Rapeseed Storage Quality Detection Using Hyperspectral Image Technology - An Application for Future Smart Cities

Abstract

Abstract At present, the application of hyperspectral image technology in image target detection is lacking black-and-white correction, and the average spectral reflectance cannot be calculated, which leads to large error in image feature detection and classification. In this study, hyperspectral image technology was applied to the detection of rapeseed storage quality, and germination detection was completed during the storage of rapeseed. The black-and-white board correction to the hyperspectral data was completed and the spectral characteristic curve of the rapeseed sample hyperspectral image was obtained. The average spectral reflectance is calculated, the threshold of hyperspectral image is estimated, and the correlation technique is used to denoise the hyperspectral image. Based on this, the edge feature of the rapeseed hyperspectral image is recognized, and the feature classification of the hyperspectral rapeseed image is realized by combining the gray co-occurrence matrix. The experimental results show that the proposed method can detect the germination of rapeseed with high precision under the application of hyperspectral image technology. This study provides a reliable basis for the application of hyperspectral image technology.