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Apple Production Plays A Vital Role In Global Horticulture, But Its Yield And Quality Are Often Threatened By Various Leaf Diseases Such As Apple Scab, Powdery Mildew, And Rust. Early And Accurate Detection Of These Diseases Is Crucial For Effective Management And Prevention. Traditional Manual Inspection Methods Are Time-consuming, Labor-intensive, And Prone To Human Error, While Conventional Machine Learning Techniques Often Lack Robustness And Generalization Ability In Complex Field Environments. To Address These Challenges, This Study Proposes An Apple Leaf Disease Detection Algorithm Based On An Improved YOLOv8 Model. The Enhanced Framework Integrates Optimized Feature Extraction Modules, Multi-scale Attention Mechanisms, And Lightweight Convolutional Operations To Improve Detection Accuracy While Reducing Computational Complexity. The Proposed Model Is Trained And Validated On A Large Dataset Of Apple Leaf Images Under Varying Conditions Of Illumination, Occlusion, And Background Interference. Experimental Results Demonstrate That The Improved YOLOv8 Achieves Higher Precision, Recall, And Mean Average Precision (mAP) Compared To The Baseline YOLOv8 And Other State-of-the-art Object Detection Models. This Approach Provides An Efficient And Reliable Solution For Real-time Apple Leaf Disease Detection, Offering Significant Potential For Precision Agriculture And Intelligent Crop Management Systems.