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Potato Is One Of The Most Widely Cultivated And Consumed Crops Worldwide, But Its Productivity Is Severely Threatened By Various Leaf Diseases Such As Early Blight, Late Blight, And Bacterial Infections. Timely And Accurate Detection Of These Diseases Is Essential To Reduce Yield Losses And Ensure Sustainable Agricultural Practices. Traditional Manual Identification Methods Are Often Labor-intensive, Time-consuming, And Prone To Human Error, Making Them Unsuitable For Large-scale Monitoring. Recent Advancements In Computer Vision And Deep Learning Provide Promising Solutions For Automated Plant Disease Recognition. This Study Focuses On Potato Disease Detection Using Convolutional Neural Networks (CNNs) Integrated With Image Processing Techniques. The Proposed Approach Involves Preprocessing Potato Leaf Images, Feature Extraction, And Classification Of Healthy And Diseased Leaves. CNNs Are Employed Due To Their Superior Capability In Learning Hierarchical Features Directly From Image Data Without The Need For Handcrafted Features. Experimental Results Demonstrate High Accuracy And Robustness Of The Model In Detecting And Classifying Potato Leaf Diseases. The Findings Highlight The Potential Of CNN-based Frameworks As An Effective Decision-support Tool For Precision Agriculture, Enabling Farmers To Take Timely Preventive Measures And Improve Crop Yield.