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Research Article
Deep Learning-Based Leaf Disease Detection in Crop Using Images for Agricultural Application
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.
Published Online: March-April 2024
Pages: 146-150
Cite this article
↗ https://www.doi.org/10.59256/ijire.20240502018
References
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J. 1–24 (2022).
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Process. Agric. 9, 212–223 (2022).
4. Lawal, M. O. Tomato detection based on modified YOLOv3 framework. Sci. Rep. 11, 1–11 (2021).
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Class Files, vol. 14, no. 8, 2020.
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arXiv preprint arXiv:2005.00777.
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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.
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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