2011 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.
Two miniature visible/near-infrared spectrometers were tested and evaluated for apple fruit maturity assessment. In addition, an inhouse developed bioyield firmness tester was also evaluated for apple firmness measurement at different compressive speeds. Preliminary analysis showed that bioyield firmness measurement can be done at a much higher compressive speed than at the one currently used for the tester.
An improved machine vision system that utilized a low-cost digital color camera was built and incorporated into the first version presorting prototype for sorting apples into two grades (i.e., fresh market and cull). Experiments were conducted for three varieties of apple to evaluate the performance of the machine vision system. In addition, different types of defective fruit, including scab, cuts, hail damage, insect bites, etc., were collected from two research orchards. Color images acquired for these fruit were used for developing an automatic fruit defect detection algorithm and building a fruit defects database. The machine vision system has fully met initial expectations in sorting apples for size and color. Based on the test evaluations and inputs from the apple growers, a new design for the presorting system has been proposed, and it will be ready for testing for the 2011 harvest season.
Optical absorption and scattering spectra for 500-1,000 nm were measured for ‘Golden Delicious’ and ‘Granny Smith’ apples of various firmness levels, using an inhouse developed instrument. Tissue specimens were then excised for microscopic image analysis using scanning electron microscope (SEM) and confocal laser scanning microscope (CLSM). The optical absorption and scattering properties were found to correlate with the area and diameter of fruit cells, and they were also related to the mechanical properties (i.e., elasticity) of apple tissues. Further research was conducted to measure the optical absorption and scattering properties of apples and peaches and determine their correlation with the fruit firmness and soluble solids content. Good to excellent correlations between the optical property measurements and fruit firmness and soluble solids content were obtained for both apple and peach.
Experiments were conducted during the 2010 harvest season and continued for three months after the harvest to evaluate the firmness and soluble solids content (SSC) of more than 3,400 ‘Delicious’, ‘Golden Delicious’, and ‘Jonagold’ apples, using four nondestructive instruments/sensors (i.e., bioyield firmness, sonic firmness, visible/near-infrared spectroscopy, and spectral scattering). Different spectral/image analysis methods and sensors combinations were evaluated and compared for firmness and SSC prediction. Significantly better predictions of fruit firmness and SSC were obtained using the sensor and data fusion approach, with the improvements ranging between 8% and 26%. The integration of spectral scattering and near-infrared techniques showed particular promising results for accurate measurement of fruit firmness and SSC.
Sensor data fusion for improving apple quality assessment. Nondestructive, rapid, and accurate measurement of firmness and soluble solids content - two important quality attributes of apples - is challenging because they are influenced by both physiological and environmental factors. Research was conducted to evaluate and integrate several nondestructive sensing technologies for improving fruit firmness and soluble solids content prediction. They were two inhouse developed sensors (i.e., bioyield firmness tester and online spectral scattering system), a commercial sonic firmness sensor and a visible/near-infrared sensor. Mathematical methods were developed to extract and integrate the information acquired by the four sensors for more than 6,400 apples of three varieties that were harvested in 2009 and 2010. The integration of these sensing methods significantly improved firmness and soluble solids content prediction accuracies; the improvements ranged between 8% and 26%, compared with individual sensing techniques. Optimal sensor data fusion methods were developed, which will provide a more accurate and robust approach for development of online sorting and grading technology for apple firmness and soluble solids content.
Optical and structural characterization of apple fruit. Currently light-based sensing techniques, such as imaging and spectroscopy, are being widely used for assessing quality and properties of horticultural and food products. There is, however, a considerable knowledge gap in understanding light scattering and absorption in the plant tissue, two basic phenomena when light interacts with biological materials. Research was carried out to measure the absorption and scattering properties of apple tissues for 500-1,000 nm, a spectral region that is useful for assessing fruit quality. Microscopic image analyses and mechanical compressive tests were performed to quantify the micro-structural characteristics (i.e., cell size, shape, area, void space, etc.) and mechanical properties (i.e., elasticity and strength) of tissue specimens excised from apples for different storage times. Both absorption and scattering parameters were positively correlated with the size and area of apple tissue cells. There was a strong correlation between the optical absorption and scattering parameters and the elasticity of apple tissues. This research has provided new knowledge of the relationship between the optical, mechanical and micro-structural properties of apple tissue, and it has demonstrated that the optical absorption and scattering parameters can be used to assess the structural characteristics and quality-related properties (i.e., firmness and soluble solids content) of apple fruit.
Machine vision system for automatic infield sorting of apples. Technology for infield sorting and grading of apples is needed to help growers achieve savings in postharvest storage and packing, which are a major cost component in apple production. A low-cost machine/computer vision system using a digital color camera was developed by researchers at the ARS East Lansing, MI location, to sort apples for size, shape and surface color at a speed of 4-6 fruit per second. The machine vision system was able to accurately measure the size of apples, exceeding the USDA apple grading standards, and it achieved better results in sorting undersized apples than a commercial mechanical sizing machine. The system also achieved superior color sorting results, which are comparable to that by a commercial machine vision-based sorting system for packinghouse use. The machine vision system is now being integrated into a mobile infield sorting system for sorting, grading and tracking apples, which will enable U.S. apple growers to reduce overall production cost and better manage harvested apples during postharvest storage and handling.
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