Progress 05/19/15 to 05/18/20
Outputs
Progress Report Objectives (from AD-416): 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. Approach (from AD-416): 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. Objective 1a: Measurement of optical absorption and scattering properties can provide a new means for assessing postharvest quality of horticultural products. The hyperspectral imaging-based spatially resolved technique developed by our laboratory in previous research enables nondestructive, fast measurement of the optical absorption and scattering properties of horticultural and food products. Research was conducted on improving the technique for more accurate measurement of optical properties of food products with layered structures (i.e., skin and flesh). Computer simulations and experiments were conducted to determine the optimal system configurations and inverse algorithms for optical property estimations. New, improved algorithms were developed, which performed significantly better compared to the conventional method for measuring horticultural and food products of homogenous and layered structures. A new spatially resolved spectroscopy (SRS) system using a multi-channel hyperspectral imaging sensor as a platform, was developed for simultaneous acquisition of 30 spatially resolved spectra over the 550-1, 650 nm for food samples. Mathematical methods were developed for estimating the optical absorption and scattering properties from the acquired spectra and for prediction of postharvest quality of tomatoes and other food products. The technique enables better quality assessment of horticultural and food products. Spatial-frequency domain imaging (SFDI) is an emerging technique for measurement and spacial mapping of optical properties of biological tissues. Great challenges, however, still exist in accurate measurement of optical properties of food products using the SFDI technique. New, improved approaches to the SFDI data analysis were proposed for estimation of optical properties for both homogenous and layered food products. Computer simulations, followed with experimental validations, were conducted to determine the optimal mathematical procedures and system parameters for optical property estimations. The proposed new methods and algorithms significantly improved the accuracy of the SFDI technique for measurement of the optical absorption and scattering of food products. Objective 1b: Machine vision technology is widely used for defects inspection of horticultural products, but its performance is still short of meeting industry expectations. Research was conducted on the development of a new imaging modality with substantially improved capabilities for detecting surface and subsurface defects of horticultural products. Two new structured-illumination reflectance imaging (SIRI) systems were built for acquiring images from food samples under illumination of sinusoidal patterns, compared to uniform illumination commonly used for conventional imaging systems. The first system allows acquiring broadband images in the visible spectral region, while the second system enables acquiring spectral images over the spectral region of 600-1,000 nm. Studies showed that SIRI can reveal some hidden features of horticultural products, which are difficult to ascertain by conventional imaging techniques, and it exhibited superior performance in detecting such defects as subsurface bruising in apples. Two new demodulation methods were proposed, which is a critical procedure in implementing the SIRI technique, and they only require two patterned images instead of three by the conventional demodulation method. The methods enable faster acquisition of patterned images and would facilitate the implementation of the SIRI technique for online applications. In addition, a new image enhancement technique, called bi- dimensional empirical mode decomposition (BEMD), was proposed to remove artifacts in the demodulated images for improving subsequent image processing and defect detection. Light attenuation or penetration in biological tissues is related to the spatial frequency of illumination. By utilizing this important feature, our study demonstrated that it is feasible to acquire patterned images from food samples subjected to composite patterns of illumination with different spatial frequencies. This approach can be useful for more effective, simultaneous detection of different types of surface and/or subsurface defects. Studies were conducted, using the SIRI systems, coupled with the new image processing methods, on detection of surface and subsurface defects (i.e., bruise, defects due to mechanical and/or physiological disorder, early disease infection, etc.) for apples, peaches, pickling cucumbers, and tomatoes. In all the studies, SIRI exhibited superior results over conventional imaging technique under uniform illumination. Finally, a general-purpose, graphical user interfacing program, called siriTool, was developed for automatic image acquisition, demodulation and enhancement, object segmentation, image features extraction and selection, and classification. The program incorporates the two image demodulation algorithms, a new image segmentation technique, and the BEMD. Furthermore, it also enables using different methods for object classifications. An experimental study was carried out on the detection of yellowish subsurface spot defects in pickling cucumbers, which are difficult to ascertain by conventional imaging technique. siriTool was able to achieve 98% or higher classification accuracies, which were significantly better than that by using conventional imaging technique. Objective 2: Harvest and postharvest