Classification method for folded flue-cured tobacco based on hyperspectral imaging and conventional neural networks

The efficient classification of flue-cured tobacco by automated machines continues to pose a significant challenge. Producers are grappling with escalating labor shortages in the grading of flue-cured tobacco due to the demanding working conditions and a robust workforce. This paper proposes an innovative approach to ascertain the grades of folded flue-cured tobacco using hyperspectral imaging (HSI) technology in conjunction with a one-dimensional convolutional neural network (1D-CNN) model. Initially, a comprehensive dataset comprising 405 hyperspectral images capturing ten grades of folded flue-cured tobacco was meticulously collected, illustrating various degrees of folding. Subsequently, diverse preprocessing techniques were applied to the spectra obtained from the regions of interest. Moreover, advanced algorithms, including the least angle regression algorithm (LAR), successive project algorithm (SPA), and competitive adaptive reweighted sampling algorithm (CARS), were employed to select the most pertinent feature bands. Finally, based on the feature wavelengths, a 1D-CNN grading model was established and compared with random forest (RF), artificial neural network (ANN), backpropagation neural network (BPNN), and residual neural network (ResNet) classification models. Comparative analysis of these models reveals that the LAR-CNN algorithm outperforms others, achieving a classification accuracy of 96.3% and a minimum loss function value of 0.1. Notably, the LAR algorithm implemented in this study successfully reduces the number of bands used in the model from 360 to 20. In summary, this fusion of HSI and 1D-CNN not only conveys a distinct advantage in discerning leaf folds and intricate morphological traits but also heralds an innovative era for the automation of flue-cured tobacco classification, offering unprecedented precision and efficiency. This methodology paves the way for remarkable strides in augmenting the efficacy and precision of automated flue-cured tobacco grading, mitigate subjective biases in manual grading methods, and offer essential technical support for the development of automatic grading devices for folded flue-cured tobacco leaves. Hence, continued research and development initiatives stand as indispensable to realize the full potential of such innovative technologies.