Progress 07/07/10 to 05/18/15
Outputs
Progress Report 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. 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. Harvest and postharvest packing are two major cost components in apple production. There is a critical need for appropriate technology to improve harvest efficiency and worker safety and address the issue of agricultural labor shortage. Moreover, technology is also needed for sorting and grading apples in the orchard, so as to help growers achieve cost savings in postharvest storage and packing and enhance product quality and traceability. ARS researchers at East Lansing, Michigan recently developed cost-effective, automatic in-orchard sorting technology, which sorts apples into two or three quality grades for size and color. Further research, however, is needed to improve the sorting and handling functions of the technology and integrate it with existing or new harvest systems, so that apple growers can achieve cost savings and productivity improvement. Research was carried out on the design of a new apple harvest and automated sorting system with enhanced harvest and in-orchard sorting capabilities. An overall design for the system has been completed, which incorporates important functions like self- propelling, harvest aiding/enhancing and automatic handling of fruit bins. A new, simpler fruit sorter design was proposed and constructed to replace the current one. Collaboration with a commercial horticultural equipment manufacturer has been established for the joint development and transfer of the new apple harvest and automatic sorting technology for commercial use. An advisory group consisting of apple growers, university extension specialists and packinghouse manager has been formed to provide input and advice for the project and facilitate the transfer of the technology under development to apple growers. Research was conducted to measure the sucrose, soluble solids and moisture contents and mechanical properties of sugar beets using a spectral scattering technique developed by ARS researchers at East Lansing, Michigan. Calibration models were developed relating spectral scattering data to the standard reference measurements of sucrose, soluble solids and moisture contents and mechanical compressive properties of beet samples. Spectral scattering technique achieved good to excellent results for predicting the sucrose, soluble solids and moisture contents of beets, while poor results were obtained for prediction of the mechanical properties of sugar beets. The research demonstrated that spectral scattering technique is useful for predicting the composition and quality of sugar beets, and it can help breeders and growers in germplasm selection for high sucrose yield and in determining the optimum harvest time and processing quality of beets. Research was conducted on the development of spatially-resolved spectroscopic technique to measure the optical absorption and scattering properties of horticultural and food products, for better assessment of product quality and condition. A new lighting configuration was proposed for improving the measurement of optical properties. Quantitative analyses were performed to compare the new lighting configuration with the existing lighting configuration, and it was found that the new lighting configuration showed potential for improving the measurement of optical properties. Research was also conducted on modeling light transfer in biological tissues like fruit by using finite element method, a powerful computer simulation method, in order to improve and optimize the measurement of optical properties and hence quality for horticultural products. The research examined different boundary conditions and their effects on the propagation of light in the fruit tissue. The propagation of light in the tissue was found to be dependent on the type of boundary condition imposed on the fruit, and appropriate boundary conditions were determined for accurate simulation of light propagation in biological tissues. Research confirmed that light beam has a significant effect on the measurement accuracy of optical absorption and scattering properties and it is important that an appropriate size light beam be used in measuring the optical properties of horticultural and food products. The research provides a foundation for further study of different lighting configurations and sensing probes in order to improve the accuracy of measuring the optical properties of horticultural and food