Location: Sugarbeet and Bean Research2016 Annual Report
1. Enable new commercial imaging and spectroscopic methods to determine fruit and vegetable internal quality and maturity. 2. Enable new, economical, accurate, automated, in-orchard methods for commercial apple quality tracing and grading.
1) Improvements will be made in the method and technique for measuring the optical absorption and scattering properties of horticultural and food products that may be considered homogeneous or layered in the tissue structure. Factors affecting the optical property measurements, including light source configuration, the geometry and surface roughness of samples, and inverse algorithm, will be evaluated by using numerical simulation (e.g., Monte Carlo and finite element) and experiment for phantom tissues and real samples, so as to improve the measurement accuracy and reproducibility. New methods and algorithms will be developed for accurate measurement of the optical properties of layered food products. Experiments will be carried out to measure the optical properties of horticultural products like apple, orange, and pickling vegetable. The measured optical properties will be used to predict quality and condition of the products. 2) Research will be conducted on the development of a new sensing technique for more effective quality evaluation of horticultural and food products. Specifically, different light illumination and image acquisition methods will be investigated for detecting properties and characteristics of plant tissues at different depths. Light penetration characteristics in plant tissues will be studied through computer simulations and experimental tests. Image processing algorithms will be developed for extraction of important features from the reflectance images to characterize internal quality (including defect) of fruit and vegetable. A new sensing system that incorporates conventional imaging or hyperspectral imaging technique with the optimal lighting configuration and dedicated image processing algorithms will be assembled and evaluated for real time detection of internal quality for fruit and vegetable. 3) Research will be conducted to develop cost effective, automated in-orchard apple sorting technology. New and improved functions will be developed and incorporated into the machine vision system to allow more effective sorting and grading of different varieties of apple for color, size and defect. More efficient and reliable sorter and bin filler designs in modular format will be proposed, assembled and tested in laboratory and field. A new method for handling individual fruit bins will be proposed and implemented so that no fruit bins would be left half-filled and the possible down time for the harvest crew resulting from the bin handling would be eliminated or minimized. The new and improved sorting system will be integrated with either a self-propelled or tractor-driven harvest aid platform for automatically sorting and grading apples into two or three quality grades as well as enhancing harvest efficiency and worker safety. Laboratory and field tests and demonstrations will be carried out, in close collaboration with commercial equipment manufacturer, growers, and extension personnel, to facilitate the development and transfer of the technology to the end user.
Research was carried out to improve the method and technique, based on the hyperspectral imaging–based spatially-resolved principle, for measuring the optical absorption and scattering properties of food and agricultural products, which can in turn be used for assessing food compositions and quality attributes. Computer simulations were performed to model light propagation in turbid food and determine the effect of different light source designs and boundary conditions on the measurement of light reflectance profiles from food samples. The most appropriate boundary condition was determined for mathematical simulation of the light propagation in food samples. Moreover, an appropriate lighting design was also determined for measuring the optical properties. Further studies were conducted on optimizing the mathematical procedures for estimating the optical properties of food and biological materials from the measured spatially-resolved diffuse reflectance data. Monte Carlo simulation, a stochastic simulation method, was used for modeling light propagation in a large number of turbid materials with different optical properties. Mathematical methods, including data normalization, selection of optimal reflectance data points, and the inverse algorithm for estimating the optical properties, were quantitatively examined, from which the optimal procedure of estimating the optical properties was proposed. The proposed new algorithm and procedure has resulted in better estimations of optical properties for food and biological samples, which will be implemented in the optical property measuring instrument developed by the ARS lab at East Lansing, Michigan. The research has resulted in two journal manuscripts and several conference proceeding papers or presentations. Good progress has been made in the development of a new imaging technique, called structured-illumination