handling (including storage, sorting, grading and packaging) are labor intensive operations, which account for about half of the total production cost for U.S. apple growers. A study was conducted of the economic benefits from adopting harvest assisting and infield sorting technologies. Results showed that significant savings in postharvest handling can be achieved if high quality apples for the fresh market can be segregated from inferior fruit that are only suitable for making juice or processed products at the time of harvesting in the orchard. Furthermore, greater economic benefits can be accrued by integration of infield sorting technology with a harvest assisting machinery system. While system cost is of great concern, there are several major technological challenges for automatic infield sorting, which include, but are not limited to, the development of a new machine vision inspection system that is compact, efficient and robust for orchard use, automatic bin filling technology for handling graded fruit in bins, and an automatic bin handling system. After several iterations in prototyping and testing, a cost-effective and compact machine vision- based system was constructed, and it consists of a digital color camera enabling acquisition of 30 images per second for inspecting fruit size and color or shape, an innovative, compact conveying module for fruit singulation, rotation and transporting, and a novel fruit sorting mechanism. Two versions of fruit sorting mechanisms were developed. The first version allows fruit to be sorted into two or three quality grades (i.e., fresh market, processing and cull or juice) at a rate up to 6 fruit per second, while the second, improved version, which is more compact and robust, sorts apples into two quality grades (i.e., fresh market and cull or processing) at a rate up to 12 fruit per second. In collaboration with a commercial horticultural equipment manufacturer, ARS researchers at East Lansing, Michigan, designed and constructed a self-propelled apple harvest and infield sorting machine prototype. In addition to the innovative machine vision sorting module, this machine also has several other major innovations, which include automatic bin fillers for handling harvested fruit in bins, a computer controlled bin handling system, and adjustable harvest platforms with a special fruit receiving design for improving harvest efficiency and worker ergonomics. The bin filler is mainly composed of two pairs of foam rollers that allow sorted apples to drop freely for a maximum height of 1.5 m, a rotary fruit distributor, and a sensor and an onboard microprocessor for automatic monitoring and control of the fruit filling process. The computer-controlled hydraulic system minimizes the down times for fruit pickers caused by the handling of empty and full bins, thus enhancing overall harvest efficiency. Laboratory and field tests in a commercial orchard for three harvest seasons showed that the machine vision-based sorting system has fully met the performance expectations in terms of sorting accuracy and speed, and the bin fillers handled graded fruit in bins gently and evenly with minimum bruising damage to the harvested fruit. In further support for Objective 2, a study was also conducted to evaluate three commercial harvest assisting methods (i.e., the traditional ladder and bucket method, a commercial vacuum-based harvester, and a commercial harvest platform system with use of picking buckets), so as to identify key technological gaps for these systems that need be addressed in future innovation and improvement. A time and motion method was used for analyzing the entire harvest process by fruit pickers under the three methods in commercial orchards in Michigan. The study showed that these harvest methods are still low in harvest efficiency due to lack of automation and that technological innovations should be focused on reducing non-picking activities for the traditional harvest method and development of new fruit receiving and bin filling technologies for the harvest-assisting platform systems. Accomplishments 01 Automated image processing for enhanced defect detection of horticultural products. Currently, machine vision technology is widely used for automated inspection of horticultural products, but its capability of detecting defects, both surface and subsurface, still falls short of industry expectations. An ARS-led team of researchers at East Lansing, Michigan, first developed a new imaging technique based on the concept of structured lighting, which demonstrated superior capabilities of detecting surface and subsurface defects of horticultural products. A user-friendly computer program incorporating several new image processing algorithms was developed for automated image acquisition, enhancement and processing, and defect classification, a critical step in implementation of this new technique for online commercial use. The program provides an effective tool for fast, automated processing of images acquired under structured lighting as well as under conventional uniform lighting configurations. It would enable implementation of the new imaging technique for enhanced quality evaluation of horticultural products.
Impacts
(N/A)
Publications
- Hu, D., Lu, R., Huang, Y., Ying, Y., Fu, X. 2020. Effects of optical variables in a single integrating sphere system on estimation of scattering properties of turbid media. Biosystems Engineering. 194:82-98.
- Lu, Y., Lu, R. 2019. Enhancing chlorophyll fluorescence imaging under sttructured illumination with automatic vignetting correction for detection of chilling injury in cucumbers. Computers and Electronics in Agriculture. 168:105145.
