1. A modified random forest approach to improve multi-class classification performance of tobacco leaf grades coupled with nir spectroscopy;Bin;RSC Adv.,2016
2. A novel and proposed comprehensive methodology using deep convolutional neural networks for flue cured tobacco leaves classification;Dasari;Int. J. Inf. Technol.,2019
3. Method for grade identification of tobacco based on machine vision;He;Trans. ASABE,2018
4. He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778. 〈10.1109/CVPR.2016.90〉.
5. Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H., 2017. Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861. 〈10.48550/arXiv.1704.04861〉.