Location: Sugarbeet and Bean Research2012 Annual Report
1a. Objectives (from AD-416):
This research will develop practical sensors and technologies for quality measurement and grading of fruits and vegetables before, at and after harvest. It also aims to generate new knowledge and understanding of the optical and mechanical properties of fruits and vegetables and their relationship with the physiological factors and quality attributes. A systems approach of integrating sensors development, properties characterization, and models/algorithms development will be applied to attain the following specific objectives: Objective 1: Develop cost effective sensors and sensing systems to measure and monitor the quality/maturity of individual apples in the orchard. Objective 2: Develop commercially viable technology to presort and grade apples in the orchard so as to decrease postharvest handling and storage costs for fruit growers. Objective 3: Develop technology to accurately and rapidly assess, sort, and grade harvested tree fruits and vegetables for multiple internal quality attributes (firmness, flavor, ripeness) and defects.
1b. Approach (from AD-416):
First, latest miniature near-infrared (NIR) technology will be used in the development of a portable fruit maturity sensor. Fruit maturity measurement will be achieved through integration of NIR technology with the nondestructive firmness measurement method recently developed in our lab. Algorithms will be developed and integrated into the sensor for real-time measurement of fruit firmness, soluble solids content and other maturity parameters. Laboratory and field tests will be performed to assess the sensor’s performance and portability. 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.
3. Progress Report:
Experiments were continued for assessing the maturity and quality of apples during the 2011 harvest season and after harvest. Four sensors, including an in-house built bioyield firmness tester, an acoustic firmness sensor, a visible/near-infrared sensor, and an in-house built online spectral scattering system, were tested and evaluated for predicting the firmness and/or soluble solids content of three varieties of apple. Classification models were developed using the spectral scattering and visible/near-infrared spectroscopic data to sort the apples into two or three quality grades, and superior results for classification of apples into two classes of firmness with an accuracy of 98% for one variety were obtained. Further studies were also carried out to determine the optimal approach for upgrading the apple quality prediction/classification models by considering growing season, number of samples selected from prior harvest years, and optimal combinations of wavelengths. 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.