Leaf diseases detection empowered with transfer learning model

Muhamamd Daniyal Baig, Hafiz Burhan Ul Haq, Muhammad Asif, Aqdas Tanvir

Article ID: 2358
Vol 2, Issue 1, 2024
DOI: https://doi.org/10.54517/cte.v2i1.2358
VIEWS - 112 (Abstract)

Abstract

The detection of leaf diseases using modern technology has significant importance in agriculture and artificial intelligence. Deep learning, specifically, plays a crucial role in this field, as it enables accurate and efficient disease classification. Early detection of leaf diseases is vital to implementing timely treatments and preventing widespread damage to leaves. Leaf diseases can be caused by various factors, including bacteria, fungi, viruses, and other pathogens. Among them, bacteria and viruses are the most invasive and can lead to substantial yield losses if not identified and treated promptly. Bacterial and viral infections are common in agricultural settings, affecting leaves of all types and ages. Our research aims to propose a transfer learning-based model for predicting leaf diseases using a dataset of leaf images. The images will be classified into healthy or diseased leaves based on extracted features. The proposed model, named Leaf Disease Transfer Learning Algorithm (LDTLA), demonstrates promising results with an average accuracy of 97.37% on the dataset. Utilizing convolutional neural networks (CNN) and deep learning techniques, our LDTLA model outperforms previous quantitative and qualitative research studies in leaf disease detection. This advanced approach to leaf disease identification holds the potential to revolutionize agriculture by enabling farmers to make informed decisions, implement targeted treatments, and minimize leaf losses caused by diseases.


Keywords

convolutional neural network (CNN); deep learning; leaf diseases; agricultural imaging; transfer learning; Leaf Disease Transfer Learning Algorithm (LDTLA)

Full Text:

PDF



References

1. Thakur PS, Sheorey T, Ojha A. VGG-ICNN: A Lightweight CNN model for crop disease identification. Multimedia Tools and Applications. 2022, 82(1): 497-520. doi: 10.1007/s11042-022-13144-z

2. Farooqui NA, Mishra AK, Mehra R. Automatic crop disease recognition by improved abnormality segmentation along with heuristic-based concatenated deep learning model. Intelligent Decision Technologies. 2022, 16(2): 407-429. doi: 10.3233/idt-210182

3. Sakkarvarthi G, Sathianesan GW, Murugan VS, et al. Detection and Classification of Tomato Crop Disease Using Convolutional Neural Network. Electronics. 2022, 11(21): 3618. doi: 10.3390/electronics11213618

4. Fenu G, Malloci FM. Forecasting Plant and Crop Disease: An Explorative Study on Current Algorithms. Big Data and Cognitive Computing. 2021, 5(1): 2. doi: 10.3390/bdcc5010002

5. Ouhami M, Hafiane A, Es-Saady Y, et al. Computer Vision, IoT and Data Fusion for Crop Disease Detection Using Machine Learning: A Survey and Ongoing Research. Remote Sensing. 2021, 13(13): 2486. doi: 10.3390/rs13132486

6. Dai G, Fan J. An Industrial-Grade Solution for Crop Disease Image Detection Tasks. Frontiers in Plant Science. 2022, 13. doi: 10.3389/fpls.2022.921057

7. Orchi H, Sadik M, Khaldoun M. On Using Artificial Intelligence and the Internet of Things for Crop Disease Detection: A Contemporary Survey. Agriculture. 2021, 12(1): 9. doi: 10.3390/agriculture12010009

8. Zhi P, Chang C. Exploiting Epigenetic Variations for Crop Disease Resistance Improvement. Frontiers in Plant Science. 2021, 12. doi: 10.3389/fpls.2021.692328

9. Yuan Y, Xu Z, Lu G. SPEDCCNN: Spatial Pyramid-Oriented Encoder-Decoder Cascade Convolution Neural Network for Crop Disease Leaf Segmentation. IEEE Access. 2021, 9: 14849-14866. doi: 10.1109/access.2021.3052769

10. Bhagwat R, Dandawate Y. A Framework for Crop Disease Detection Using Feature Fusion Method. International Journal of Engineering and Technology Innovation. 2021, 11(3): 216-228. doi: 10.46604/ijeti.2021.7346

11. Kale R, Shitole S. Analysis of crop diseases detection with SVM, KNN and random forest classification. Information Technology in Industry. 2021, 9(1): 364-372.

