Identification of CNN hyper-parameters for tobacco leaf quality classification on Nvidia Jetson Nano
DOI:
https://doi.org/10.15587/1729-4061.2023.289017Keywords:
fast tobacco leaves classification, Convolutional Neural Network, Nvidia Jetson NanoAbstract
At the moment, there are some inaccuracies in manual classification for tobacco leaf quality selection, which are influenced by some factors, such as human fatigue or poor lighting. These cases lead to the need for another method that is more consistent, faster and reliable.
This research is an implementation of CNN (Convolutional Neural Network) in the classification of fresh tobacco leaves in terms of maturity grades. The primary objective is to develop an efficient CNN model capable to automize the classification of tobacco leaves into three maturity criteria: immature, mature, and old.
This methodology consists of some key factors, including color thresholding strategies to purge the noise from the background, Basic Image Manipulation approaches, the systemized screening of different input sizes, and CNN models to enhance the results.
The result of this research proves that the developed CNN model has 97.9 % accuracy achieved following 200 training sessions. The model is trained on a dataset comprising 1,249 fresh leaf photos, with a balanced 80:10:10 for train, validation and test ratio. However, the study emphasizes that the CNN model has successfully supported the tobacco leaf discrimination on a Jetson Nano Single-Board Computer with a Graphic Processing Unit (GPU).
The study extends beyond the mere theoretical contribution to practical applications in sorting “Gagang Rejeb Sidi” tobacco leaf, the highest quality tobacco variety in South Malang, East Java, Indonesia. Classification using a webcam as an input device shows the fastest processing time of 203.17 ms and the maximum is 1,363 ms.
This CNN model algorithm will be applied to a tobacco leaf selector machine, which has a high-speed conveyor and a three-position selector arm. The machine will be operated close to the field in post-harvest time under uniform lighting conditions.
Overall, the result of this research is highly relevant in terms of the short duration and accuracy for understanding the commodity classification. It provides a new angle toward speeding up the classification process and improving Indonesian tobacco quality
Supporting Agency
- State Polytechnic of Malang
References
- Susanti, A., Waryanto, B. (Eds.) (2018). Agricultural Statistic 2018. Ministry of Agriculture Republic of Indonesia. Center for Agriculture Data and Information System.
- Sari, Y., Pramunendar, R. A. (2017). Classification Quality of Tobacco Leaves as Cigarette Raw Material Based on Artificial Neural Networks. International Journal of Computer Trends and Technology, 50 (3), 147–150. doi: https://doi.org/10.14445/22312803/ijctt-v50p126
- McMurtrey, J. E. (2023). tobacco. Encyclopedia Britannica. Available at: https://www.britannica.com/plant/common-tobacco
- Song, A.-P., Hu, Q., Ding, X.-H., Di, X.-Y., Song, Z.-H. (2020). Similar Face Recognition Using the IE-CNN Model. IEEE Access, 8, 45244–45253. doi: https://doi.org/10.1109/access.2020.2978938
- Harivanto, Sudiro, S. A., Kusuma, T. M., Madenda, S., Rere, L. M. R. (2020). Detection of Fingerprint Authenticity Based on Deep Learning Using Image Pixel Value. 2020 Fifth International Conference on Informatics and Computing (ICIC). doi: https://doi.org/10.1109/icic50835.2020.9288589
- Suardi, C., Handayani, A. N., Asmara, R. A., Wibawa, A. P., Hayati, L. N., Azis, H. (2021). Design of Sign Language Recognition Using E-CNN. 2021 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT). doi: https://doi.org/10.1109/eiconcit50028.2021.9431877
- Liu, J., Shen, J., Shen, Z., Liu, R. (2012). Grading tobacco leaves based on image processing and generalized regression neural network. 2012 IEEE International Conference on Intelligent Control, Automatic Detection and High-End Equipment. doi: https://doi.org/10.1109/icade.2012.6330105
- Mustaffa, I. B., Khairul, S. F. B. M. (2017). Identification of fruit size and maturity through fruit images using OpenCV-Python and Rasberry Pi. 2017 International Conference on Robotics, Automation and Sciences (ICORAS). doi: https://doi.org/10.1109/icoras.2017.8308068
- Luo, H., Zhang, C. (2018). Features Representation for Flue-cured Tobacco Grading Based on Transfer Learning to Hard Sample. 2018 14th IEEE International Conference on Signal Processing (ICSP). doi: https://doi.org/10.1109/icsp.2018.8652385
- Suzen, A. A., Duman, B., Sen, B. (2020). Benchmark Analysis of Jetson TX2, Jetson Nano and Raspberry PI using Deep-CNN. 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). doi: https://doi.org/10.1109/hora49412.2020.9152915
- Valueva, M. V., Nagornov, N. N., Lyakhov, P. A., Valuev, G. V., Chervyakov, N. I. (2020). Application of the residue number system to reduce hardware costs of the convolutional neural network implementation. Mathematics and Computers in Simulation, 177, 232–243. doi: https://doi.org/10.1016/j.matcom.2020.04.031
- Pandian, J., Kumar, V., Geman, O., Hnatiuc, M., Arif, M., Kanchanadevi, K. (2022). Plant Disease Detection Using Deep Convolutional Neural Network. Applied Sciences, 12 (14), 6982. doi: https://doi.org/10.3390/app12146982
- Lu, M., Wang, C., Wu, W., Zhu, D., Zhou, Q., Wang, Z. et al. (2023). Intelligent Grading of Tobacco Leaves Using an Improved Bilinear Convolutional Neural Network. IEEE Access, 11, 68153–68170. doi: https://doi.org/10.1109/access.2023.3292340
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Copyright (c) 2023 Budhy Setiawan, Indrazno Siradjuddin, Arwin Datumaya Wahyudi Sumari, Widjanarko, Eka Mandyatma, David Fydo Putradi
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