CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
Implementation of Feature Selection Using Correlation Matrix in Python

Ahmad Farhan AlShammari
Random Articles
Reseach Article

Enhancing Tea Plant Health Through Machine Learning: EfficientNet-B0 for Tea Sickness Detection

by Ali Raza, Muhammad Subhan
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 45
Year of Publication: 2024
Authors: Ali Raza, Muhammad Subhan
10.5120/ijca2024924092

Ali Raza, Muhammad Subhan . Enhancing Tea Plant Health Through Machine Learning: EfficientNet-B0 for Tea Sickness Detection. International Journal of Computer Applications. 186, 45 ( Oct 2024), 32-43. DOI=10.5120/ijca2024924092

@article{ 10.5120/ijca2024924092,
author = { Ali Raza, Muhammad Subhan },
title = { Enhancing Tea Plant Health Through Machine Learning: EfficientNet-B0 for Tea Sickness Detection },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2024 },
volume = { 186 },
number = { 45 },
month = { Oct },
year = { 2024 },
issn = { 0975-8887 },
pages = { 32-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number45/enhancing-tea-plant-health-through-machine-learning-efficientnet-b0-for-tea-sickness-detection/ },
doi = { 10.5120/ijca2024924092 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-10-26T00:55:48.957339+05:30
%A Ali Raza
%A Muhammad Subhan
%T Enhancing Tea Plant Health Through Machine Learning: EfficientNet-B0 for Tea Sickness Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 45
%P 32-43
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Tea is a globally important crop that is prone to numerous diseases that can significantly affect its quality and production. This study proposes a novel work of improving the health of tea plants through Machine Learning (ML) for tea sickness detection from the EfficientNet-B0 convolutional neural network (CNN). By training the model on a comprehensive dataset of tea leaf images, significant improvements in disease detection accuracy were achieved. The architecture of the EfficientNet-B0 was well optimized explicitly for the use of this study and its test accuracy is 94.64%. This performance highlights the ability of the model as a classifier of the healthy and diseased tea leaves. EfficientNet-B0 is a promising solution to better manage and detect diseases in the initial stages, which confirmed the effectiveness when applied in this context. This stays in contrast to the traditional disease diagnosing approach in agriculture. This approach is an improvement by combining Deep Learning (DL) with real-life applications in farming.