reflectance imaging (SIRI), for enhanced detection of food quality. In contrary to conventional uniform, diffuse illumination that is commonly used in machine vision systems for food quality inspection, SIRI relies on the application of special patterns of illumination to the sample for obtaining feature-enhanced images with better spatial resolutions and light penetration depth control to achieve enhanced capabilities for detecting quality of food. A laboratory SIRI system was constructed and used to detect fresh bruising in apples. Experiments were conducted on apples with bruises that were generated by artificial impact tests or occurred naturally during the operation of an apple infield sorting machine. New demodulation methods, a critical step in obtaining both direct component (equivalent to uniform illumination) and alternating component (giving enhanced image features for the sample at a more controlled light penetration depth) images, were proposed and compared for performance and speed. Results showed that instead of using three SIRI phase-shifted images that are commonly needed for image demodulation, the proposed new demodulation methods only need two phase-shifted images, while achieving comparable performance with a much shorter image acquisition time. Moreover, different patterns of illumination or combinations of multiple patterns of illumination were investigated, which allow for simultaneous interrogation of the internal tissues at different depths. SIRI was able to effectively detect fresh bruises on apples, which otherwise could not have been detected by conventional machine vision technique. Further studies were also done on detecting different types of surface defects on two varieties of apple. Preliminary results showed that SIRI could be used to detect surface defects when appropriate high spatial frequencies of illumination are selected. Research is ongoing on integrating spectral imaging technique with SIRI and improving its imaging acquisition speed, so that the technique can be useful for industrial applications. The research has resulted in six journal manuscripts, with three being accepted or published. Significant progress has been made for the past year on the development of a new self-propelled apple harvest and automatic infield sorting machine for commercial use. A new, improved automatic fruit sorting and grading system was designed and constructed. The improved fruit sorting systems is simpler in design and more cost effective, compared to the previous version. It is being integrated into the new self-propelled apple harvest platform, whose overall design was provided by ARS researchers at East Lansing, Michigan, with the construction being done by a commercial collaborator. Moreover, new, improved bin fillers were designed and constructed for providing more effective handling of apples in the bin. Laboratory tests were conducted on evaluating the bruising potential for the apple harvest and sorting machine and results showed that most bruising occurred when the apples were released from the bin filler into the bin that was still empty and that the machine would provide satisfactory performance in minimizing fruit bruising during handling. The new apple harvest and sorting machine is also able to automatically handle empty and full bins, without causing disruption to the harvest crew, which is important for improving harvest efficiency. Economic analysis was performed to look into machinery cost, occupational injury decrease, harvest efficiency increase and savings in postharvest storage and packing, from adopting the new machine. Results showed that U.S. fresh apple growers could achieve annual cost savings ranging from $3,000 to $55,000 per unit machine, depending on the percentage of processing apples or culls and production yield for a specific orchard. For processing apple growers, the economic benefits from adopting the machine could be even greater, because processing apples are sold at much lower prices compared to fresh apples. The new machine has been scheduled for testing and demonstration in a commercial orchard during 2016 harvest season. Three invention disclosures were filed for the new apple harvest and sorting machine.
1. Development of a new imaging technique for enhanced food quality detection. Food loss and waste due to inferior or defective fruit causes a huge economic loss to the U.S. fruit industry annually. Currently, machine vision is widely used for inspecting external defects of fruit, but it still cannot fully meet the industry’s demand in performance. Researchers at East Lansing, Michigan have developed a new imaging technique, called structured-illumination reflectance imaging (SIRI), for enhanced detection of fruit subsurface and surface defects. The SIRI system achieved high detection rates for fresh bruising in apples, a commonly encountered defect for apple and other fruits, which are much better than that by conventional machine vision technique under uniform illumination, and it is also promising for detecting various types of surface defects on apples. The new SIRI technique has great potential for improving quality inspection of fruit and other food products.
