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Plant Diseases Significantly Affect Agricultural Productivity, Leading To Severe Economic Losses And Threats To Food Security Worldwide. Traditional Disease Identification Methods Rely Heavily On Expert Knowledge And Manual Inspection, Which Are Time-consuming, Error-prone, And Impractical For Large-scale Monitoring. With The Advancement Of Artificial Intelligence, Deep Learning Has Emerged As A Powerful Tool For Automated Plant Disease Recognition Through Image Analysis. This Study Focuses On Developing An Intelligent System For Plant Leaf Disease Recognition Using Deep Learning Algorithms, Particularly Convolutional Neural Networks (CNNs). The Proposed Model Leverages Image Preprocessing, Feature Extraction, And Classification Techniques To Accurately Identify And Categorize Different Types Of Leaf Diseases. By Training On Large-scale Plant Image Datasets, The System Demonstrates High Accuracy In Distinguishing Between Healthy And Diseased Leaves, Even In Complex Environments. Such An Approach Not Only Reduces Dependency On Manual Expertise But Also Enables Real-time And Cost-effective Disease Detection, Empowering Farmers With Early Diagnosis And Effective Crop Management Strategies. The Results Highlight The Potential Of Deep Learning-based Solutions In Precision Agriculture And Sustainable Farming Practices.