Acknowledgement
This work was supported by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET) and Korea Smart Fam R&D Foundation (KosFarm) through Smart Farm Innovation Technology Development Progarm, funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA) and Ministry of Science and ICT (MSIT), Rural Development Administration (RDA) (421009043HD020).
References
- Barbedo J.G.A. 2016, A review on the main challenges in automatic plant disease identification based on visible range ima ges. Biosyst Eng 144:52-60. doi:10.1016/j.biosystemseng.2016.01.017
- Chakruno P., S. Banik, and K. Sumi 2022, Important diseases of tea (Camellia sinensis L.) and their integrated management. In Diseases of Horticultural Crops: Diagnosis and Management, vol 4. Apple Academic Press, USA, pp 119-138. doi:10.1201/9781003160472-7
- Chugh G., A. Sharma, P. Choudhary, and R. Khanna 2020, Potato leaf disease detection using InceptionV3. Int Res J Eng Technol 7:1363-1366.
- Datta S., and N. Gupta 2023, A novel approach for the detection of tea leaf disease using deep neural network. Procedia Comput Sci 218:2273-2286. doi:10.1016/j.procs.2023.01.203
- Demsar J., and B. Zupan 2012, Orange data mining fruitful and fun. Inf Druzba IS 6:1-486.
- Demsar J., T. Curk, A. Erjavec, C. Gorup, T. Hocevar, M. Milutinovic, M. Mozina, M. Polajnar, M. Toplak, and A. Staric, et al. 2013, Orange data mining toolbox in Python. J Mach Learn Res 14:2349-2353.
- Guo T., J. Dong, H. Li, and Y. Gao 2017, Simple convolutional neural network on image classification. In IEEE 2017 2nd International Conference on Big Data Analysis (ICBDA), pp 721-724. doi:10.1109/ICBDA.2017.8078730
- Hidayatuloh A., M. Nursalman, and E. Nugraha 2018, Identification of tomato plant diseases by leaf image using Squeezenet model. In 2018 International Conference on Information Technology Systems and Innovation (ICITSI), pp 199-204. doi:10.1109/ICITSI.2018.8696087
- Hu G., X. Yang, Y. Zhang, and M. Wan 2019, Identification of tea leaf diseases by using an improved deep convolutional neural network. Sustain Comput Inform Syst 24:100353. doi:10.1016/j.suscom.2019.100353
- Ishak A., K. Siregar, R. Ginting, and M. Afif 2020, Orange software usage in data mining classification method on the dataset lenses. In IOP Conference Series: Materials Science and Engineering (IOP Publishing) 1003(1):012113. doi:10.1088/1757-899X/1003/1/012113
- Jiang X., Y. Pang, X. Li, J. Pan, and Y. Xie 2018, Deep neural networks with elastic rectified linear units for object recognition. Neurocomputing 275:1132-1139. doi:10.1016/j.neucom.2017.09.056
- Kaggle Data Science Company 2017, https://www.kaggle.com/ Accessed 03 May 2023.
- Kansara D., and V. Sawant 2020, Comparison of traditional machine learning and deep learning approaches for sentiment analysis. In Advanced Computing Technologies and Applications: Proceedings of 2nd International Conference on Advanced Computing Technologies and Applications-ICACTA Springer, Singapore, pp 365-377.
- Keith L., W.H. Ko, and D.M. Sato 2006, Identification guide for diseases of tea (Camellia sinensis): Plant Disease PD-33. University of Hawaii, Honolulu, HI, USA.
- Khan E., M.Z.U. Rehman, F. Ahmed, and M.A. Khan 2021, Classification of diseases in citrus fruits using SqueezeNet. In IEEE 2021 International Conference on Applied and Engineering Mathematics (ICAEM), pp 67-72. doi:10.1109/ICAEM53552.2021.9547133
- Kimutai G., and A. Forster 2022, Tea sickness dataset. Mendeley Data V2. doi:10.17632/j32xdt2ff5.2
- Latha R.S., G.R. Sreekanth, R.C. Suganthe, R. Rajadevi, S. Karthikeyan, S. Kanivel, and B. Inbaraj 2021, Automatic detection of tea leaf diseases using deep convolution neural network. In 2021 International Conference on Computer Communication and Informatics (ICCCI), pp 1-6. doi:10.1109/ICCCI50826.2021.9402225
- Mahesh B. 2020, Machine learning algorithms-a review. Int J Sci Res 9:381-386. doi:10.21275/ART20203995
- Mikolajczyk A., and M. Grochowski 2018, Data augmentation for improving deep learning in image classification problem. In 2018 International Interdisciplinary PhD Workshop (IIPhDW), pp 117-122.
- Mirza A.H. 2018, Computer network intrusion detection using various classifiers and ensemble learning. In 2018 26th Signal Processing and Communications Applications Conference (SIU), pp 1-4. doi:10.1109/SIU.2018.8404704
- Mohapatra S., and T. Swarnkar 2021, Comparative study of different orange data mining tool-based AI techniques in image classification. In S Das, MN Mohanty, eds, Advances in Intelligent Computing and Communication: Lecture Notes in Networks and Systems, vol 202. Springer, Singapore, pp 611-620. doi:10.1007/978-981-16-0695-3_57
- Nanehkaran Y.A., D. Zhang, J. Chen, Y. Tian, and N. Al-Nabhan 2020, Recognition of plant leaf diseases based on computer vision. J Ambient Intell Human Comput pp 1-18. doi:10.1007/s12652-020-02505-x
- Neyshabur B., S. Bhojanapalli, D. McAllester, and N. Srebro 2017, Exploring generalization in deep learning. Adv Neural Inf Process Syst 30.