12. Nagasubramanian G, Sakthivel RK, Patan R, et al. Ensemble Classification and IoT-Based Pattern Recognition for Crop Disease Monitoring System. IEEE Internet of Things Journal. 2021, 8(16): 12847-12854. doi: 10.1109/jiot.2021.3072908

13. Agarwal M, Gupta SKr, Biswas KK. Development of Efficient CNN model for Tomato crop disease identification. Sustainable Computing: Informatics and Systems. 2020, 28: 100407. doi: 10.1016/j.suscom.2020.100407

14. Wen J, Shi Y, Zhou X, et al. Crop Disease Classification on Inadequate Low-Resolution Target Images. Sensors. 2020, 20(16): 4601. doi: 10.3390/s20164601

15. Sharma RK, Das SK, Gourisaria MK, et al. A Model for Prediction of Paddy Leaf Disease Using CNN. Advances in Intelligent Systems and Computing. 2020, 1119: 533-543.

16. Wang X, Wang Z, Zhang S. Segmenting leaf disease leaf image by modified fully-convolutional networks. In: Huang DS, Bevilacqua V, Premaratne P (editors). Intelligent Computing Theories and Application, Proceedings of the 15th International Conference, ICIC 2019; 3–6 August 2019; Nanchang, China. Springer; 2019. Volume 11643. pp. 646-652. doi: 10.1007/978-3-030-26763-6_62

17. Richey B, Majumder S, Shirvaikar M, Kehtarnavaz N. Real-time detection of maize crop disease via a deep learning-based smartphone app. In: Kehtarnavaz N, Carlsohn MF (editors). Real-Time Image Processing and Deep Learning 2020. SPIE; 2020. Volume 11401. doi: 10.1117/12.2557317

18. Zhang S, Huang W, Wang H. Crop disease monitoring and recognizing system by soft computing and image processing models. Multimedia Tools and Applications. 2020, 79(41-42): 30905-30916. doi: 10.1007/s11042-020-09577-z

19. Akram W, Mahmood K, Li X, et al. An energy-efficient and secure identity based RFID authentication scheme for vehicular cloud computing. Computer Networks. 2022, 217: 109335. doi: 10.1016/j.comnet.2022.109335

20. Mahmood K, Ferzund J, Saleem MA, et al. A provably secure mobile user authentication scheme for big data collection in IoT-enabled maritime intelligent transportation system. IEEE Transactions on Intelligent Transportation Systems. 2023, 24(2): 2411-2421. doi: 10.1109/TITS.2022.3177692

21. Shamshad S, Ayub MF, Mahmood K, et al. An Identity-Based Authentication Protocol for the Telecare Medical Information System (TMIS) Using a Physically Unclonable Function. IEEE Systems Journal. 2022, 16(3): 4831-4838. doi: 10.1109/jsyst.2021.3118014

22. Shamshad S, Mahmood K, Kumari S, et al. Comments on “Insider Attack Protection: Lightweight Password-Based Authentication Techniques Using ECC”. IEEE Systems Journal. 2021, 15(1): 877-880. doi: 10.1109/jsyst.2020.2986377

23. Shafiq A, Altaf I, Mahmood K, Kumari S, Chen CM. An ECC based remote user authentication protocol. Journal of Internet Technology. 2020, 21(1): 285-294.

24. Ahmed S, Shamshad S, Ghaffar Z, et al. Signcryption Based Authenticated and Key Exchange Protocol for EI-Based V2G Environment. IEEE Transactions on Smart Grid. 2021, 12(6): 5290-5298. doi: 10.1109/tsg.2021.3102156

25. Heydari M, Sajad Sadough SM, Chaudhry SA, et al. An improved one-to-many authentication scheme based on bilinear pairings with provable security for mobile pay-TV systems. Multimedia Tools and Applications. 2016, 76(12): 14225-14245. doi: 10.1007/s11042-016-3825-0

26. Hussain S, Ahmad MB, Asif M, et al. APT adversarial defence mechanism for industrial IoT enabled cyber-physical system. IEEE Access. 2023, 11: 74000-74020. doi: 10.1109/ACCESS.2023.3291599

27. Ali W, Khan A, Akram W. Analyzing the deployment and performance of Multi-CDNs in Pakistan. Pakistan Journal of Engineering and Technology. 2021, 4(1): 169-174.

28. Kayani SN, Nawaz S, Ul Haq HB, et al. SmartBin: An Approach to Smart Living Community Using IoT Techniques and Tools. Pakistan Journal of Engineering and Technology. 2022, 5(4): 44–51. doi: 10.51846/vol5iss4pp44-51.

29. Badar HMS, Mahmood K, Akram W, et al. Secure authentication protocol for home area network in smart grid-based smart cities. Computers and Electrical Engineering. 2023, 108: 108721. doi: 10.1016/j.compeleceng.2023.108721

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Muhamamd Daniyal Baig, Hafiz Burhan Ul Haq, Muhammad Asif, Aqdas Tanvir

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.