products, which are either homogeneous or composed of two layers of homogenous tissues like fruit skin and flesh. Conventional imaging techniques rely on uniform illumination for detecting surface defects and/or internal quality of horticultural products, which have only achieved limited success. Structured lighting with different spatial patterns has been demonstrated in recent research to be useful for imaging internal biological tissues and for reconstructing three-dimensional objects. Research was initiated on using structured lighting for more effective detection of internal quality, including defect, of horticultural products. An imaging system with a structured lighting configuration was assembled and initial tests were performed on samples embedded with foreign objects under different lighting patterns. Image processing algorithms are being developed to extract useful features for effective detection of embedded objects and defects in the samples. Accomplishments 01 Predicting bruise susceptibility of apples by optical properties. Apples are susceptible to bruising during harvest, transport and postharvest handling. To mitigate bruising damage, it is important to be able to predict the bruise susceptibility of apples, which is conventionally evaluated by destructive impact method. Research was conducted to nondestructively predict the susceptibility of apples to impact bruising by using a spectral scattering technique recently developed by ARS researchers at East Lansing, Michigan. The bruising susceptibility of apples was evaluated using impact test and the actual bruise volumes were estimated by computer vision technique. Spectral scattering measurements gave good prediction of the bruise susceptibility of individual apples. The research, for the first time, demonstrated that spectral scattering technique is useful for assessing the susceptibility of apples to impact damage, and it can provide additional useful information about quality of apples during postharvest sorting and grading. 02 Hyperspectral imaging for detecting chilling injury in cucumbers. Chilling injury is a physiological disorder that often occurs after cucumbers have been subjected to low temperatures. Early detection of chilling injury in cucumbers will help growers and food processors deliver quality products to the consumer. Research was conducted by applying hyperspectral imaging technique, an optical technique for acquiring both spectral (wavelength) and spatial (image) information from objects, to detect chilling injury in cucumbers. A hyperspectral imaging system, developed by ARS researchers at East Lansing, Michigan, was used to acquire reflectance images in the visible region and transmittance images in the near-infrared region for each cucumber simultaneously. Optimal wavelengths were identified for classification of normal and chilling injured cucumbers and computer algorithms were developed, which achieved high rates of classification accuracy. The hyperspectral imaging technique, coupled with the optimal wavelengths, can be adopted for offline and online inspection of chilling injured cucumbers to improve food quality.
Impacts
(N/A)
Publications
- Mendoza, F., Cichy, K.A., Lu, R., Kelly, J.D. 2014. Evaluation of canning quality traits in black beans (Phaseolus vulgaris L.) by visible/near- infrared spectroscopy. Food and Bioprocess Technology. 7:2666-2678.
- Pan, L., Lu, R., Zhu, Q., McGrath, J.M., Tu, K. 2015. Measurement of moisture, soluble solids, and sucrose content and mechanical properties in sugar beet using portable visible and near-infrared spectroscopy. Postharvest Biology and Technology. 102:42-50.
- Zhu, Q., He, C., Lu, R., Mendoza, F., Cen, H. 2015. Ripeness of 'Sun Bright' tomato using the optical absorption and scattering properties. Postharvest Biology and Technology. 103:27-34.
- Rady, A., Guyer, D.E., Lu, R. 2015. Evaluation of physiological status of potato tubers using hyperspectral imaging. Food and Bioprocess Technology. 8(5):995-1010.
- Lu, R., Park, B. 2015. Introduction. In: Park, B., Lu, R., editors. Hyperspectral Imaging Technology In Food and Agriculture. New York, NY: Springer. p. 3-8.
- Mendoza, F., Lu, R. 2015. Basics of image analysis. In: Park, B., Lu, R., editors. Hyperspectral Imaging Technology In Food and Agriculture. New York, NY: Springer. p. 9-56.
- Lu, R., Cen, H. 2015. Measurement of food optical properties. In: Park, B., Lu, R., editors. Hyperspectral Imaging Technology In Food and Agriculture. New York, NY: Springer. p. 203-226.
