Dr. Renfu Lu is a Research Agricultural Engineer and Research Leader with the U.S. Department of Agriculture (USDA) Agricultural Research Service (ARS) - the principal federal government research agency in agriculture, food and natural resources. As Research Leader, he leads and manages the USDA/ARS research unit at Michigan State University, and supervises three federal government appropriated research programs in engineering, genetics, and pathology for fruits, vegetables, dry beans and sugar beet. His research is focused on sensing and automation for quality evaluation and grading of fruits and vegetables. His research applies state-of-the-art technologies in imaging, spectroscopy, sensors and controls as well as advanced mathematical/statistical methods, for assessing quality and condition of fruits and vegetables before, at and after harvest. As an Adjunct Professor, Dr. Lu supervises and mentors graduate students in Biosystems Engineering.
Fellow, American Society of Agricultural and Biological Engineers (ASABE), 2013
Outstanding Alumni Award, College of Agricultural Sciences, Pennsylvania State University, 2011
Federal Laboratory Consortium Technology Transfer Award, 2009
Select Paper Awards, ASABE Information and Electrical Technologies (IET) Division, 2006, 2008, 2009, 2010
Superior Paper Award, ASABE, 2004
Honorable Mention Paper Award, ASABE, 1997, 1998
Lu, Y., Li, R., and Lu, R. 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 127:652-656. 2016
Lu, Y., R. Li, and Lu, R. Structured-illumination reflectance imaging (SIRI) for enhanced detection of fresh bruises in apples. Postharvest Biology and Technology 117:89-93. 2016.
Lu, R. (ed.). Light Scattering Technology for Food Property, Quality and Safety Assessment, 459pp. CRC Press, Taylor & Francis Group. 2016 (Book)
Pan, L., Lu, R., Tu, K., and Cen, H. Predict composition and mechanical properties of sugar beet using hyperspectral scattering. Food and Bioprocess Technology 9:1177-1186. 2016.
Park, B. and Lu, R. (ed.). Hyperspectral Imaging Technology in Food and Agriculture, 403pp. Springer. 2015. (Book)
Zhu, Q., He, C., Lu, R., Mendoza, F., and Cen, H. Ripeness evaluation of ‘Sun Bright’ tomato using the optical absorption and scattering properties. Postharvest Biology and Technology 103:27-34. 2015.
Cen, H., Lu, R., Ariana, D. P., and Mendoza, F. Hyperspectral imaging-based classification and wavebands selection for internal defect detection of pickling cucumbers. Food and Bioprocess Technology. 7(6):1689-1700. 2014.
Leiva-Valenzuela, G. A., Lu, R., and Aguilera, J. M. Assessment of internal quality of blueberries using hyperspectral transmittance and reflectance images with whole spectra or selected wavelengths. Innovative Food Science and Emerging Technologies. 24(SI):2-13. 2014.
Lu, R. and Ariana, D. P. Detection of fruit fly infestation in pickling cucumbers using a hyperspectral reflectance/transmission imaging system. Postharvest Biology and Technology 81(1): 44-50. 2013.
Mendoza, F., Lu, R., and Cen, H. 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. 2014.