Rapid and non-destructive characterization of post-harvest quality of seeds, fruits, and vegetables is paramount to increasing crop profitability for growers and nutritious value and product quality for customers. Furthermore, simulation techniques with measurements of tissue characteristics can establish reference datasets providing scientists useful information for understanding of tissue development at the fundamental level. Our lab has developed non-destructive sensing techniques (such as: hyperspectral imaging, electronic nose, and thermal imaging) for various crop postharvest quality sensing such as blueberry internal bruising, onion postharvest diseases, and cotton fiber foreign matter.
2014, Best Paper Award from the Information and Electrical Technologies (IET) division of the American Society of Agricultural and Biological Engineers (ASABE)
Zhang, M., C. Li, F. Takeda, and F. Yang. 2017. Detection of internally bruised blueberries using hyperspectral transmittance imaging. Transactions of ASABE, 60(5): 1-14.
Fan, S., Li, C., Huang, W., & Chen, L. (2017). Detection of blueberry internal bruising over time using NIR hyperspectral reflectance imaging with optimum wavelengths. Postharvest Biology and Technology, 134, 55-66.
Zhang, M., C. Li and F. Yang. 2017. Classification of Foreign Matter Embedded inside Cotton Lint using Short Wave Infrared (SWIR) Hyperspectral Transmittance Imaging. Computers and Electronics in Agriculture. 10.1016/j.compag.2017.05.005. In press.
Jiang, Y., C. Li, and F. Takeda. 2016. Nondestructive detection and quantification of blueberry bruising using near-infrared (NIR) hyperspectral reflectance imaging. Scientific Reports. 6: srep35679.
Zhang, R., C. Li, M. Zhang, and J. Rodgers. 2016. Shortwave infrared hyperspectral reflectance imaging for cotton foreign matter classification. Computers and Electronics in Agriculture. 127: 260-270.
Jiang, Y. and C. Li. 2015. mRMR-based feature selection for classification of cotton foreign matter using hyperspectral imaging. Computers and Electronics in Agriculture. 10.1016/j.compag.2015.10.017.
Chugunov, S. and C. Li. 2015. Monte Carlo simulation of light propagation in healthy and diseased onion bulbs with multiple layers. Computers and Electronics in Agriculture. 117: 91-101. DOI:1016/j.compag.2015.07.015.
Wang, W. and C. Li. 2015. A multimodal machine vision system for quality inspection of onions. Journal of Food Engineering. DOI: 10.1016/j.jfoodeng.2015.06.027.
Mustafic, A., Y. Jiang, and C. Li. 2015. Cotton contamination detection and classification using hyperspectral fluorescence imaging. Textile Research Journal. DOI: 10.1177/0040517515590416.
2015. Detection and Discrimination of Cotton Foreign Matter Using Push-Broom Based Hyperspectral Imaging: System Design and Capability. PLoS ONE 10(3): e0121969. doi: 10.1371/journal.pone.0121969.
Chugunov, S. and C. Li. 2015. Parallel implementation of inverse adding-doubling and Monte Carlo multi-layered programs for high performance computing systems with shared and distributed memory. Computer Physics Communications. DOI: 10.1016/j.cpc.2015.02.029.
Berry impact record device (BIRD)
Please visit the BIRD project for details.
Electronic nose (e-nose)
Konduru, T., G. Rains, and C. Li. 2015. Detecting sour skin infected onions using a customized gas sensor array. Journal of Food Engineering. 160: 19-27. DOI: 10.1016/j.jfoodeng.2015.03.025.
Konduru, T., G. Rains, and C. Li. 2015. A customized metal oxide semiconductor-based gas sensor array for onion quality evaluation: system development and characterization. Sensors. 15, 1252-1273.
Kuzy, J., Y. Jiang, and C. Li, 2017. Blueberry bruise detection by pulsed thermographic imaging. Postharvest Biology and Technology, 136 (2018): 166-177.
Kuzy, J. and Li, C., 2017. A Pulsed Thermographic Imaging System for Detection and Identification of Cotton Foreign Matter. Sensors, 17(3), p.518.
For more papers please visit the full publication list.