References
  1. Kamburawala, T. U., & Abeyrathne, D. H. (2024). Competitiveness of Sri Lankan Tea in the International Market: A Trade Flow Analysis. KDU Journal of Multidisciplinary Studies, 6(1).
  2. Wang, Y., Xu, R., Bai, D., & Lin, H. (2023). Integrated learning-based pest and disease detection method for tea leaves. Forests, 14(5), 1012.
  3. Bag, S., Mondal, A., & Banik, A. (2022). Exploring tea (Camellia sinensis) microbiome: Insights into the functional characteristics and their impact on tea growth promotion. Microbiological Research, 254, 126890.
  4. Kotwal, J., Kashyap, R., & Pathan, S. (2023). Agricultural plant diseases identification: From traditional approach to deep learning. Materials Today: Proceedings, 80, 344-356.
  5. Condran, S., Bewong, M., Islam, M. Z., Maphosa, L., & Zheng, L. (2022). Machine learning in precision agriculture: a survey on trends, applications and evaluations over two decades. IEEE Access, 10, 73786-73803.
  6. Dang, M., Wang, H., Li, Y., Nguyen, T. H., Tightiz, L., Xuan-Mung, N., & Nguyen, T. N. (2024). Computer Vision for Plant Disease Recognition: A Comprehensive Review. The Botanical Review, 1-61.
  7. Attri, I., Awasthi, L. K., & Sharma, T. P. (2024). Machine learning in agriculture: a review of crop management applications. Multimedia Tools and Applications, 83(5), 12875-12915.
  8. Murugan, J., Kaliyanandi, M., & Carmel Sobia, M. (2024). Revolutionizing Precision Agriculture Using Artificial Intelligence and Machine Learning. Data Science for Agricultural Innovation and Productivity, 110.
  9. Shoaib, M., Shah, B., Ei-Sappagh, S., Ali, A., Ullah, A., Alenezi, F., ... & Ali, F. (2023). An advanced deep learning models-based plant disease detection: A review of recent research. Frontiers in Plant Science, 14, 1158933.
  10. Gupta, D. S. K., & Malik, S. (2022). Application of predictive analytics in agriculture. TTIDMKD, 2(4), 1-5.
  11. Nancy, P., Pallathadka, H., Naved, M., Kaliyaperumal, K., Arumugam, K., & Garchar, V. (2022, March). Deep Learning and Machine Learning Based Efficient Framework for Image Based Plant Disease Classification and Detection. In 2022 International Conference on Advanced Computing Technologies and Applications (ICACTA) (pp. 1-6). IEEE.
  12. Harakannanavar, S. S., Rudagi, J. M., Puranikmath, V. I., Siddiqua, A., & Pramodhini, R. (2022). Plant leaf disease detection using computer vision and machine learning algorithms. Global Transitions Proceedings, 3(1), 305-310.
  13. Khan, A. I., Quadri, S. M. K., Banday, S., & Shah, J. L. (2022). Deep diagnosis: A real-time apple leaf disease detection system based on deep learning. computers and Electronics in Agriculture, 198, 107093.
  14. Thakur, P. S., Khanna, P., Sheorey, T., & Ojha, A. (2022). Trends in vision-based machine learning techniques for plant disease identification: A systematic review. Expert Systems with Applications, 208, 118117.
  15. Tan, M. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. arXiv preprint arXiv:1905.11946.
  16. Dollár, P., Singh, M., & Girshick, R. (2021). Fast and accurate model scaling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 924-932).
  17. Papoutsis, I., Bountos, N. I., Zavras, A., Michail, D., & Tryfonopoulos, C. (2023). Benchmarking and scaling of deep learning models for land cover image classification. ISPRS Journal of Photogrammetry and Remote Sensing, 195, 250-268.
  18. Atila, Ü., Uçar, M., Akyol, K., & Uçar, E. (2021). Plant leaf disease classification using EfficientNet deep learning model. Ecological Informatics, 61, 101182.
  19. Pandian, J. A., Kumar, V. D., Geman, O., Hnatiuc, M., Arif, M., & Kanchanadevi, K. (2022). Plant disease detection using deep convolutional neural network. Applied Sciences, 12(14), 6982.
  20. Balaji, B., Murthy, T. S., & Kuchipudi, R. (2023). A comparative study on plant disease detection and classification using deep learning approaches. Int. J. Image Graph. Signal Process.(IJIGSP), 15(3), 48-59.
  21. Heng, Q., Yu, S., & Zhang, Y. (2024). A new AI-based approach for automatic identification of tea leaf disease using deep neural network based on hybrid pooling. Heliyon, 10(5).
  22. Srivastav, S., Gulcria, K., & Sharma, S. (2024, May). Revolutionizing Tea Leaf Disease Detection for Sustainable Cultivation using Convolutional Neural Network. In 2024 5th International Conference for Emerging Technology (INCET) (pp. 1-5). IEEE.
  23. Li, S., Zhang, Z., & Li, S. (2024). Identification of Tea Disease under complex backgrounds based on Minimalism neural network architecture and Channel Reconstruction Unit. IEEE Access.
  24. Kulkarni, Priyanka, and Swaroopa Shastri. "Rice leaf diseases detection using machine learning." Journal of Scientific Research and Technology (2024): 17-22.
  25. Zhang, Y., Zhou, G., Chen, A., He, M., Li, J., & Hu, Y. (2023). A precise apple leaf diseases detection using BCTNet under unconstrained environments. Computers and Electronics in Agriculture, 212, 108132.
  26. Gong, X., & Zhang, S. (2023). A high-precision detection method of apple leaf diseases using improved faster R-CNN. Agriculture, 13(2), 240.
  27. Yoseph, B. (2024). ETHIOPIAN COFFEE LEAF DISEASE DETECTION USING DEEP LEARNING (Doctoral dissertation, St. Mary's University).
  28. Lekha, J., Saraswathi, S., Suryaprabha, D., & Thomas, N. (2024, March). Tomato Leaf Disease Detection using Machine Learning Model. In Proceedings of the 1st International Conference on Artificial Intelligence, Communication, IoT, Data Engineering and Security, IACIDS 2023, 23-25 November 2023, Lavasa, Pune, India.
  29. Wajid, A. H., Saher, N., Nawaz, S. A., Arshad, M., & Nasir, M. (2024). Tomato Leaf Disease Detection and Classification Using Convolutional Neural Network and Machine Learning. Journal of Computing & Biomedical Informatics.
  30. Verma, S., Kumar, P., & Singh, J. P. (2024). A unified lightweight CNN-based model for disease detection and identification in corn, rice, and wheat. IETE Journal of Research, 70(3), 2481-2492.
  31. Ramadan, S. T. Y., Sakib, T., Al Farid, F., Islam, M. S., Abdullah, J., Bhuiyan, M. R., ... & Karim, H. A. (2024). Improving Wheat Leaf Disease Classification: Evaluating Augmentation Strategies and CNN-Based Models With Limited Dataset. IEEE Access.
  32. Khalid, M. M., & Karan, O. (2024). Deep learning for plant disease detection. International Journal of Mathematics, Statistics, and Computer Science, 2, 75-84.
  33. Goodfellow, I. (2016). Deep learning (Vol. 196). MIT press.
  34. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
  35. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. nature, 323(6088), 533-536.
  36. Kingma, D. P. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
  37. Smith, L. N. (2017, March). Cyclical learning rates for training neural networks. In 2017 IEEE winter conference on applications of computer vision (WACV) (pp. 464-472). IEEE.
  38. Ioffe, S. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167.
  39. Nitish, S. (2014). Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res., 15, 1.
  40. Nair, V., & Hinton, G. E. (2010). Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th international conference on machine learning (ICML-10) (pp. 807-814).
  41. Manning, C. (1999). Foundations of statistical natural language processing. The MIT Press.
  42. Powers, D. M. (2020). Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv preprint arXiv:2010.16061.
Index Terms

Computer Science
Information Sciences
Machine Learning (ML)
Neural Networks
Image Classification
Deep Learning (DL)
Computer Vision
Agriculture Technology

Keywords

Tea Sickness Detection EfficientNet-B0 Crop Disease Detection Convolutional Neural Networks (CNNs) Transfer Learning Plant Health Monitoring

Powered by PhDFocusTM