Application of machine vision and convolutional neural networks in discriminating tobacco leaf maturity on mobile devices

Abstract

As harvesting at the right time is crucial to ensuring the best quality and maximizing the yield of tobacco leaves, great attention has been paid to the research of “harvest maturity”. Fresh tobacco leaves can be manually categorized into four maturity stages, including immature, pseudomature, mature, and hypermature, according to the leaf tissue structure and color. To improve the discriminative accuracy, convenience, and automation of maturity levels, a tobacco leaf maturity discriminative method based on machine vision and deep learning was developed and integrated into a system based on the mobile terminal. In this method, the chromatic features and texture features can be automatically extracted from the acquired digital image of tobacco leaf by convolutional neural networks (CNNs). Experimental results of an independent test set consisting of 480 tobacco leaf samples demonstrated that the proposed system was able to classify the maturity stage of tobacco leaf efficiently and accurately. The system's ability to run on mobile devices, like smartphones, makes it easier to accurately collect mature tobacco leaves and reduces subjectivity compared to the current visual identification approach. Moreover, this system can provide a picking date recommendation to tobacco growers.