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Research Article

Deep Learning-Based Leaf Disease Detection in Crop Using Images for Agricultural Application

Open Access Peer Reviewed Crossref DOI

Authors

  • Sameer Rajendra Nakhale1
  • Dr. Sanjay Asutkar2

1PG Scholar, Department of Electronics & Communication Engineering, Tulsiramji Gaikwad Patil College of Engineering & Technology, Nagpur, Maharashtra, India. 2Department of Electronics & Communication Engineering, Tulsiramji Gaikwad Patil College of Engineering & Technology, Nagpur, Maharashtra, India.

Article Information

Published Online
March-April 2024
Pages
146-150
Article Type
Research Article
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References

1. Moussafir, M. et al. Design of efficient techniques for tomato leaf disease detection using genetic algorithm-based and deep neural
networks. Plant Soil 479, 251–266 (2022).
2. Mahum, R. et al. A novel framework for potato leaf disease detection using an efficient deep learning model. Hum. Ecol. Risk Assess. Int.
J. 1–24 (2022).
3. Shah, D., Trivedi, V., Sheth, V., Shah, A. & Chauhan, U. ResTS: Residual deep interpretable architecture for plant disease detection. Inf.
Process. Agric. 9, 212–223 (2022).
4. Lawal, M. O. Tomato detection based on modified YOLOv3 framework. Sci. Rep. 11, 1–11 (2021).
5. M. Zhang, J. Li, Y. Li, and R. , “Deep learning for short term voltage stability assessment of power system,” IEEE Access, vol. 99, 2021.
6. Y. Hou and S. Jia, “Deep feature mining via attention based BiLSTM-GCN for human motor imagery recognition,” Journal of LATEX
Class Files, vol. 14, no. 8, 2020.
7. Y. Hou, S. Jia, S. Zhang et al., “Deep feature mining via attention-based BiLSTM- GCN for human motor imagery recognition,” 2020,
arXiv preprint arXiv:2005.00777.
8. Zia, T. & Zahid, U. Long short-term memory recurrent neural network architectures for Urdu acoustic modeling. Int. J. Speech Technol.
22, 21–30 (2019).
9. Kocić, J.; Jovičić, N.; Drndarević, V. An End-to-End Deep Neural Network forAutonomous Driving Designed for Embedded Automotive Platforms. Sensors 2019, 19, 2064.
10. Hossain, S.; Lee, D.-J. Autonomous-Driving Vehicle Learning Environments using Unity Real-time Engine and End-to-End CNN
Approach. J. Korea Robot. Soc. 2019, 14, 122–130.
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