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Ujjwal Sonawane, Vishnu Kant Pandey, Mukesh Singhaniya, T.V. Kafare
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- Student, Student, Student, Professor Department of Electronics and Telecommunication Engineering, Sinhgad College of Engineering, Pune, Department of Electronics and Telecommunication Engineering, Sinhgad College of Engineering, Pune, Department of Electronics and Telecommunication Engineering, Sinhgad College of Engineering, Pune, Department of Electronics and Telecommunication Engineering, Sinhgad College of Engineering, Pune Maharashtra, Maharashtra, Maharashtra, Maharashtra India, India, India, India
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Abstract
nIn addition to providing high-quality food, the agriculture industry plays a critical role in supporting expanding people and economies. Plant diseases can have a detrimental effect on biodiversity and result in significant losses in food production. Automated methods for early and precise identification of plant diseases can reduce financial losses and enhance the quality of food produced. Deep learning has significantly improved object detection and picture classification accuracy in recent years. Thus, for effective plant disease identification, we used pre-trained convolutional neural network (CNN) models in this paper. An important aspect of the Indian economy is smart farming. However, structural changes to India’s agriculture are already occurring, creating a catastrophe. Encouraging farmers to persist in crop production and turning agriculture into a viable enterprise are the only ways to address this dilemma. Our suggested methodology focuses on cultivation by employing machine learning to identify crop illnesses based on historical data and forecasting suitable crops based on climatic conditions.
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Keywords: CNN, Dataset, Kaggle, API, deep learning, agriculture
n[if 424 equals=”Regular Issue”][This article belongs to Recent Trends in Sensor Research & Technology(rtsrt)]
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References
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- Altieri, M.A. Agroecology: The Science of Sustainable Agriculture; CRC Press: Boca Raton, FL, USA, 2018.
- Gebbers, R.; Adamchuk, V.I. Precision agriculture and food security. Science 2010, 327, 828–831.
- Carvalho, F.P. Agriculture, pesticides, food security and food safety. Environ. Sci. Policy 2006, 9, 685 692.
- Mohanty, S.P.; Hughes, D.P.; Salathé, M. Using deep learning for image-based plant disease detection. Front. Plant Sci. 2016, 7, 1419.
- Miller, S.A.; Beed, F.D.; Harmon, C.L. Plant disease diagnostic capabilities and networks. Annu. Rev. Phytopathol. 2009, 47, 15–38.
- LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature 2015, 521, 436–444.
- Najafabadi, M.M.; Villanustre, F.; Khoshgoftaar, T.M.; Seliya, N.; Wald, R.; Muharemagic, E. Deep learning applications and challenges in big data analytics. J. Big Data 2015, 2, 1–21.
- Szegedy, C.; Liu, W.; Jia, Y.; Sermanet, P.; Reed, S.; Anguelov, D.; Erhan, D.; Vanhoucke, V.; Rabinovich, A. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 7–12 June 2015; pp. 1–9.
- Abade, A.; Ferreira, P.A.; de Barros Vidal, F. Plant diseases recognition on images using convolutional neural networks: A systematic review. Comput. Electron. Agric. 2021, 185, 106125.
- Dhaka, V.S.; Meena, S.V.; Rani, G.; Sinwar, D.; Ijaz, M.F.; Woźniak, M. A survey of deep convolutional neural networks applied for prediction of plant leaf diseases. Sensors 2021, 21, 4749.
- Kamilaris, A.; Prenafeta-Boldú, F.X. Disaster monitoring using unmanned aerial vehicles and deep learning. arXiv 2018, arXiv:1807.11805.
- Lu, J.; Tan, L.; Jiang, H. Review on convolutional neural network (CNN) applied to plant leaf disease classification. Agriculture 2021, 11, 707
- Bangari, S.; Rachana, P.; Gupta, N.; Sudi, P.S.; Baniya, K.K. A Survey on Disease Detection of a potato Leaf Using CNN. In Proceedings of the 2nd IEEE International Conference on Artificial Intelligence and Smart Energy (ICAIS), Coimbatore, India, 23–25 February 2022; pp. 144–149.
- Fernández-Quintanilla, C.; Peña, J.; Andújar, D.; Dorado, J.; Ribeiro, A.; López-Granados, F. Is the current state of the art of weed monitoring suitable for site-specific weed management in arable crops? Weed Res. 2018, 58, 259–272.
- Sethy PK, Behera SK, Kannan N, Narayanan S, Pandey C. Smart paddy field monitoring system using deep learning and IoT. Concurrent Engineering. 2021 Mar;29(1):16-24.
- Ferentinos KP. Deep learning models for plant disease detection and diagnosis. Computers and electronics in agriculture. 2018 Feb 1;145:311-8.
- Brahimi M, Arsenovic M, Laraba S, Sladojevic S, Boukhalfa K, Moussaoui A. Deep learning for plant diseases: detection and saliency map visualisation. Human and machine learning: Visible, explainable, trustworthy and transparent. 2018:93-117.
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| Volume | 11 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 01 | |
| Received | April 25, 2024 | |
| Accepted | May 10, 2024 | |
| Published | May 25, 2024 |
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