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This study presents the development of an SDG-driven Convolutional Neural Network (CNN) model for the multi-class classification of tomato leaf diseases using a combination of local and public datasets. The custom CNN model achieved an impressive accuracy of 99.8% in identifying various diseases, including Bacterial Spot and Early Blight, thereby enhancing early detection and potentially improving tomato production and food security. The research aligns with the United Nations Sustainable Development Goals

Author

Yeash

Language

EN