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

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.