- Huang, Y., Lu, R., Chen, K. 2019. Detection of internal defect of apples by a multichannel Vis/NIR spectroscopic system. Postharvest Biology and Technology. 161:111065.
- Hu, D., Lu, R., Ying, Y. 2020. Spatial frequency domain imaging coupled with frequency optimization for estimating optical properties of two- layered food and agricultural products. Journal of Food Engineering. 277:109909.
- Sun, Y., Lu, R., Wang, X. 2020. Evaluation of fungal infection in peaches based on optical and microstructural properties. Postharvest Biology and Technology. 165:111181.
- Lu, R., Van Beers, R., Saeys, W., Li, C., Cen, H. 2019. Measurement of optical properties of fruits and vegetables: A review. Postharvest Biology and Technology. 159:111003.
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Progress 10/01/18 to 09/30/19
Outputs
Progress Report Objectives (from AD-416): 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. Approach (from AD-416): 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. Structured-illumination reflectance imaging (SIRI) provides a new modality for quality evaluation of horticultural and food products, owing to its ability of acquiring two sets of independent images, i.e., direct component (DC) and amplitude component (AC). DC images are equivalent to those obtained under conventional uniform or diffuse illumination, while AC images, which are unique to the SIRI technique, can provide more detailed features with higher resolution and contrast as well as the depth-resolving capabilities through modulation of the spatial frequency of illumination patterns. A user graphic interface (GUI) was developed for post-imaging processing and analysis, which includes demodulation of acquired SIRI images, image enhancement, and image processing and classification for defect detection. Experiments were conducted on using SIRI to detect early disease infection in navel oranges. The test fruit samples were inoculated with fungi to allow the disease infection to develop in the fruit over a period of 7 days. SIRI images were then acquired from the infected fruit for different spatial frequencies of illumination and at different wavelengths to determine the optimal illumination parameters for detection of early symptoms of disease infection in navel oranges, which are generally difficult to ascertain by visual inspection during the initial stage of infection. Preliminary analysis of SIRI images showed that the technique was promising for detecting early disease infection in navel oranges. Furthermore, experiments were carried out on using SIRI to detect subsurface bruising in tomatoes harvested at different stages of ripeness. SIRI images were processed and analyzed for determining the optimal spatial frequency and wavelength for bruise detection in tomatoes. Results showed that AC images provided more distinct features for bruised tissues and were thus advantageous for bruise detection. Moreover, the optimal spatial frequency of illumination was found to vary with the stage of tomato ripeness; higher frequencies were better for detecting bruises in green tomatoes, while low frequencies were more effective for detecting bruises in red or more ripe tomatoes. Many plant materials, upon excitation by, or absorption of, ultraviolet (UV) or shortwave visible light, will emit longer-wavelength radiation, which is called fluorescence. Chlorophyll fluorescence (CF) is sensitive to maturity, tissue damage and other tissue abnormalities. Hence, CF imaging can provide an effective means for detecting stress-induced defects (e.g., chilling injury) for green-skinned horticultural products like pickling cucumbers. The SIRI technique was used for detecting bruise damage symptoms in pickling cucumbers. Results showed that SIRI was able to enhance the detection of subsurface bruising in pickling cucumbers. Moreover, the SIRI technique, when integrated with CF, provided enhanced image features for differentiating normal and chilling-injury tissues, compared to conventional uniform illumination. A field study was conducted to evaluate different apple harvest methods in allocating the picker⿿s time for effective (i.e., detaching fruit from tree) and non-effective (i.e., reaching for and transporting picked fruit to the bucket or a similar fruit receiving device) picking activities, so as to help design a more efficient apple picking-aid technology. During the 2018 harvest season, video recordings were taken of fruit pickers working with the traditional bucket/ladder method, a self-driven commercial harvest platform with the use of picking buckets, and a commercial vacuum harvester that replaces both buckets and ladders, at three commercial orchards in Michigan. Analysis of the recorded video images showed that the actual time