Identification of Disease Type of Tobacco Leaves Based on Near Infrared Spectroscopy and Convolutional Neural Network

Abstract

It is important to identify the types of tobacco diseases accurately and take effective control measures in time to improve the efficiency of tobacco planting. In this paper, a hand-held nearinfrared spectrometer was used to collect the spectral data of different types of tobacco disease samples. The training models were established via convolutional neural network algorithm. Meanwhile, the traditional classification algorithms support vector machine and back propagation neural network were also compared. The results showed that the prediction accuracy of convolutional neural network algorithm was the highest and the overall performance of the model was the best. The rapid detection method based on a hand-held near-infrared spectrometer and convolutional neural network algorithm could identify tobacco leaf disease species efficiently, non-destructively, quickly and accurately, which provided a new technical reference for tobacco leaf disease species detection and identification.