BGWL-YOLO: A Lightweight and Efficient Object Detection Model for Apple Maturity Classification Based on the YOLOv11n Improvement

China is the world’s leading producer of apples. However, the current classification of apple maturity is predominantly reliant on manual expertise, a process that is both inefficient and costly. In this study, we utilize a diverse array of apples of varying ripeness levels as the research subjects. We propose a lightweight target detection model, termed BGWL-YOLO, which is based on YOLOv11n and incorporates the following specific improvements. To enhance the model’s ability for multi-scale feature fusion, a bidirectional weighted feature pyramid network (BiFPN) is introduced in the neck. In response to the problem of redundant computation in convolutional neural networks, a GhostConv is used to replace the standard convolution. The Wise-Inner-MPDIoU (WIMIoU) loss function is introduced to improve the localization accuracy of the model. Finally, the LAMP pruning algorithm is utilized to further compress the model size. The experimental results demonstrate that the BGWL-YOLO model attains a detection and recognition precision rate of 83.5%, a recall rate of 81.7%, and an average precision mean of 90.1% on the test set. A comparative analysis reveals that the number of parameters has been reduced by 65.3%, the computational demands have been decreased by 57.1%, the frames per second (FPS) have been boosted by 5.8% on the GPU and 32.8% on the CPU, and most notably, the model size has been reduced by 74.8%. This substantial reduction in size is highly advantageous for deployment on compact smart devices, thereby facilitating the advancement of smart agriculture.