Prediction of moisture contents in green peppers using hyperspectral imaging based on a polarized lighting system

Mohammad Akbar Faqeerzada1   Anisur Rahman1,2   Geonwoo Kim3,*   Eunsoo Park1   Rahul Joshi1   Santosh Lohumi1   Byoung-Kwan Cho1,4,*   

1Department of Biosystems Machinery Engineering, College of Agriculture and Life Science,
2Department of Farm Power and Machinery, Bangladesh Agricultural University, Mymensingh-2202, Bangladesh
3Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Powder Mill Road, BARC-East, Bldg 303, Beltsville, MD 20705, USA
4Department of Smart Agriculture System, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Korea


In this study, a multivariate analysis model of partial least square regression (PLSR) was developed to predict the moisture content of green peppers using hyperspectral imaging (HSI). In HSI, illumination is essential for high-quality image acquisition and directly affects the analytical performance of the visible near-infrared hyperspectral imaging (VIS/NIR-HSI) system. When green pepper images were acquired using a direct lighting system, the specular reflection from the surface of the objects and their intensities fluctuated with time. The images include artifacts on the surface of the materials, thereby increasing the variability of data and affecting the obtained accuracy by generating false-positive results. Therefore, images without glare on the surface of the green peppers were created using a polarization filter at the front of the camera lens and by exposing the polarizer sheet at the front of the lighting systems simultaneously. The results obtained from the PLSR analysis yielded a high determination coefficient of 0.89 value. The regression coefficients yielded by the best PLSR model were further developed for moisture content mapping in green peppers based on the selected wavelengths. Accordingly, the polarization filter helped achieve an uniform illumination and the removal of gloss and artifact glare from the green pepper images. These results demonstrate that the HSI technique with a polarized lighting system combined with chemometrics can be effectively used for high-throughput prediction of moisture content and image-based visualization.

Figures & Tables

Fig. 1. Hyperspectral imaging system with a polarized lighting system. EMCCD, electron multiplying charge-coupled device.