ENHANCED DETECTION OF BACTIRIAL DISEASES IN RICE PLANTS USING PRE-TRAINED LEARNING MODELS

Authors

  • RISHI YADAV,DR. RAVI SINGH PIPPAL

Keywords:

Rice Plant Diseases, Image Processing, Bacterial, Viral, Fungal, Deep Learning.

Abstract

Rice is a fundamental crop in India, holding the largest area under cultivation including both brown and white varieties. It plays a crucial role in the nation's economy by providing employment and contributing significantly to the Gross Domestic Product (GDP). With advancements in technology, particularly in the era of machine learning (ML), there has been a shift towards automating the process of detecting diseases in rice plants using image-based analysis.

References

Chapaneri, Radhika, Maithili Desai, Anmolika Goyal, Shreya Ghose, and Sheona Das. "Plant disease detection: A comprehensive survey." In 2020 3rd International Conference on Communication System, Computing and IT Applications (CSCITA), pp. 220-225. IEEE, 2020.

Malathi, V., and M. P. Gopinath. "A review on rice crop disease classification using computational approach." International Journal of Image and Graphics 23, no. 03 (2023): 2240007.

Julie, J., J. Joshan Athanesious, and S. Adharsh. "Novel Disease detection for paddy crop using CNN with Transfer Learning." In 2021 4th International Conference on Computing and Communications Technologies (ICCCT), pp. 252 255. IEEE, 2021.

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Published

2024-01-20

How to Cite

RISHI YADAV,DR. RAVI SINGH PIPPAL. (2024). ENHANCED DETECTION OF BACTIRIAL DISEASES IN RICE PLANTS USING PRE-TRAINED LEARNING MODELS . Journal of Computational Analysis and Applications (JoCAAA), 33(1A), 503–511. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/1933

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