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Fruits Are An Essential Component Of Global Agriculture And Food Supply, Yet Accurate Identification Of Fruit Species Remains A Significant Challenge Due To Variations In Size, Shape, Color, Texture, And Environmental Conditions During Image Acquisition. Traditional Manual Classification Methods Are Time-consuming, Error-prone, And Impractical For Large-scale Applications. Recent Advancements In Computer Vision And Deep Learning Have Opened New Opportunities For Automated And Highly Accurate Fruit Species Detection. This Study Proposes An Improved Fruit Species Detection System Using Image Processing Techniques Combined With Deep Learning Models Such As Convolutional Neural Networks (CNNs). The System Utilizes Preprocessing Methods Including Noise Reduction, Image Enhancement, And Segmentation To Extract Discriminative Features. These Features Are Then Fed Into Deep Learning Architectures To Achieve Robust Classification Across Diverse Fruit Categories. Experimental Results Demonstrate That The Proposed Approach Significantly Enhances Detection Accuracy And Efficiency Compared To Conventional Machine Learning Techniques. The System Has Potential Applications In Smart Agriculture, Food Quality Control, Automated Harvesting, And Supply Chain Management, Thereby Contributing To Increased Productivity And Reduced Labor Dependency.