2. Development of apple harvest and automatic infield sorting technology for commercial use. Harvest and postharvest storage and packing are major operations in apple production and handling. Currently, commercial harvest aid machines are available for improving harvest efficiency and the working condition for workers, but they do not have the capability for automatic sorting and grading of fruit in the orchard. Researchers at the ARS East Lansing, Michigan location, in collaboration with a commercial horticultural equipment company, have developed a first self-propelled prototype machine with an automatic fruit sorting and grading system and a fully-integrated harvest aid function, for commercial use. The new machine has several novel, cost-effective design features in fruit sorting and grading, and fruit and bin handling in the orchard. Economic analysis showed that adoption of the machine can help U.S. fresh apple growers achieve significant cost savings, ranging between $3,000 and $55,000 annually per unit machine, and processing apple growers could achieve even more cost savings. Moreover, the machine also provides detailed information about the quality of harvested fruit, thus further enhancing product traceability and postharvest inventory management.
Zhu, Q., Guan, J., Huang, M., Lu, R., Mendoza, F. 2015. Evaluating bruise susceptibility of ’Golden Delicious’ apples using hyperspectral scattering technique. Postharvest Biology and Technology. 114:86-89.
Wang, A., Lu, R., Xie, L. 2015. Finite element modeling of light propagation in turbid media under illumination of a continuous-wave beam. Applied Optics. 55(1):95-103.
Lu, Y., Li, R., Lu, R. 2016. Structured-illumination reflectance imaging (SIRI) for enhanced detection of fresh bruises in apples. Postharvest Biology and Technology. 117:89-93.
Cen, H., Lu, R., Zhu, Q., Mendoza, F. 2015. Nondestructive detection of chilling injury in cucumber fruit using hyperspectral imaging with feature selection and supervised classification. Postharvest Biology and Technology. 111:325-361.
Pan, L., Lu, R., Zhu, Q., Tu, K., Cen, H. 2016. Predict compositions and mechanical properties of sugar beet using hyperspectral scattering. Food and Bioprocess Technology. 9(7):1177-1186.
Lu, Y., Li, R., Lu, R. 2016. Fast demodulation of pattern images by spiral phase transform in structured-illumination reflectance imaging for detection of bruises in apples. Computers and Electronics in Agriculture. 127:652-658.
Lu, R. 2016. Preface. In: Lu, R., editor. Light Scattering Technology for Food Property, Quality and Safety Assessment. Abingdon, United Kingdom: CRC Press, Taylor & Francis Group. p. xi-xiii.
Lu, R. 2016. Introduction to light and optical theories. In: Lu, R., editor. Light Scattering Technology for Food Property, Quality and Safety Assessment. Abingdon, United Kingdom: CRC Press, Taylor & Francis Group. p. 1-18.
Lu, R. 2016. Overview of light interaction with food and biological materials. In: Lu, R., editor. Light Scattering Technology for Food Property, Quality and Safety Assessment. Abingdon, United Kingdom: CRC Press, Taylor & Francis Group. p. 19-42.
Lu, R. 2016. Theory of light transfer in food and biological materials. In: Lu, R., editor. Light Scattering Technology for Food Property, Quality and Safety Assessment. Abingdon, United Kingdom: CRC Press, Taylor & Francis Group. p. 43-78.
Cen, H., Lu, R., Nguyen-Do-Trong, N., Saeys, W. 2016. Spatially-resolved spectroscopic technique for measuring optical properties of food. In: Lu, R., editor. Light Scattering Technology for Food Property, Quality and Safety Assessment. Abingdon, United Kingdom: CRC Press, Taylor & Francis Group. p. 159-186.
Mendoza, F., Lu, R. 2016. Dynamic light scattering for measuring microstructure and rheological properties of food. In: Lu, R., editor. Light Scattering Technology for Food Property, Quality and Safety Assessment. Abingdon, United Kingdom: CRC Press, Taylor & Francis Group. p. 331-360.
Lu, Y., Lu, R. 2016. Quality evaluation of apple by computer vision. 2nd Edition In: Sun, D. editor. Computer Vision Technology for Food Quality Evaluation. London, United Kingdom: Elsevier. p. 273-303.