基于改进YOLOv8的芒果成熟度检测方法OA

To enhance the recognition accuracy of mangoes at different maturity stages and ensure high-quality harvesting,this study proposed a mango maturity detection method based on the YOLOv8—GAEW model.The method addressed common challenges in mango mechanical harvesting,such as small target sizes,dense fruit clustering,and severe occlusions in natural environments.Built on the YOLOv8 framework,the proposed approach incorporated a GhostNet lightweight backbone to reduce computational complexity and enable model lightweight processing.An AIFI scale feature interaction mechanism was introduced to improve the extraction and fusion of mango feature information,thereby enhancing detection accuracy.Additionally,the ECA attention mechanism was incorporated to effectively capture inter-channel dependencies and strengthen feature representation.The WIoUv3 loss function was adopted to prioritize medium-quality anchor boxes,further improving localization performance.Experimental results indicated that,compared with the original YOLOv8 model,the proposed YOLOv8—GAEW model reduced the number of parameters and floating-point operations by 22.26%and 34.15%,respectively.Meanwhile,detection accuracy of the YOLOv8—GAEW model increased from 97.45%to 99.23%.