- Nusrat I., and S.B. Jang 2018, A comparison of regularization techniques in deep neural networks. Symmetry 10(11):648. doi:10.3390/sym10110648
- Patro V.M., and M.R. Patra 2014, Augmenting weighted average with confusion matrix to enhance classification accuracy. Trans Mach Learn Artif Intell 2(4):77-91. doi:10.14738/tmlai.24.328
- Ratra R., and P. Gulia 2020, Experimental evaluation of open source data mining tools (WEKA and Orange). Int J Eng Trends Technol 68(8):30-35. doi:10.14445/22315381/IJETTV68I8P206S
- Raut P., and A. Dani 2020, Correlation between number of hidden layers and accuracy of artificial neural network. In Advanced Computing Technologies and Applications: Proceedings of 2nd International Conference on Advanced Computing Technologies and Applications-ICACTA. Springer, Singapore, pp 513-521. doi:10.1007/978-981-15-3242-9_49
- Shafi I., J. Ahmad, S.I. Shah, and F.M. Kashif 2006, Impact of varying neurons and hidden layers in neural network architecture for a time frequency application. In 2006 International Multitopic Conference, pp 188-193. doi:10.1109/INMIC.2006.358160
- Sharma S, S. Sharma, and A. Athaiya 2020, Activation functions in neural networks. Int J Eng Appl Sci 4(12):310-316. doi:10.33564/IJEAST.2020.v04i12.054
- Shi Y., T. ValizadehAslani, J. Wang, P. Ren, Y. Zhang, M. Hu, and H. Liang 2022, Improving imbalanced learning by pre-finetuning with data augmentation. In Fourth International Workshop on Learning with Imbalanced Domains: Theory and Applications, pp 68-82.
- Shrestha A., and A. Mahmood 2019, Review of deep learning algorithms and architectures. IEEE Access 7:53040-53065. doi:10.1109/ACCESS.2019.2912200
- Shruthi U., V. Nagaveni, and B.K. Raghavendra 2019, A review on machine learning classification techniques for plant disease detection. In 5th International Conference on Advanced Computing and Communication Systems (ICACCS), pp 281-284. doi:10.1109/ICACCS.2019.8728415
- Sibi P., S.A. Jones, and P. Siddarth 2013, Analysis of different activation functions using back propagation neural networks. J Theor Appl Inf Technol 47:1264-1268
- Singh R., N. Sharma, and R. Gupta 2023, Proposed CNN model for tea leaf disease classification. In 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), pp 53-60. doi:10.1109/ICAAIC56838.2023.10140680
- Singh V., N. Sharma, and S. Singh 2020, A review of imaging techniques for plant disease detection. Artif Intell 4:229-242. doi:10.1016/j.aiia.2020.10.002
- Sladojevic S., M. Arsenovic, A. Anderla, D. Culibrk, and D. Stefanovic 2016, Deep neural networks-based recognition of plant diseases by leaf image classification. Comput Intell Neurosci 2016:1-11. doi:10.1155/2016/3289801
- Szandala T. 2021, Review and comparison of commonly used activation functions for deep neural networks. In: A Bhoi, P Mallick, CM Liu, V Balas, eds, Bio-inspired Neurocomputing. Studies in Computational Intelligence, vol 903. Springer, Singapore, pp 203-224. doi:10.1007/978-981-15-5495-7_11
- Tian Y., and Y. Zhang 2022, A comprehensive survey on regularization strategies in machine learning. Inf Fusion 80:146-166. doi:10.1016/j.inffus.2021.11.005
- Tiwari R.G., A. Misra, and N. Ujjwal 2022, Image Embedding and Classification using Pre-Trained Deep Learning Architectures. In 2022 8th International Conference on Signal Processing and Communication (ICSC), pp 125-130. doi:10.1109/ICSC56524.2022.10009560
- Tripathi M. 2021, Analysis of convolutional neural network-based image classification techniques. J Innov Image Proc 3(2):100-117. doi:10.36548/jiip.2021.2.003
- Uzair M., and N. Jamil 2020, Effects of hidden layers on the efficiency of neural networks. In 2020 23rd International Multitopic Conference (INMIC), pp 1-6. doi:10.1109/INMIC50486.2020.9318195
- Vaishnav D., and B.R. Rao 2018, Comparison of machine learning algorithms and fruit classification using orange data mining tool. In 2018 3rd International Conference on Inventive Computation Technologies (ICICT), pp 603-607. doi:10.1109/ICICT43934.2018.9034442
- Xia X., C. Xu, and B. Nan 2017, Inception-v3 for flower classification. In 2017 2nd International Conference on Image, Vision, and Computing (ICIVC), pp 783-787. doi:10.1109/ICIVC.2017.7984661