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Progress 10/01/13 to 09/30/14
Outputs
Progress Report 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. 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. In-orchard sorting/grading of apples can help growers achieve cost savings in production and postharvest storage and packing and enhance product quality and traceability. To achieve this goal, an automatic in- orchard sorting system that is compact, reliable, easy to operate and low in cost is needed, in addition to meeting all sorting and grading requirements. Efforts were made in developing a new version in-orchard fruit sorter that is compact in size and can be operated more efficiently and reliably, compared to the current version. A prototype sorter was designed, assembled and tested, which showed initial promising results. In addition, a new concept of handling graded fruit was proposed for implementation with the apple harvesting and sorting system. The new design concept, after incorporated into the system, will significantly improve the efficiency of handling graded apples and avoid the problem of having fruit bins that are not fully filled. Progress was made in improving the online hyperspectral imaging system for automatic inspection of cucumbers for both external and internal quality characteristics. Improvements to the hardware and software were accomplished, which include the redesign and assembly of the imaging chamber and the sample handling unit. The improved system allows acquiring hyperspectral transmittance/reflectance images from cucumbers and other food products in both stationary and online configurations. Experiments were conducted to acquire hyperspectral transmittance/ reflectance images for automatic detection of defective cucumbers (i.e., misshapen, mechanical damage, abnormal fruit, chilling injury, physiological disorder, etc.). A new algorithm is being developed to automatically extract important features for characterizing the defect of cucumbers. Initial evaluation of the algorithm showed excellent detection results for chilling injured cucumbers, with the accuracy being greater than 90%. An extensive study of different near-infrared sensing systems that are used in laboratories and commercial packinghouses was conducted. A new visible and near-infrared multiple-sensing configuration was proposed and is being assembled for detecting internal quality and condition of apple and other fruits. The new multiple-sensing configuration acquires multiple spectra simultaneously from each fruit over the visible and near- infrared spectral region, allowing for more effective detection of internal quality of fruit. A study was initiated to determine how variety, quality variability, sampling technique, harvest season, and data processing method would affect the prediction model performance for apple firmness and soluble solids content, using spectral scattering data. The spectral scattering data were collected for three varieties of apple over two harvest seasons. A new algorithm of predicting apple firmness and soluble solids content was developed and tested for the three varieties of apple. Statistical analysis was conducted to quantify the effect of each factor on the prediction of apple firmness and soluble solids content. Recommendations were made on appropriate sampling and data processing procedures for development of a robust model for quality prediction of apples. Accomplishments 01 Visible and near-infrared (Vis/NIR) spectroscopy for quality evaluation of sugar beet. Sucrose, moisture and soluble solids content are important quality attributes for sugar beet, but current methods for measuring them are destructive and time consuming. Effective and fast measurement of these attributes will help the breeder, grower and processor in breeding, production, postharvest handling and processing of sugar beet. ARS researchers at East Lansing, Michigan tested and evaluated two portable Vis/NIR spectrometers, operated in a specially- designed sensing configuration, for rapid measurement of sucrose, moisture and soluble solids content for intact and sliced beets. Statistical models were developed for the Vis/NIR spectral data to predict the quality attributes of beets. The portable spectrometers gave good measurement of sucrose, moisture and soluble solids content for both intact and sliced beets, when compared with standard reference methods. Portable Vis/NIR spectrometry can be potentially used for measuring or monitoring the sucrose, moisture, and soluble solids content of beets in the field and at the sugar processing facility after harvest. 02 Spectral scattering for maturity/quality assessment of peach and tomato. Spectral scattering is a new technique for quantification of light absorption and scattering in plant and food products. The technique has proven useful for assessing composition and texture of plant products like apple. Research was conducted on using the spectral scattering system developed by ARS researchers at East Lansing, Michigan, to assess the maturity and/or postharvest quality of peach and tomato, two important horticultural crops. Spectral scattering data were acquired from peaches and tomatoes harvested at different degrees of maturity, from which optical absorption and scattering information was extracted. Maturity parameters, including color, firmness and soluble solids content, were assessed using standard destructive methods. Calibration models were developed to predict the maturity parameters and for classification of tomatoes and peaches into different maturity/quality grades. Good predictions of firmness and soluble solids content were achieved for peach fruit, while accurate classification (around 90%) of tomatoes for maturity grades was obtained. Wavelengths that are important for prediction or classification of peach and tomato fruit for maturity were also identified. Spectral scattering technique for optical absorption and scattering measurement is useful for maturity assessment of horticultural products, which can help producers deliver superior, consistent products to the market to meet or exceed consumer expectations.
