【摘要】 本文针对观赏植物病虫害识别的难点问题，以北方常见的观赏植物海棠树为例，提出了一种基于改进的YOLOv8n(MCSW-YOLOv8)海棠叶片常见病害的检测方法。该方法以自然环境下的海棠叶片为研究对象，对采集的样本进行增强处理。对模型进行以下优化：将原模型的主干网络替换为MobileNetV4，采用Wise-IoU(WIoU)V3作为边界框回归的损失函数，将SPP与ELAN结合起来，提高了模型对不同尺度物体的识别能力。最后添加CA注意力机制分解水平或垂直方向的池化，保留位置信息，提升目标检测中的边界框定位精准度。实验结果表明：本文提出的MCSW-YOLOv8目标检测算法在数据集上的查准率提高了7.32%,mAP@0.5提高了7.03%,mAP@0.5:0.95提高了3.53%，取得了较为理想的检测结果。总的来说，MCSW-YOLOv8模型适用于常见海棠叶病害的小目标检测。更多还原

【Abstract】 Aiming at the difficulties in the identification of ornamental plant pests and diseases, taking crabapple trees as example, which are common ornamental species in northern China, this paper proposes a detection method for common leaf diseases based on the improved YOLOv8n(MCSW-YOLOv8). This method targets crabapple leaves in natural environments, and enhances the collected samples. The following optimizations are applied to the model: Replace the backbone network of the original model with MobileNetV4, adopt Wise-IoU(WIoU) V3 as the loss function for bounding box regression, and integrate SPP and ELAN to enhance the model’s capability to recognize objects of varying scales. Finally, incorporate CA attention mechanism to decompose horizontal and vertical pooling, retain position information, and improve the accuracy of boundary frame positioning in target detection. The results show that the MCSW-YOLOv8 object detection algorithm proposed in this paper improves the precision by 7.32%, mAP@0.5 by 7.03%, and mAP@0.5:0.95 by 3.53% on the dataset, achieving satisfactory detection performance.更多还原