Intelligent Grading of Tobacco Leaves Using an Improved Bilinear Convolutional Neural Network.

At present, tobacco leaf grading relies mainly on manual classification, which is highly intensive with respect to labor, materials, and cost; in addition, the performance of manual grading is poor. The realization of automatic grading is an urgent requirement in the tobacco industry. To address this need, we developed assembly line equipment and an RGB image classification method for tobacco grading. There is little difference in appearance between different grades of tobacco leaves, but there are some differences in high-level semantic features; therefore, tobacco grading is fundamentally a fine-grained visual categorization task. It is difficult to classify tobacco using hand-crafted image features. Therefore, in our method, we use a pyramid structure, attention mechanism, and angle decision loss function to improve the bilinear convolutional neural network (a fine-grained visual categorization framework) for tobacco grading. The feature extraction and classification model was trained using 66,966 images, to effectively extract high-level semantic features from a global tobacco image and multi-scale features from a local tobacco image. In an online test on assembly line equipment, the proposed model was able to classify six main grades of tobacco with an accuracy of 80.65%, which is higher than that of the state-of-the-art model. Additionally, the time required to classify each tobacco leaf was 42.1 ms. This work is of great significance for the industrial application of tobacco grading models and grading equipment, and it provides a theoretical reference for the quality grading of other agricultural products.