allocation for effective and non- effective picking activities varied with the harvest method used; the traditional bucket/ladder method was least efficient as pickers needed to spend a higher percent of their time on non-effective picking activities. Pickers, on average, spent between 24% and 29% of their total picking time on detaching fruit from trees, between 33% and 40% time on reaching for fruit, and between 24% and 29% on transporting picked fruit to the bucket (or the tube in the case of the vacuum harvester). This research shows that considerable improvement in harvest productivity can be achieved by reducing the time needed for non-effective picking activities (i.e., reaching and transporting). The knowledge gained from the study has been used in the design of a new apple picking-aid technology. Progress was made on the development of an improved apple harvest and infield sorting technology. A new, improved sorting mechanism, along with a new computer sorting algorithm, was designed and constructed. The new sorting mechanism is simpler and more compact in the overall system design; moreover, it is capable of sorting fruit at a rate of 11 fruit per second or higher without causing bruising damage to fruit during sorting. Laboratory tests showed that the new, improved sorting mechanism performed significantly better than the previous version, in term of sorting accuracy, throughput or sorting rate, and bruising damage. Improvements to the bin fillers in the mechanical design and software control were also made for better handling harvested fruit into individual bins or containers. Furthermore, a new apple picking-aid technology is being developed. These improved systems (i.e., the new sorter, improved bin fillers, and new harvest-aid platforms) are being integrated with the apple harvest and infield sorting machine developed earlier. Field tests and demonstration are planned for the 2019 harvest season. Accomplishments 01 Development of a new, improved automated apple infield sorting technology. Automated infield sorting enables low-quality or inferior fruit to be segregated from fresh-market quality fruit at the time of harvesting, so that these fruit can be handled differently and more economically in postharvest storage and packing. ARS researchers at East Lansing, Michigan, designed and constructed a new version automated infield sorting system. Compared to the earlier version, this new sorting system is simpler, more compact and reliable in performance, and capable of sorting fruit at a rate of 11 fruit per second or higher. Computer algorithms were also developed for the new sorting system. Laboratory tests showed that the new sorting system achieved 100% sorting accuracy with superior grading repeatability and no bruising damage to fruit during sorting. The new sorting system has been incorporated into the self-propelled apple harvest and infield machine and is ready for field testing and demonstration in the commercial orchard. With the adoption of this new, improved infield sorting technology, U.S. apple growers can achieve significant cost savings in postharvest handling of harvested fruit, improve postharvest management and reduce postharvest loss.
Impacts
(N/A)
Publications
- Huang, Y., Lu, R., Chen, K. 2017. Prediction of firmness parameters of tomato by portable visible and near-infrared spectroscopy. Journal of Food Engineering. 8:185-198.
- Lu, Y., Lu, R. 2019. Structured-illumination reflectance imaging for the detection of defects in fruit: Analysis of resolution, contrast and depth- resolving features. Biosystems Engineering. 180:1-15.
- Sun, Y., Lu, R., Lu, Y., Tu, K., Pan, L. 2019. Detection of early decay in peaches by structured-illumination reflectance imaging. Postharvest Biology and Technology. 151:68-78.
- Hu, D., Lu, R., Ying, Y., Fu, X. 2019. A stepwise method for estimating optical properties of two-layer turbid media from spatial-frequency domain reflectance. Optics Express. 27(2):1124-1141.
- Lu, Y., Lu, R. 2018. Detection of surface and subsurface defects of apples using structured-illumination reflectance imaging with machine learning algorithms. Transactions of the ASABE. 61(6):1831-1842.
- Mendoza, F., Wiesinger, J., Lu, R., Nchimbi, S., Miklas, P.N., Kelly, J.D., Cichy, K.A. 2018. Prediction of cooking time for soaked and unsoaked dry beans (Phaseolus vulgaris L.) using hyperspectral imaging technology. The Plant Phenome Journal.
- Zhang, Z., Pothula, A., Lu, R. 2018. A review of bin filling technologies for fruit harvest and postharvest handling. Applied Engineering in Agriculture. 34(4):687-703.
- Zhang, Z., Pothula, A., Lu, R. 2019. Improvements and evaluation of an in- field bin filler for apple bruising and distribution. Transactions of the ASABE. 62(2):271-280.
