Cong Sunli1, Sun Jun1, Mao Hanping2, Wu Xiaohong1, Wang Pei1, Zhang Xiaodong2. 1. School of Electrical and Information Engineering of Jiangsu University, Zhenjiang, China. 2. Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, Jiangsu University, Zhenjiang, China.
Abstract
BACKGROUND: Mold contamination of grains not only contributes to inedible food, resulting in economic losses, but also leads to mold in humans and livestock, and can even be carcinogenic to them. Rice, as one of the main grain varieties, if stored improperly, is easily susceptible to mildew. In order to detect the total number of mold colonies in rice more accurately, a method based on hyperspectral imaging technology was investigated. RESULTS: In this paper, non-destructive detection for the total number of mold colonies in rice was performed from the angle of spectral analysis. A determination coefficient of 0.9621 for the calibration set and 0.9511 for the prediction set between the spectral data and number of mold colonies were eventually achieved by establishing the best support vector regression (SVR) model, optimized by the Gray Wolf Optimization (GWO) algorithm. CONCLUSION: Hyperspectral imaging technology combined with the optimal model (GWO-SVR) is feasible for non-destructive detection of the total number of mold colonies in rice, providing a promising tool for the mold detection of other agricultural products.
BACKGROUND: Mold contamination of grains not only contributes to inedible food, resulting in economic losses, but also leads to mold in humans and livestock, and can even be carcinogenic to them. Rice, as one of the main grain varieties, if stored improperly, is easily susceptible to mildew. In order to detect the total number of mold colonies in rice more accurately, a method based on hyperspectral imaging technology was investigated. RESULTS: In this paper, non-destructive detection for the total number of mold colonies in rice was performed from the angle of spectral analysis. A determination coefficient of 0.9621 for the calibration set and 0.9511 for the prediction set between the spectral data and number of mold colonies were eventually achieved by establishing the best support vector regression (SVR) model, optimized by the Gray Wolf Optimization (GWO) algorithm. CONCLUSION: Hyperspectral imaging technology combined with the optimal model (GWO-SVR) is feasible for non-destructive detection of the total number of mold colonies in rice, providing a promising tool for the mold detection of other agricultural products.