed a different degree of ability to predict apple quality. Better predictions of the firmness and, in most cases, of the SSC were obtained using sensors fusion than using individual sensors, as measured by number of latent variables, correlation coefficient, and standard error of prediction (SEP). Results obtained from the two harvest seasons with the multi-sensor fusion approach were quite consistent, confirming the validity and robustness of the proposed approach. The SEPs for firmness measurement of JG, GD and RD using the best combination of two-sensor data were reduced by 13.3, 19.7 and 7.9% for the 2009 data and 16.0, 12.6 and 4.7% for the 2010 data; and using all four-sensor data by 21.8, 25.6 and 13.6% in 2009, and 14.9, 21.9, and 7.9% in 2010, respectively. For SSC prediction, using the two-sensor data (i.e., NIR and scattering) improved predictions for JG, GD and RD apples harvested in 2009, with their SEP values being reduced by 10.4, 6.6 and 6.8%, respectively. This research demonstrated that the fused systems provided more complete and complementary information and, thus, were more powerful than the individual sensors in prediction of apple quality.