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A Residual Neural Network based Method for The Classification of Tobacco Cultivation Regions Using Near-infrared Spectroscopy Sensors
Infrared Physics & Technology ( IF 3.3 ) Pub Date : 2020-12-01 , DOI: 10.1016/j.infrared.2020.103494 Daiyu Jiang , Guanqiu Qi , Gang Hu , Neal Mazur , Zhiqin Zhu , Di Wang
Infrared Physics & Technology ( IF 3.3 ) Pub Date : 2020-12-01 , DOI: 10.1016/j.infrared.2020.103494 Daiyu Jiang , Guanqiu Qi , Gang Hu , Neal Mazur , Zhiqin Zhu , Di Wang
Abstract Near-infrared (NIR) spectroscopy techniques have been widely used to classify tobacco cultivation regions. NIR spectroscopy of tobacco leaves involves a large number of correlated features, so it is difficult to find the connection between spectral data and tobacco cultivation regions. This paper proposes a novel classification model of tobacco cultivation regions that integrates residual network (ResNet) and NIR spectroscopy techniques. As the number of neural network layers increases, the network may have issues such as network degradation, gradient disappearance, and the reduction of sample recognition rate. The proposed model applies a residual module to a neural network which effectively solves or alleviates the vanishing gradient issues caused by the increase of network depth. This paper also adds balance and suppression factors to the loss function to solve the issues caused by uneven sizes of tobacco samples collected from different cultivation regions in the training process. In the proposed method, tobacco samples are marked as internal and external samples respectively during the training process. Internal samples are collected from the corresponding cultivation regions in the north, northeast, and northwest of Guizhou Province, China. External samples are collected from other cultivation regions. The weight distributions of internal and external samples can be adjusted by experimental results to improve the identification accuracy of the proposed solution. The size of training samples determines the generalization ability of the network and affects the experimental results. A parametric rectified linear unit (PReLU) function is integrated into the network, in which the parameters of a linear unit are adaptively learned to further improve the identification accuracy of the proposed solution. Compared with current mainstream methods, the experimental results confirm that the proposed model is superior in accurately identifying different cultivation regions of tobacco leaves.
更新日期：2020-12-01
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