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Progress 10/01/17 to 09/30/18
Outputs
Progress Report Objectives (from AD-416): 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. Approach (from AD-416): 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. Spatially resolved spectroscopy (SRS) allows acquiring spectral information at multiple spatial-resolved distances from the illumination of a focused light source, thus enabling better assessment of quality and condition of fruit and other food products. Using the recently developed SRS system, spectral data were collected from tomatoes of different maturity stages, along with conventional single-point spectroscopy (SPS) covering the visible and near-infrared spectral region (400-1,700 nm). Calibration models were then developed using individual SRS spectra and their combinations, as well as the computed optical absorption and scattering spectra, for assessing the firmness, soluble solids content and pH of tomatoes. Results showed that prediction of the quality parameters for tomatoes varied with the spatial position of acquired spectra. Overall, combination of multiple SRS spectra resulted in more consistent, better prediction of quality parameters, and SRS was also superior to SPS in assessing these quality parameters. Conventional SPS is restricted in its ability of detecting internal defects that are distributed in small, discrete regions inside a fruit. A multichannel hyperspectral imaging system in semi-transmittance mode was constructed for detecting internally defective apples. The new multichannel system enables simultaneous acquisition of six spectra covering different sections of fruit in 360 degrees, thus having potential for more effectively detecting localized defects inside apple fruit. Experiments were conducted on detecting internal defect of �Honeycrisp� apples using the multichannel system. Multi-spectra were acquired for each apple in three different orientations. Classification models were developed for individual spectra and their averaged spectra for each of the three fruit orientations. Results showed that defect detection results varied with the position of acquired spectra and fruit orientation. Combination of the six spectra overall resulted in better results for classification of defective and normal apples, with the overall accuracies of as high as 96%. Spatial-frequency domain imaging (SFDI) provides a new means for measuring optical absorption and scattering properties of fruit and other food products, which are directly related to the chemical composition and physical properties of the products. Food products are generally heterogeneous in their structural and optical properties. For instance, many fruits are composed of skin and flesh, each of which has different properties. Hence it is desirable or necessary to be able to measure optical properties of each layer in order to better characterize the properties of samples. Measurement of optical properties for two-layer samples, however, presents many technical challenges due to the complexity in the mathematical model and a large number of optical parameters to be estimated. The conventional method with SFDI would estimate all four optical parameters (two for each layer) simultaneously, which often results in large, unacceptable estimation errors. To improve estimation accuracy for optical parameters with SFDI, a new stepwise method was proposed for estimating the optical parameters of two-layer samples. With this method, the optical properties of each layer are estimated separately and in multiple steps. Results for simulation samples demonstrated that the stepwise method greatly improved the accuracy of estimating optical properties of both layers, compared to the conventional one-step method. Moreover, the research also determined the constraining conditions on the appropriate range of top layer thicknesses, within which the optical properties of each layer can be estimated with acceptable accuracies. Good progress has been made on the development of structured- illumination reflectance imaging (SIRI) technique as a new modality for enhanced defect detection of fruit. A fast image preprocessing algorithm, called bi-dimensional empirical mode (BEMD), was developed for removing noise and artifacts in the demodulated SIRI images. Both simulation and experiment results showed that the proposed image preprocessing algorithm was effective in enhancing the features of SIRI images. The proposed BEMD method was further implemented in conjunction with three machine learning algorithms (i.e., support vector machine, random forest, and convolutional neural network) for processing and analyzing SIRI images to detect both surface and subsurface defects of �Delicious� and �Golden Delicious� apples. Superior classification results (up to 98% accuracy) were obtained with the convolutional neural network algorithm. Further experiments also were conducted on using SIRI to detect bruises in peaches and for early detection of disease infection in peaches. Analysis of the acquired images showed that SIRI performed consistently better than conventional uniform-illumination imaging technique for early detection of disease symptoms in peaches. These studies showed that SIRI coupled with appropriate image processing algorithms, can provide an effective means for enhanced detection of defects on fruit. Furthermore, an initial exploration was conducted of two instrumentation configurations for real-time acquisition of SIRI images from moving samples. Preliminary analysis showed that it is feasible to implement