Hyperspectral detection of fresh corn peeling damage using germinating sparse classification method

Zhenye Li; Jun Fu; Zhi Chen; Qiankun Fu; Xiwen Luo

doi:10.3389/fpls.2022.1039110

Hyperspectral detection of fresh corn peeling damage using germinating sparse classification method

Front Plant Sci. 2022 Nov 29:13:1039110. doi: 10.3389/fpls.2022.1039110. eCollection 2022.

Authors

Zhenye Li^{1

2}, Jun Fu^{1

2}, Zhi Chen^{1

3}, Qiankun Fu^{1

2}, Xiwen Luo^{1

4}

Affiliations

¹ Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun, China.
² Key Laboratory of Efficient Sowing and Harvesting Equipment, Ministry of Agriculture and Rural Affairs, Jilin University, Changchun, China.
³ Chinese Academy of Agricultural Mechanization Sciences, Beijing, China.
⁴ College of Engineering, South China Agricultural University, Guangzhou, China.

Abstract

Peeling damage reduces the quality of fresh corn ear and affects the purchasing decisions of consumers. Hyperspectral imaging technique has great potential to be used for detection of peeling-damaged fresh corn. However, conventional non-machine-learning methods are limited by unsatisfactory detection accuracy, and machine-learning methods rely heavily on training samples. To address this problem, the germinating sparse classification (GSC) method is proposed to detect the peeling-damaged fresh corn. The germinating strategy is developed to refine training samples, and to dynamically adjust the number of atoms to improve the performance of dictionary, furthermore, the threshold sparse recovery algorithm is proposed to realize pixel level classification. The results demonstrated that the GSC method had the best classification effect with the overall classification accuracy of the training set was 98.33%, and that of the test set was 95.00%. The GSC method also had the highest average pixel prediction accuracy of 84.51% for the entire HSI regions and 91.94% for the damaged regions. This work represents a new method for mechanical damage detection of fresh corn using hyperspectral image (HSI).

Keywords: damage detection; dictionary learning; fresh corn; hyperspectral image; sparse representation.