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Rice Is One Of The Most Important Staple Crops Worldwide, And Its Productivity Is Significantly Affected By Various Leaf Diseases Such As Bacterial Blight, Brown Spot, And Leaf Smut. Early And Accurate Detection Of These Diseases Is Crucial For Effective Crop Management And Yield Improvement. Traditional Manual Identification Methods Are Time-consuming, Subjective, And Prone To Error, Making Them Unsuitable For Large-scale Monitoring. Recent Advances In Deep Learning Have Provided Efficient Solutions For Automated Plant Disease Recognition. This Study Focuses On The Classification Of Rice Leaf Diseases Using Convolutional Neural Networks (CNNs) And Transfer Learning Approaches. A CNN Model Is Developed For Feature Extraction And Classification, While Pre-trained Models Such As VGG16, ResNet50, And InceptionV3 Are Fine-tuned To Improve Accuracy And Reduce Training Time. Experimental Results Demonstrate That Transfer Learning-based Models Outperform Standard CNNs By Achieving Higher Classification Accuracy, Robustness, And Generalization. The Proposed Framework Provides An Efficient And Scalable Solution For Real-time Rice Disease Detection, Thereby Supporting Precision Agriculture And Sustainable Farming Practices.