Intelligent recognition of tobacco leaves states during curing with deep neural network.

Introduction: The state monitoring of tobacco leaves during the curing process is crucial for process control and automation of tobacco agricultural production. While most of the existing research on tobacco leaves state recognition focused on the temporal state of the leaves, the morphological state was often neglected. Moreover, the previous research typically used a limited number of non-industrial images for training, creating a significant disparity with the images encountered in actual applications. Methods: To investigate the potential of deep learning algorithms in identifying the morphological states of tobacco leaves in real industrial scenarios, a comprehensive and large-scale dataset was developed in this study. This dataset focused on the states of tobacco leaves in actual bulk curing barn in multiple production areas in China, specifically recognizing the degrees of yellowing, browning, and drying. Then, an efficient deep learning method was proposed based on this dataset to enhance the predictive performance. Results: The prediction accuracy achieved for the yellowing degree, browning degree, and drying degree were 83.0%, 90.5%, and 75.6% respectively. The overall average accuracy, satisfied the requirements of practical application scenarios with a value of 83%. Discussion: Our proposed framework effectively enables morphological state recognition in industrial curing, supporting parameter optimization and enhanced tobacco quality.