Testing Method of Wild Emmer Wheat Hardness in High Humidity Environment Based on Image Enhancement
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Abstract:
In order to effectively detect the grain hardness in high humidity environment, the hyperspectral image of wild two-grain wheat produced by a cereal processing factory was obtained by hyperspectral image system. The adaptive denoising method (PLS) based on matching idea was used to remove the banded noise of hyperspectral image of wild two-grain wheat and enhance the quality of hyperspectral image of wild two-grain wheat in high humidity environment. On this basis, the average value of spectral data of wild two-grain wheat was taken as spectral data, and the prediction model of grain hardness of wild two-grain wheat in high humidity environment was constructed to realize the accurate detection of grain hardness in high humidity environment. The simulation results showed that when the humidity of processing environment was 55%, the average value of grain hardness of wild two-grain wheat detected by the proposed method was 1745 g, and the difference between the proposed method and the standard infrared detection method was only 3 g. When the ambient humidity was increased to 75%, the result of the proposed method was 1712 g, which is 42 g different from the standard infrared detection method. The proposed method had a high precision in detecting grain hardness of wild two-grain wheat, which is better than the acoustic vibration frequency band amplitude characteristic method.
