Location: Sugarbeet and Bean Research
2012 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.
A prototype mobile system for infield sorting of apples was built, which has some unique features in fruit singulation and rotation for imaging, the delivery of fruit into bins, and the bin filler design. The prototype sorts apples into two or three quality grades (i.e., cull, processing and fresh market) at a speed of six fruit per second. Effort was made in developing computer algorithms for detecting defective fruit, including scab, cuts, hail damage, insect bites, etc. Color images were collected from the defective apples harvested from two Michigan State University research orchards in 2011. Initial evaluation of the defects detection algorithm showed promising results and further research is being carried out to improve the algorithm for detecting those defects or blemishes that are more difficult to identify. In addition, a new color image segmentation method, an important step in the image processing, was developed to improve the accuracy of segmenting dark colored fruit from the background.
Research was carried out to develop algorithms for automatic detection and segregation of normal and mechanically injured pickling cucumbers from the hyperspectral reflectance/transmittance images, which were acquired using a laboratory online system under an improved lighting configuration. Initial results showed that with the new lighting design, the optimal wavelengths identified from the hyperspectral image data achieved superior results (95% or higher in accuracy) for differentiating defective pickling cucumbers from normal ones.
Research was also conducted on the feasibility of hyperspectral imaging technique for assessing the firmness and soluble solids content of blueberries and for detecting mechanical bruising and shriveling in blueberries. Algorithms were developed for predicting the firmness and soluble solids content of blueberries using the hyperspectral images. Results showed that hyperspectral imaging can provide an effective means to differentiate between soft and firm blueberries, and it could be implemented for online sorting and grading of blueberries based on firmness and, possibly, soluble solids content.
Mendoza, F., Lu, R., Ariana, D., Cen, H., Bailey, B.B. 2011. Integrated spectral and image analysis of hyperspectral scattering data for prediction of apple fruit firmness and soluble solids content. Postharvest Biology and Technology. 62(2):149-160.
Cen, H., Lu, R., Mendoza, F., Ariana, D.P. 2012. Assessing multiple quality attributes of peaches using spectral absorption and scattering properties. Transactions of the ASABE. 55(2):647-657.