Impacts
(N/A)
Publications
- Cen, H., Lu, R., Ariana, D.P., Mendoza, F. 2014. Hyperspectral imaging- based classification and wavebands selection for internal defect detection of pickling cucumbers. Food and Bioprocess Technology. 7:1689-1700.
- Mendoza, F., Lu, R., Cen, H. 2014. Grading of apples based on firmness and soluble solids content using VIS-SWNIR spectroscopy and spectral scattering techniques. Journal of Food Engineering. 125(3):59-68.
- Qibing, Z., Huang, M., Lu, R., Mendoza, F. 2014. Analysis of hyperspectral scattering images using a moment method for apple firmness prediction. Transactions of the ASABE. 57(1):75-83.
- Leiva-Valenzuela, G., Lu, R., Aguilera, J. 2014. Assessment of internal quality of blueberries using hyperspectral transmittance and reflectance images with whole spectra or selected wavelengths. Innovative Food Science and Emerging Technologies. DOI: 10.1016/j.ifset.2014.02.006.
- Pan, L., Zhu, Q., Lu, R., McGrath, J.M. 2014. Determination of sucrose content in sugar beet by portable visible and near-infrared spectroscopy. Food Chemistry. DOI: 10.1016/j.foodchem.2014.06.117.
- Lu, R., Cen, H. 2013. Non-destructive methods for food texture assessment. In: Kilcast, D., editor. Instrumental Assessment of Food Sensory Quality - A Practical Guide. New York: Elsevier. p. 230-254.
- Lu, R. 2013. Principle of solid food texture analysis. In: Kilcast, D., editor. Instrumental Assessment of Food Sensory Quality - A Practical Guide. New York: Elsevier. p. 103-128.
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Progress 10/01/12 to 09/30/13
Outputs
Progress Report 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. 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. Spectral scattering is useful for nondestructive sensing of fruit firmness. The technique, however, depends on appropriate quantification of scattering features and the development of a reliable statistical prediction model relating the scattering features to fruit firmness. A new method, called moment method, was proposed to extract important features from the spectral scattering images, and it was evaluated for �Delicious�, �Golden Delicious� and �Jonagold� apples. The method resulted in consistently better prediction of fruit firmness, compared with the mean reflectance method used in previous studies. Since spectral scattering for firmness prediction is influenced by such factors as the variability of firmness in the calibration samples, data processing method and harvest season, research was carried out to evaluate the effect of these factors and their interactions on the performance of the firmness prediction models for three cultivars of apple. The model performance generally improved with the increasing number of samples used for building the prediction model. Overall about 400 samples with a representative range of firmness were needed for building a robust firmness prediction model. Progress was made on the development of a mobile system for harvesting and sorting apples in the orchard. Harvest aid functions that are suitable for six to eight workers were incorporated into the mobile system, which enhance harvest efficiency and improve worker safety. Improvements to the bin filler designs were made for better delivery and distribution of harvested fruit into the bins. New functions and algorithms were developed and incorporated into the apple sorting/grading software program to provide more user-friendly interfacing in selecting grading criteria and quality grades. Research was carried out to improve the hyperspectral imaging system, operated in simultaneous reflectance and transmittance modes, for online inspection of both external and internal quality of pickling cucumbers. Two optimal wavelengths or wavebands were determined using two different wavelengths selection methods (i.e., minimum redundancy-maximum relevance and principal component analysis). Superior results in segregating defective cucumbers from normal ones were obtained, with an accuracy of greater than 94%. The identified wavebands can be implemented for fast online inspection of internal defect of pickling cucumbers. Firmness and soluble solids content (SSC) are important in assessing the quality and shelf life of blueberries. Hyperspectral reflectance and transmittance images were acquired from blueberries over the wavelengths of 500-1,000 nm. Statistical models were developed for prediction of firmness and SSC. Better predictions of SSC and firmness were obtained using reflectance mode than transmittance mode. Fruit orientation only had small effect on reflectance measurement and