Research on Multi-Scenario Fruit Detection and Classification Based on Improved YOLOv5

This study addresses the challenge of efficient fruit detection and accurate classification in complex environments. Based on the YOLOv5 framework, a training set was constructed by combining publicly available and self-collected datasets, focusing on three types of fruits: bananas, apples, and oranges. The study incorporated data augmentation, transfer learning, the CBAM (Convolutional Block Attention Module) attention mechanism, and an improved Non-Maximum Suppression (NMS) algorithm. Experimental results demonstrated an average precision of 96.5% and an inference speed of 39.26 frames per second under complex scenarios. Coupled with a user-friendly interface design, this system provides an effective real-time detection and statistical solution for agriculture, retail, and food processing industries.