Acceso abierto

Research on automatic biomass grading and quality assessment technology for tobacco industry based on deep convolutional neural network

, , , , ,  y   
03 sept 2024

Cite
Descargar portada

Zhang, M., Chen, T. E., Gu, X., Chen, D., Wang, C., Wu, W., ... & Zhao, C. (2023). Hyperspectral remote sensing for tobacco quality estimation, yield prediction, and stress detection: A review of applications and methods. Frontiers in Plant Science, 14, 1073346. Search in Google Scholar

Li, Q., Ye, Z., Liang, H., Yu, Z., Fang, Z., Cai, G., ... & Liu, Z. (2023). Rapid Identification of Herbaceous Biomass Based on Raman Spectrum Analysis. In 3D Imaging—Multidimensional Signal Processing and Deep Learning: Images, Augmented Reality and Information Technologies, Volume 1 (pp. 213-226). Singapore: Springer Nature Singapore. Search in Google Scholar

Wei, X., Deng, C., Fang, W., Xie, C., Liu, S., Lu, M., ... & Wang, Y. (2024). Classification method for folded flue-cured tobacco based on hyperspectral imaging and conventional neural networks. Industrial Crops and Products, 212, 118279. Search in Google Scholar

Tufail, M., Iqbal, J., Tiwana, M. I., Alam, M. S., Khan, Z. A., & Khan, M. T. (2021). Identification of tobacco crop based on machine learning for a precision agricultural sprayer. IEEE access, 9, 23814-23825. Search in Google Scholar

Odabas, M. S., Şenyer, N., & Kurt, D. (2023). Determination of quality grade of tobacco leaf by image processing on correlated color temperature. Concurrency and Computation: Practice and Experience, 35(2), e7506. Search in Google Scholar

Şenyer, N., Oktaş, R., Odabas, M. S., Kurt, D., & Karaboğa, E. (2023). A Hybrid Mobile Application for Quality Grade of Tobacco (Nicotiana tabacum L.) Using Correlated Color Temperature. Türkiye Tarımsal Araştırmalar Dergisi, 10(2), 147-153. Search in Google Scholar

Wu, T., Zhang, Y., Gong, Z., & Lu, D. (2022). Quantification of Tobacco Leaf Appearance Quality Index Based on Computer Vision. IEEE Access, 10, 120352-120368. Search in Google Scholar

Huang, V., Head, A., Hyseni, L., O’Flaherty, M., Buchan, I., Capewell, S., & Kypridemos, C. (2023). Identifying best modelling practices for tobacco control policy simulations: a systematic review and a novel quality assessment framework. Tobacco control, 32(5), 589-598. Search in Google Scholar

Thimmegowda, T. G. M., & Jayaramaiah, C. (2023). Cluster-based segmentation for tobacco plant detection and classification. Bulletin of Electrical Engineering and Informatics, 12(1), 75-85. Search in Google Scholar

Sahu, A., & Dante, H. (2018, May). Non-destructive rapid quality control method for tobacco grading using visible near-infrared hyperspectral imaging. In Image Sensing Technologies: Materials, Devices, Systems, and Applications V (Vol. 10656, p. 1065603). SPIE. Search in Google Scholar

Dasari, S. K., & Prasad, V. (2019). A novel and proposed comprehensive methodology using deep convolutional neural networks for flue cured tobacco leaves classification. International Journal of Information Technology, 11, 107-117. Search in Google Scholar

Lu, M., Wang, C., Wu, W., Zhu, D., Zhou, Q., Wang, Z., ... & Chen, D. (2023). Intelligent grading of tobacco leaves using an improved bilinear convolutional neural network. IEEE Access, 11, 68153-68170. Search in Google Scholar

Jiang Tao Ji,Ming Li Deng,Zhi Tao He,Shi Tong Jia,Xin Wu Du,Ya Kai He & Jian Jun Liu. (2014). Extraction of Tobacco Leaves Color Features Based on HSI Color Space. Applied Mechanics and Materials(651-653),2424-2429. Search in Google Scholar

R V Aswiga,S Sridevi & B Indira. (2024). Leveraging Quantum Kernel Support Vector Machine for breast cancer diagnosis from Digital Breast Tomosynthesis images. Quantum Machine Intelligence(2), Search in Google Scholar

Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, 2-4-7 Aomi, Koto-ku, Tokyo, Japan. (2019). Difficulty-weighted learning: A novel curriculum-like approach based on difficult examples for neural network training. Expert Systems With Applications83-89. Search in Google Scholar

Zhenkun Li,Yifu Lan & Weiwei Lin. (2024). Footbridge damage detection using smartphone-recorded responses of micromobility and convolutional neural networks. Automation in Construction105587-105587. Search in Google Scholar