Identification of moldy wheat in terahertz images based on broad learning system

Wang Fei; Zhang Yuan; Jiang Yuying; Ge Hongyi; Chen Xinyu; Li Li

doi:10.1117/12.2665559

31 January 2023 Identification of moldy wheat in terahertz images based on broad learning system

Wang Fei, Zhang Yuan, Jiang Yuying, Ge Hongyi, Chen Xinyu, Li Li

Proceedings Volume 12505, Earth and Space: From Infrared to Terahertz (ESIT 2022); 1250511 (2023) https://doi.org/10.1117/12.2665559
Event: Earth and Space: From Infrared to Terahertz (ESIT 2022), 2022, Nantong, China

Abstract

The traditional moldy wheat identification and detection method require complex processing steps, which take a long time and have less feature extraction ability, resulting in poor moldy wheat identification and detection. In this paper, a F-C-BLS terahertz spectral image recognition method for moldy wheat is proposed based on broad learning system. The F-C-BLS moldy wheat classification and recognition model is constructed to enhance the image quality and improve the network feature extraction. Experimental results show that the classification accuracy of our F-C-BLS network is 5.11%, 5.27%, 3.89 and 4.06% higher than that of BLS, RF, CNN and RNN, respectively. Therefore, our algorithm can effectively provide a new and effective method for the early identification of wheat mold.

Citation Download Citation

Wang Fei, Zhang Yuan, Jiang Yuying, Ge Hongyi, Chen Xinyu, and Li Li "Identification of moldy wheat in terahertz images based on broad learning system", Proc. SPIE 12505, Earth and Space: From Infrared to Terahertz (ESIT 2022), 1250511 (31 January 2023); https://doi.org/10.1117/12.2665559

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