Location: Sugarbeet and Bean Research
2013 Annual Report
Second, commercially viable infield mobile sorting technology will be developed for sorting and grading harvested apples into two or three quality grades (fresh market, processing, and cull). A cost effective machine vision system and a fruit bin filler will be designed and built; they will then be integrated into existing infield apple handling systems for effective segregation of unmarketable or defective fruit from fresh market grade fruit. Laboratory and infield tests will be performed to evaluate the mobile infield sorting prototype for performance and bruising to apples. We will collaborate with a horticultural equipment manufacturer and university extension specialists, so that the developed technology can be quickly adopted by growers to achieve production cost savings.
Third, research will be conducted on the development of a hyperspectral imaging-based spatially-resolved method for measuring the optical absorption and scattering properties of horticultural and food products. An optical property measuring prototype will be built and tested for automatic measurement of the optical properties of fruits and other food and agricultural products. Optimization of the hardware (light source, source-detector distance, etc.) and algorithms will be performed through Monte Carlo simulation and experiment to improve measurement accuracy and repeatability. Experiments and mathematical or statistical analyses will be performed to relate the optical properties to the structural/mechanical properties of apples and to the quality attributes of apples, peaches and tomatoes. Moreover, research will be conducted to improve spectral scattering technology for sorting and grading apples for firmness and soluble solids content. Improved hardware designs and new spectral scattering analysis methods integrating both spectral and image features will be considered and incorporated into the laboratory spectral scattering prototype for classification of apples into different quality grades based on firmness/soluble solids content. Finally, an online hyperspectral imaging system, which integrates reflectance in the visible spectral region and transmittance in the short-wave near-infrared region, will be developed for online sorting and grading of pickling cucumbers and/or pickled products for external and internal quality (or defect). Different lighting designs and spectral imaging acquisition modalities will be considered and evaluated. Image processing and analysis algorithms will be developed for rapid detection and segregation of defective pickling cucumbers/pickles.
Progress was made on the development of a mobile system for harvesting and sorting apples in the orchard. Harvest aid functions that are suitable for six to eight workers were incorporated into the mobile system, which enhance harvest efficiency and improve worker safety. Improvements to the bin filler designs were made for better delivery and distribution of harvested fruit into the bins. New functions and algorithms were developed and incorporated into the apple sorting/grading software program to provide more user-friendly interfacing in selecting grading criteria and quality grades.
Research was carried out to improve the hyperspectral imaging system, operated in simultaneous reflectance and transmittance modes, for online inspection of both external and internal quality of pickling cucumbers. Two optimal wavelengths or wavebands were determined using two different wavelengths selection methods (i.e., minimum redundancy-maximum relevance and principal component analysis). Superior results in segregating defective cucumbers from normal ones were obtained, with an accuracy of greater than 94%. The identified wavebands can be implemented for fast online inspection of internal defect of pickling cucumbers.
Firmness and soluble solids content (SSC) are important in assessing the quality and shelf life of blueberries. Hyperspectral reflectance and transmittance images were acquired from blueberries over the wavelengths of 500-1,000 nm. Statistical models were developed for prediction of firmness and SSC. Better predictions of SSC and firmness were obtained using reflectance mode than transmittance mode. Fruit orientation only had small effect on reflectance measurement and insignificant effect on transmittance measurement.
Huang, M., Wang, B., Zhu, Q., Lu, R. 2012. Analysis of hyperspectral scattering images using locally linear embedding algorithm for apple mealiness classification. Computers and Electronics in Agriculture. 89:175-181.
Mendoza, F., Lu, R., Haiyan, C. 2012. Comparison and fusion of four nondestructive sensors for predicting apple fruit firmness and soluble solids content. Postharvest Biology and Technology. 73:89-98.
Lu, R., Ariana, D.P. 2013. Detection of fruit fly infestation in pickling cucumbers using a hyperspectral reflectance/transmittance imaging system. Postharvest Biology and Technology. 81(1):44-50.
Leiva-Valenzuela, G., Lu, R., Aguilera, J. 2013. Prediction of firmness and soluble solids content of blueberries using hyperspectral reflectance imaging. Journal of Food Engineering. 115(1):91-98.
Mizushima, A., Lu, R. 2013. An image segmentation method for apple sorting and grading using support vector machine and Otsu's method. Computers and Electronics in Agriculture. 94(1):29-37.
Mizushima, A., Lu, R. 2013. A low-cost color vision system for automatic estimation of apple fruit orientation and maximum equatorial diameter. Transactions of the ASABE. 56(3):813-827.
Cen, H., Lu, R., Mendoza, F., Beaudry, R. 2013. Relationship of the optical absorption and scattering properties with mechanical and structural properties of apple tissue. Postharvest Biology and Technology. 83:33-38.
Qin, J., Chao, K., Kim, M.S., Lu, R., Burks, T. 2013. Hyperspectral and multispectral imaging for evaluating food safety and quality. Computers and Electronics in Agriculture. 118:157-171.