Location: Sugarbeet and Bean Research
2019 Annual Report
Objectives
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
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
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
1. 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.
Review 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. https://doi.org/10.1016/j.biosystemseng.2019.01.014.
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. https://doi.org/10.1016/j.postharvbio.2019.01.011.
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. https://doi.org/10.1364/OE.27.001124.
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. https://doi.org/10.13031/trans.12930.
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. https://doi.org/10.2135/tppj2018.01.0001.
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.
