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

Wang Fei, zhang yuan, Jiang Yuying, Ge Hongyi, Chen Xinyu, L. Li
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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.
基于广义学习系统的太赫兹图像霉变小麦识别
传统的霉变小麦鉴定检测方法处理步骤复杂,耗时长,特征提取能力差,导致霉变小麦鉴定检测效果较差。提出了一种基于广义学习系统的F-C-BLS太赫兹光谱霉变小麦图像识别方法。为了提高图像质量,改进网络特征提取,构建了F-C-BLS霉变小麦分类识别模型。实验结果表明,我们的F-C-BLS网络的分类准确率比BLS、RF、CNN和RNN分别提高了5.11%、5.27%、3.89%和4.06%。因此,该算法可以有效地为小麦霉病的早期识别提供一种新的有效方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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