Skip to main content
ARS Home » Midwest Area » East Lansing, Michigan » Sugarbeet and Bean Research » Research » Research Project #428477

Research Project: Nondestructive Quality Assessment and Grading of Fruits and Vegetables

Location: Sugarbeet and Bean Research

2017 Annual Report

1. Enable new commercial imaging and spectroscopic methods to determine fruit and vegetable internal quality and maturity. 2. Enable new, economical, accurate, automated, in-orchard methods for commercial apple quality tracing and grading.

1) Improvements will be made in the method and technique for measuring the optical absorption and scattering properties of horticultural and food products that may be considered homogeneous or layered in the tissue structure. Factors affecting the optical property measurements, including light source configuration, the geometry and surface roughness of samples, and inverse algorithm, will be evaluated by using numerical simulation (e.g., Monte Carlo and finite element) and experiment for phantom tissues and real samples, so as to improve the measurement accuracy and reproducibility. New methods and algorithms will be developed for accurate measurement of the optical properties of layered food products. Experiments will be carried out to measure the optical properties of horticultural products like apple, orange, and pickling vegetable. The measured optical properties will be used to predict quality and condition of the products. 2) Research will be conducted on the development of a new sensing technique for more effective quality evaluation of horticultural and food products. Specifically, different light illumination and image acquisition methods will be investigated for detecting properties and characteristics of plant tissues at different depths. Light penetration characteristics in plant tissues will be studied through computer simulations and experimental tests. Image processing algorithms will be developed for extraction of important features from the reflectance images to characterize internal quality (including defect) of fruit and vegetable. A new sensing system that incorporates conventional imaging or hyperspectral imaging technique with the optimal lighting configuration and dedicated image processing algorithms will be assembled and evaluated for real time detection of internal quality for fruit and vegetable. 3) Research will be conducted to develop cost effective, automated in-orchard apple sorting technology. New and improved functions will be developed and incorporated into the machine vision system to allow more effective sorting and grading of different varieties of apple for color, size and defect. More efficient and reliable sorter and bin filler designs in modular format will be proposed, assembled and tested in laboratory and field. A new method for handling individual fruit bins will be proposed and implemented so that no fruit bins would be left half-filled and the possible down time for the harvest crew resulting from the bin handling would be eliminated or minimized. The new and improved sorting system will be integrated with either a self-propelled or tractor-driven harvest aid platform for automatically sorting and grading apples into two or three quality grades as well as enhancing harvest efficiency and worker safety. Laboratory and field tests and demonstrations will be carried out, in close collaboration with commercial equipment manufacturer, growers, and extension personnel, to facilitate the development and transfer of the technology to the end user.

Progress Report
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.

1. 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.

Review 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.