Hyperspectral dimension reduction and navel orange surface disease defect classification using independent component analysis-genetic algorithm

Jing Li; Liang He; Muhua Liu; Jinyin Chen; Long Xue

doi:10.3389/fnut.2022.993737

Hyperspectral dimension reduction and navel orange surface disease defect classification using independent component analysis-genetic algorithm

Front Nutr. 2022 Oct 19:9:993737. doi: 10.3389/fnut.2022.993737. eCollection 2022.

Authors

Jing Li¹, Liang He¹, Muhua Liu¹, Jinyin Chen², Long Xue^{1

3}

Affiliations

¹ Jiangxi Key Laboratory of Modern Agricultural Equipment, College of Engineering, Jiangxi Agricultural University, Nanchang, China.
² Collaborative Innovation Center of Postharvest Key Technology and Quality Safety of Fruits and Vegetables in Jiangxi Province, Nanchang, China.
³ Key Laboratory of Optics-Electrics Application of Biomaterials of Jiangxi Province, Jiangxi Agricultural University, Nanchang, China.

Abstract

Canker is a common disease of navel oranges that is visible before harvest, and penicilliosis is a common disease occurring after harvest and storage. In this research, the typical fruit surface, canker spots, penicillium spore, and hypha of navel oranges were, respectively, identified by hyperspectral imaging. First, the light intensity on the edge of samples in hyperspectral images was improved by spherical correction. Then, independent component images and weight coefficients were obtained using independent component analysis. This approach, combined with use of a genetic algorithm, was used to select six characteristic wavelengths. The method achieved dimension reduction of hyperspectral data, and the testing time was reduced from 46.21 to 1.26 s for a self-developed online detection system. Finally, a deep learning neural network model was established, and the four kinds of surface pixels were identified accurately.

Keywords: ICA-GA; canker; hyperspectral image; navel orange; penicilliosis.