Retrieval of nicotine content in cigar leaves by remote analysis of aerial hyperspectral combining machine learning methods.

Cigar leaf is a special type of tobacco plant, which is the raw material for producing high-quality cigars. The content and proportion of nicotine and other composite substances of cigar leaves have a crucial impact on their quality and vary greatly with the time of harvest. Hyperspectral remote sensing technology has been widely used in the field of crop monitoring because of its advantages of large area coverage, fast information acquisition, short cycle turnover, strong real-time performance and high efficiency. Therefore, it is important to accurately monitor nicotine content of field crops in a timely manner in the production of high-quality cigar leaf. To this end, this study set out to measure crop reflectance spectra acquired by UAV drones from tobacco field crops by hyperspectral image acquisition. MSC, SG, and SNV were combined and applied to the raw data. The output of these operations was then further processed by CARS, SPA, and UVE algorithms to determine the nicotine sensitive bands. Three machine learning algorithms were then used to analyze the data: PLS, BP, RF, and the SVM. An inversion model of the content of nicotine was established, and the model was evaluated for accuracy. The main research conclusions are as follows: (1) With the increase in the rate of application of nitrogen fertilizer, the nicotine content of cigar leaves increased; (2) Processing data by the CARS, SPA, and UVE methods reduces the degree of data redundancy and information co-linearity in the screening of the content of nicotine sensitive bands; (3) The MSC-SNV-SG-CARS-BP model has the best predictive accuracy on the nicotine content. The prediction accuracy of the testing set was R