Abstract: Paddy leaf diseases pose a significant threat to global rice production, impacting food security and economic stability. This study explores the application of machine learning, specifically convolutional neural networks (CNNs), for the automated recognition and classification of paddy leaf diseases. The proposed CNN model analyzes leaf images to detect common diseases such as brown spot, leaf blast, and leaf blight. Leveraging advanced image processing techniques, the system achieves high accuracy in disease identification, enabling timely interventions to mitigate crop losses. Key aspects of the project include dataset preparation, model training, and performance evaluation. Through this research, we contribute to the advancement of precision agriculture and sustainable crop management practices.
Keywords: Paddy leaf diseases, Machine learning, Convolutional neural networks, Automated recognition, Crop management.
Cite:
Divyata J, Amrutha2, Harshitha, Likhitha, Pavana, "Recognition and Classification of Paddy Leaf Disease using CNN", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13374.
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DOI:
10.17148/IJARCCE.2024.13374