insignificant effect on transmittance measurement. Accomplishments 01 Optimization of spectral scattering measurement for fruit quality assessment. Spectral scattering technique provides an effective means for measuring light scattering in food products like apple, and scattering features are useful for predicting firmness and soluble solid content in fruit, two quality attributes that are important to the consumer. Appropriate quantification of scattering features and development of a proper statistical model are critical to accurate prediction of fruit firmness and other quality attributes. A new method, called moment method, was proposed and evaluated by ARS researchers in East Lansing, Michigan for describing spectral scattering features of apples. The method performed consistently better in predicting the firmness of three cultivars of apple, compared with the mean reflectance method used in previous studies. Since spectral scattering prediction of apple firmness is also influenced by the variability of firmness in the calibration samples, data processing method and harvest season, optimization of these factors was performed, which resulted in important recommendations on appropriate selection of these factors so as to achieve superior spectral scattering prediction of fruit firmness. These recommendations will help researchers and equipment developers better implement spectral scattering technique for quality assessment of fruit. 02 Spectral imaging classification of normal and defective pickling cucumbers. Pickling cucumbers are susceptible to internal damage due to adverse climate and growth conditions, pest and/or disease infestation, and improper harvest and postharvest operations. Effective detection and removal of defective cucumbers prior to brining is critical to the quality of final pickled products. ARS researchers at East Lansing, Michigan developed a hyperspectral imaging technique for detecting both external and internal quality defects of fresh pickling cucumbers. The technique is, however, not fast enough for online inspection of pickling cucumbers because it needs to acquire and process a large quantity of spectral image data. Research was thus carried out to improve the lighting configuration for better transmittance measurement of pickling cucumbers and to determine the optimal wavelengths or wavebands to meet the high speed inspection requirement. Hyperspectral reflectance and transmittance images of pickling cucumbers were acquired and analyzed to select the optimal wavebands for detecting defective cucumbers from normal ones. It was found that using the ratio of two optimal wavebands in the near-infrared region achieved more than 94% classification accuracy in differentiating normal and defective cucumbers. The identified wavebands can be implemented for rapid, real- time detection of defective pickling cucumbers, which will provide the pickle processor an effective inspection tool to ensure quality of pickled products.
Impacts
(N/A)
Publications
- Huang, M., Wang, B., Zhu, Q., Lu, R. 2012. Analysis of hyperspectral scattering images using locally linear embedding algorithm for apple mealiness classification. Computers and Electronics in Agriculture. 89:175- 181.
- Mendoza, F., Lu, R., Haiyan, C. 2012. Comparison and fusion of four nondestructive sensors for predicting apple fruit firmness and soluble solids content. Postharvest Biology and Technology. 73:89-98.
- Lu, R., Ariana, D.P. 2013. Detection of fruit fly infestation in pickling cucumbers using a hyperspectral reflectance/transmittance imaging system. Postharvest Biology and Technology. 81(1):44-50.
- Leiva-Valenzuela, G., Lu, R., Aguilera, J. 2013. Prediction of firmness and soluble solids content of blueberries using hyperspectral reflectance imaging. Journal of Food Engineering. 115(1):91-98.
- Mizushima, A., Lu, R. 2013. An image segmentation method for apple sorting and grading using support vector machine and Otsu's method. Computers and Electronics in Agriculture. 94(1):29-37.
- Mizushima, A., Lu, R. 2013. A low-cost color vision system for automatic estimation of apple fruit orientation and maximum equatorial diameter. Transactions of the ASABE. 56(3):813-827.
- Cen, H., Lu, R., Mendoza, F., Beaudry, R. 2013. Relationship of the optical absorption and scattering properties with mechanical and structural properties of apple tissue. Postharvest Biology and Technology. 83:33-38.
- Qin, J., Chao, K., Kim, M.S., Lu, R., Burks, T. 2013. Hyperspectral and multispectral imaging for evaluating food safety and quality. Computers and Electronics in Agriculture. 118:157-171.