SIRI for fast, real-time image acquisitions from moving fruit, which opens an opportunity for practical use of the technique for quality evaluation of horticultural and food products. Progress has been made on the development of apple harvest and infield sorting technology. An improved version bin filler was constructed, tested and evaluated in both laboratory and field conditions. The performance of the bin filler in terms of fruit distributions in the bin was quantified using a 3-D depth imaging technique. Laboratory and field tests showed that the new bin filler performed much better than the previous version, in terms of fruit bruising and fruit distributions during the filling process. The 3-D depth imaging technique provides a new, quantitative means for evaluating the performance of bin fillers. In addition, laboratory tests were also conducted to evaluate the performance of the sorting system, in terms of singulating and rotating each fruit for imaging as well as sorting accuracies under different sorting speeds. During the 2017 harvest season, the apple harvest and infield sorting machine was tested in a commercial orchard. While the machine has met initial expectations, it also showed several areas needing improvement, which include improving the sorting system for more accurate, reliable sorting at a higher speed of up to 9 fruits/s, further improvement on the design and construction of bin fillers and their control/sensing system for better, more reliable operation, and improving the sensors and computer program for the automatic bin handling system. Accomplishments 01 Development of new apple bin filling technology. Infield sorting or removal of inferior fruit at the time of harvest will help U.S. apple growers achieve cost savings in postharvest storage and packing, and improve postharvest disease/pest and inventory management. Bin filling is crucial in the successful development of apple harvest and infield sorting technology. After evaluating and comparing different commercial and research bin filler designs, ARS researchers at East Lansing, Michigan developed a new bin filler system for integration with the apple harvest. With several innovative features, this new bin filler is compact and simple in design and fully automated with sensors and controls. Laboratory and field tests demonstrated that the new bin filler is able to distribute apples in the bin evenly, while causing minimal bruising damage to the fruit, which is well within the industry�s requirement. The new bin filler has been integrated with the apple harvest and automated infield sorting machine and it can also be used with other commercial harvest machines, so as to help the apple industry enhance harvest productivity and reduce postharvest handling cost. 02 Development of image processing algorithms for enhanced defect detection of fruit. Detection of fruit defects, both surface and subsurface, is still challenging because there are a large variety of defects, some of which are difficult to identify by using current computer vision systems. A new structured-illumination reflectance imaging (SIRI) technique was developed recently for detection of surface and subsurface defects for apples, because it provides higher spatial resolutions and greater image contrasts and allows better control of light penetration in the fruit. However, accurate detection of defects by SIRI also depends on the development of effective image processing and classification algorithms for identifying defective tissues from normal ones. ARS researchers at East Lansing, Michigan developed a fast image preprocessing algorithm called bi-dimensional empirical mode decomposition (BEMD), for removal of noise and artifacts in the SIRI images. Simulation and experimental results showed that SIRI coupled with BEMD and a machine learning algorithm (i.e., convolutional neural network) significantly improved the accuracy of detecting surface and subsurface defects of apples and also enabled effective detection of early disease symptoms in peaches. Implementation of SIRI with these image processing algorithms will offer a new imaging modality for enhanced quality inspection of horticultural and food products, which can help the horticultural industry deliver better quality products to the consumer.
Impacts
(N/A)
Publications
- Lu, Y., Lu, R. 2018. Structured-illumination reflectance imaging coupled with phase analysis techniques for surface profiling of apples. Journal of Food Engineering. 232:11-20.
- Huang, Y., Lu, R., Xu, Y., Chen, K. 2018. Prediction of tomato firmness using a spatially-resolved multichannel hyperspectral imaging probe. Postharvest Biology and Technology. 140:18-26.
- Huang, Y., Hu, D., Lu, R., Chen, K. 2018. Quality assessment of tomato quality by optical absorption and scattering properties. Postharvest Biology and Technology. 143:78-85.
- Huang, Y., Lu, R., Chen, K. 2018. Assessment of tomato soluble solids content and pH by spatially-resolved and conventional Vis/NIR spectroscopy. Journal of Food Engineering. 236:19-28.
- Pothula, A., Zhang, Z., Lu, R. 2018. Design features and bruise damage evaluation of an apple harvest and infield sorting machine. Transactions of the ASABE. 61(3):1135-1144.
- Li, R., Lu, Y., Lu, R. 2018. Structured illumination reflectance imaging for enhanced detection of subsurface tissue bruising in apples. Transactions of the ASABE. 61(3):809-819.
- Lu, Y., Lu, R. 2018. Fast bi-dimensional empirical mode decomposition as an image enhancement technique for fruit defect detection. Computers and Electronics in Agriculture. 152:314-323.
