All Issue

2024 Vol.33, Issue 1

Original Articles

31 January 2024. pp. 1-11
Barbedo J.G.A. 2016, A review on the main challenges in automatic plant disease identification based on visible range images. Biosyst Eng 144:52-60. doi:10.1016/j.biosystemseng.2016.01.017 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 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 10.1016/j.procs.2023.01.203
Demšar J., and B. Zupan 2012, Orange data mining fruitful and fun. Inf Družba IS 6:1-486.
Demšar 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 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 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 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 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 10.1016/j.neucom.2017.09.056
Kaggle Data Science Company 2017, 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. 10.1007/978-981-15-3242-9_35
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 10.1109/ICAEM53552.2021.9547133
Kimutai G., and A. Förster 2022, Tea sickness dataset. Mendeley Data V2. doi:10.17632/j32xdt2ff5.2 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 10.1109/ICCCI50826.2021.9402225
Mahesh B. 2020, Machine learning algorithms-a review. Int J Sci Res 9:381-386. doi:10.21275/ART20203995 10.21275/ART20203995
Mikołajczyk 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. 10.1109/IIPHDW.2018.8388338
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 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 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 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 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 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/IJETT-V68I8P206S 10.14445/22315381/IJETT-V68I8P206S
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 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 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 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 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 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 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 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 10.1155/2016/3289801
Szandała 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 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 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 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 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 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 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 10.1109/ICIVC.2017.7984661
  • Publisher :The Korean Society for Bio-Environment Control
  • Publisher(Ko) :(사)한국생물환경조절학회
  • Journal Title :Journal of Bio-Environment Control
  • Journal Title(Ko) :생물환경조절학회지
  • Volume : 33
  • No :1
  • Pages :1-11
  • Received Date : 2023-09-15
  • Revised Date : 2023-10-30
  • Accepted Date : 2023-11-20