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Progress 10/01/11 to 09/30/12
Outputs
Progress Report 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. 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. Experiments were continued for assessing the maturity and quality of apples during the 2011 harvest season and after harvest. Four sensors, including an in-house built bioyield firmness tester, an acoustic firmness sensor, a visible/near-infrared sensor, and an in-house built online spectral scattering system, were tested and evaluated for predicting the firmness and/or soluble solids content of three varieties of apple. Classification models were developed using the spectral scattering and visible/near-infrared spectroscopic data to sort the apples into two or three quality grades, and superior results for classification of apples into two classes of firmness with an accuracy of 98% for one variety were obtained. Further studies were also carried out to determine the optimal approach for upgrading the apple quality prediction/classification models by considering growing season, number of samples selected from prior harvest years, and optimal combinations of wavelengths. A prototype mobile system for infield sorting of apples was built, which has some unique features in fruit singulation and rotation for imaging, the delivery of fruit into bins, and the bin filler design. The prototype sorts apples into two or three quality grades (i.e., cull, processing and fresh market) at a speed of six fruit per second. Effort was made in developing computer algorithms for detecting defective fruit, including scab, cuts, hail damage, insect bites, etc. Color images were collected from the defective apples harvested from two Michigan State University research orchards in 2011. Initial evaluation of the defects detection algorithm showed promising results and further research is being carried out to improve the algorithm for detecting those defects or blemishes that are more difficult to identify. In addition, a new color image segmentation method, an important step in the image processing, was developed to improve the accuracy of segmenting dark colored fruit from the background. Research was carried out to develop algorithms for automatic detection and segregation of normal and mechanically injured pickling cucumbers from the hyperspectral reflectance/transmittance images, which were acquired using a laboratory online system under an improved lighting configuration. Initial results showed that with the new lighting design, the optimal wavelengths identified from the hyperspectral image data achieved superior results (95% or higher in accuracy) for differentiating defective pickling cucumbers from normal ones. Research was also conducted on the feasibility of hyperspectral imaging technique for assessing the firmness and soluble solids content of blueberries and for detecting mechanical bruising and shriveling in blueberries. Algorithms were developed for predicting the firmness and soluble solids content of blueberries using the hyperspectral images. Results showed that hyperspectral imaging can provide an effective means to differentiate between soft and firm blueberries, and it could be implemented for online sorting and grading of blueberries based on firmness and, possibly, soluble solids content. Accomplishments 01 A mobile system for infield sorting of apples. Currently, apples are ha harvested from the trees, and the harvested apples are then placed in wo or plastic containers without prior sorting and grading. This practice i not cost effective because growers may incur significant cost for postharvest storage and packing of inferior or defective fruit. Moreover defective fruit are prone to pest and disease attacks, which are a major concern in postharvest storage and handling. Researchers at the ARS East Lansing, MI location developed a mobile system to automatically sort and grade apples into two or three quality grades (i.e., cull, processing an fresh market) in the orchard. The current version presorting system measures the color, size, shape and weight of each fruit, using color imaging-based machine vision technology. It also incorporates harvest ai functions to improve the working condition, and reduce safety hazard, fo workers picking apples from the trees. The technology will enable apple growers to segregate inferior or defective fruit in the orchard, which results in less postharvest disease/pest problems, lower postharvest storage and packing cost, and better fruit inventory management in the warehouse. 02 Hyperspectral imaging for quality evaluation of blueberries. The consumption of blueberry has been on steady increase over the past decad as consumers become more aware of its health benefits. To ensure product quality and shelf life, it is important that blueberries be sorted for firmness and soluble solids content, two important quality attributes fo this small fruit. Research was conducted to nondestructively assess the firmness and soluble solids content of blueberries using a hyperspectral reflectance imaging system developed by researchers at the ARS East Lansing, MI location, which acquires both spectral (wavelength) and spatial (image) information from each blueberry fruit. Fruit orientation (i.e., with the stem and calyx end facing towards the imaging device) we evaluated for their effect on the hyperspectral imaging prediction of fruit firmness and soluble solids content. The hyperspectral imaging system gave good predictions of fruit firmness and lower but promising predictions of soluble solids content. Further analysis showed that the technique could sort blueberries into two firmness grades (soft and firm while fruit orientation did not have a significant effect on the firmnes prediction. This research demonstrated that hyperspectral imaging technique can be implemented for online sorting and grading of blueberri for firmness and, possibly, soluble solids content to assure product quality and shelf life.