- Zhang, Z., Pothula, A., Lu, R. 2017. Development and preliminary evaluation of a new bin filler for apple harvesting and infield sorting. Transactions of the ASABE. 60(6):1839-1849.
- Zhang, Z., Pothula, A.K., Lu, R. 2017. Economic evaluation of apple harvest and in-field sorting technology. Transactions of the ASABE. 60(5) :1537-1550.
- Lu, Y., Lu, R. 2017. Non-destructive defect detection of apples by spectroscopic and imaging technologies: A review. Transactions of the ASABE. 60(5):1765-1790.
- Hu, D., Lu, R., Ying, Y. 2018. A two-step parameter optimization algorithm for improving estimation of optical properties using spatial frequency domain imaging. Journal of Quantitative Spectroscopy & Radiative Transfer. 207:32-40.
- Mendoza, F., Cichy, K.A., Sprague, C., Goffnet, A., Lu, R., Kelly, J.D. 2017. Prediction of canned black bean texture (Phaseolus vulgaris L.) from intact dry seeds using visible/near-infrared spectroscopy and hyperspectral imaging data. Journal of the Science of Food and Agriculture. 98(1):283-290.
- Lu, Y., Lu, R. 2017. Development of a multispectral structured- illumination reflectance imaging (SIRI) system and its application to bruise detection of apples. Transactions of the ASABE. 60(4):1379-1389.
- Liu, Z., He, Y., Cen, H., Lu, R. 2018. Deep feature representation with stacked sparse auto-encoder and convolutional neural network for hyperspectral imaging-based detection of cucumber defects. Transactions of the ASABE. 61(2):425-436.
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Progress 10/01/16 to 09/30/17
Outputs
Progress Report Objectives (from AD-416): 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. Approach (from AD-416): 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. Optical absorption and scattering properties are useful for assessing chemical compositions and structural properties of horticultural and food products. Spatial frequency domain imaging (SFDI) is an emerging technique that enables measuring and mapping the optical absorption and scattering properties of biological and food products over a large area. However, it requires using sinusoidal patterns of illumination with multiple phase shifts over a range of spatial frequencies. Since SFDI technique is prone to measurement and computational errors, computer simulations, using Monte Carlo (a statistical method) and finite element (a numerical approximation method), were conducted for determining the optimal range of spatial frequencies and spatial frequency resolution, and the best data fitting method for estimating the optical absorption and scattering properties. Experiments were further carried out to validate the computer simulation results using liquid samples of known properties. Results showed that using the proposed algorithms coupled with the optimal parameters resulted in significantly improved results in estimating the optical properties. The work lays a foundation for improving the SFDI technique for measuring optical absorption and scattering properties of horticultural and food products. Defects detection of fruit is still a challenging task for machine vision technology, because there are many different types of defects, some of which can be confused with normal, healthy tissues, thus resulting in false detection results. Our recent research has demonstrated that structured-illumination reflectance imaging (SIRI) technique can provide an effective means for detecting defects, such as bruises, on apples. Further enhancements to the SIRI system have been made by integrating the capability of acquiring multispectral images in the visible and near- infrared region. A new algorithm was developed for reconstructing the three-dimensional profile or surface geometry of fruit from the acquired SIRI images. Experimental results showed that the algorithm was effective in reconstruction of the three-dimensional shape of apples, which can be used for enhancing the detection of surface defects on apples. Further research was conducted on automatic thresholding of SIRI images, a critical step in extracting important image features for classification of defective tissues from normal tissues. After evaluation and comparison of different image thresholding methods, a general automatic thresholding methodology was developed for fast and effective segmentation of bruising areas from the SIRI images. Good classification results for fresh bruises on apples were obtained by using the new methodology. Currently, visible and near-infrared (Vis/NIR) spectroscopy is widely used for assessing properties and quality attributes of horticultural and food products. Since conventional Vis/NIR technique only acquires single spectra from a point or small area of the sample, it cannot give accurate measurements of food products those properties vary spatially or with depth. Furthermore, conventional Vis/NIR technique also is unable to measure the absorption and scattering properties of turbid foods, the two fundamental optical properties that are related to chemical compositions and structural characteristics. To address these shortcomings with conventional Vis/NIR technique, a new multichannel