Impacts
(N/A)
Publications
- Mendoza, F., Lu, R., Ariana, D., Cen, H., Bailey, B.B. 2011. Integrated spectral and image analysis of hyperspectral scattering data for prediction of apple fruit firmness and soluble solids content. Postharvest Biology and Technology. 62(2):149-160.
- Cen, H., Lu, R., Mendoza, F., Ariana, D.P. 2012. Assessing multiple quality attributes of peaches using spectral absorption and scattering properties. Transactions of the ASABE. 55(2):647-657.
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Progress 10/01/10 to 09/30/11
Outputs
Progress Report 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. 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. Accomplishments 01 Machine vision system for automatic infield sorting of apples. Technolo for infield sorting and grading of apples is needed to help growers achieve savings in postharvest storage and packing, which are a major co component in apple production. A low-cost machine/computer vision system using a digital color camera was developed by researchers at the ARS Eas Lansing, MI location, to sort apples for size, shape and surface color a 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 enab U.S. apple growers to reduce overall production cost and better manage harvested apples during postharvest storage and handling. 02 Sensor data fusion for improving apple quality assessment. Nondestructi 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 son 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. 03 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 fo products. There is, however, a considerable knowledge gap in understandi 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 quali 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. Ther was a strong correlation between the optical absorption and scattering parameters and the elasticity of apple tissues. This research has provid new knowledge of the relationship between the optical, mechanical and micro-structural properties of apple tissue, and it has demonstrated tha the optical absorption and scattering parameters can be used to assess t structural characteristics and quality-related properties (i.e., firmnes and soluble solids content) of apple fruit.
Impacts
(N/A)
Publications
- Cen, H., Lu, R., Dolan, K. 2010. Optimization of the inverse algorithm for estimating the optical properties of biological materials using spatially- resolved diffuse reflectance technique. Inverse Problems in Science and Engineering. 18(6):853-872.
- Mizushima, A., Noguchi, N., Ishii, K., Matsuo, Y., Lu, R. 2011. Development of a low-cost attitude sensor for agricultural vehicles. Computers and Electronics in Agriculture. 76(2):198-204.
- Cen, H., Lu, R. 2010. Optimization of the hyperspectral imaging-based spatially-resolved system for measuring the optical properties of biological materials. Optics Express. 18(16):17412-17432.
- Lu, R., Ariana, D.P., Cen, H. 2011. Optical absorption and scattering properties of normal and defective pickling cucumbers for 700-1000 nm. Sensing and Instrumentation for Food Quality and Safety. 5(2):198-204.
- Ariana, D.P., Lu, R. 2010. Hyperspectral waveband selection for internal defect detection of pickling cucumbers and whole pickles. Computers and Electronics in Agriculture. 741(1):137-144.
- Mizushima, A., Lu, R. 2011. Cost benefits analysis of in-field presorting for the apple industry. Applied Engineering in Agriculture. 27(1):33-40.
- Ruiz-Altisent, M., Ruiz-Garcia, L., Moreda, G., Lu, R., Hernandez-Sanchez, N., Correa, E., Diezma, B., Nicolai, B., Garcia-Ramos, J. 2010. Sensors for product characterization and quality of specialty crops - A review. Computers and Electronics in Agriculture. 74(2):176-194.
- Huang, M., Lu, R. 2010. Apple mealiness detection using hyperspectral scattering technique. Postharvest Biology and Technology. 58(3):168-175.
- Huang, M., Lu, R. 2010. Optimal wavelengths selection for hyperspectral scattering prediction of apple firmness and soluble solids content. Transactions of the ASABE. 53(4):1175-1182.
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