hyperspectral imaging sensor was developed, which enables acquiring 30 spatially resolved spectra from a sample over a broader spectral region of 550-1,650 nm at large light source-detector distances from 1.5 mm to 36 mm. The sensor can also be used for measuring food samples of flat and irregular or curved surface. Three types of calibration were carried out for the sensor to ensure it performs satisfactorily. The sensor was further tested and validated using reference liquid samples of known optical properties. Experiments were conducted on using the sensor to measure postharvest quality (firmness, soluble solids content and pH) of tomatoes. The sensor achieved superior performance, compared with Vis/NIR technique. Good progress has been made on further development of apple harvest and infield sorting technology. Bin filling is critical for the apple harvest and infield sorting machine. An improved version of bin fillers was developed, which enables better control of the movement of the bin fillers, more gentle handling of apples, and better distributions of apples in the bin. Moreover, an automatic control system was developed for handling empty and full bins. Implementation of this new function would greatly improve harvest efficiency by reducing down times for handling empty and full bins. Finally, all harvest conveyors used for transporting apples were redesigned with improved performance. Preliminary laboratory tests were conducted on evaluating the performance of the bin fillers, and results showed that they have met the requirements for the new apple harvest and infield sorting machine. In addition, improvements to the sorting system were also made so that it can sort and grade apples at a speed of at least six fruit per second. The improved apple harvest and infield sorting machine has been scheduled for field testing and evaluation during the 2017 apple harvest season. Accomplishments 01 Development of a new optical sensor for food quality detection. Currently, visible and near-infrared technique (Vis/NIR) is being used for quality assessment of horticultural and food products. Since it is only able to acquire a single spectrum from a sample, the technique is insufficient for assessing food products whose properties and characteristics vary spatially or with depth. Moreover, conventional Vis/NIR technique cannot directly measure optical absorption and scattering properties of food products, which are useful for assessing the chemical and structural properties. Researchers at East Lansing, Michigan developed a multichannel hyperspectral imaging sensor for simultaneous acquisition of 30 spatially resolved spectra of 550-1,650 nm for food samples of either flat or curved surface for large light source-detector distances. Experimental results showed that this new sensor was able to measure optical absorption and scattering properties of tomatoes, and it gave more accurate assessment of postharvest quality of tomatoes, compared with conventional Vis/NIR technique. The sensor provides a new means for more effective quality assessment of horticultural and food products.
Impacts
(N/A)
Publications
- Lu, Y., Lu, R. 2016. Using composite sinusoidal patterns in structured- illumination reflectance imaging (SIRI) for enhanced detection of defects in food. Journal of Food Engineering. 199:54-64.
- Lu, Y., Li, R., Lu, R. 2016. Gram-Schmidt orthonormalization for retrieval of amplitude images under sinusoidal patterns of illumination. Applied Optics. 55(5):6866-6873.
- Zhu, Q., Xing, Y., Lu, R., Huang, M., Ng, P. 2017. Vis/SWNIR spectroscopy and hyperspectral scattering for determining bulk density and particle size of wheat flour. Journal of Near Infrared Spectroscopy. 25(2):116-126.
- Lu, Y., Huang, Y., Lu, R. 2017. Innovative hyperspectral imaging-based techniques for quality evaluation of fruits and vegetables: a review. Applied Sciences. 7:189.
- Lu, Y., Lu, R. 2017. Histogram-based automatic thresholding for bruise detection of apples by structured-illumination reflectance imaging. Biosystems Engineering. 160:30-41.
- Hu, D., Lu, R., Ying, Y. 2017. Finite element simulation of light transfer in turbid media under structured illumination. Applied Optics. 56(21):6035- 6042.
- Huang, Y., Lu, R., Chen, K. 2017. Development of a multichannel hyperspectral imaging probe for property and quality assessment of horticultural products. Postharvest Biology and Technology. 133:88-97.
- Wang, A., Lu, R., Xie, L. 2017. Improved algorithm for estimating optical properties of food and biological materials using spatially-resolved diffuse reflectance. Optics Express. 212:1-11.
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Progress 10/01/15 to 09/30/16
Outputs
Progress Report Objectives (from AD-416): 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. Approach (from AD-416): 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. Accomplishments 01 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. 02 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.
Impacts
(N/A)
Publications
- 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.
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