Discriminator network parameters.

Accurate diagnosis of apple diseases is vital for tree health, yield improvement, and minimizing economic losses. This study introduces a deep learning-based model to tackle issues like limited datasets, small sample sizes, and low recognition accuracy in detecting apple leaf diseases. The approach begins with enhancing the CycleGAN-M network using a multi-scale attention mechanism to generate synthetic samples, improving model robustness and generalization by mitigating imbalances in disease-type representation. Next, an improved YOLOv8s-KEF model is introduced to overcome limitations in feature extraction, particularly for small lesions and complex textures in natural environments. The model’s backbone replaces the standard C2f structure with C2f-KanConv, significantly enhancing disease recognition capabilities. Additionally, we optimize the detection head with Efficient Multi-Scale Convolution (EMS-Conv), improving the model’s ability to detect small targets while maintaining robustness and generalization across diverse disease types and conditions. Incorporating Focal-EIoU further reduces missed and false detections, enhancing overall accuracy. The experiment results demonstrate that the YOLOv8s-KEF model achieves 95.0% in accuracy, 93.1% in recall, 95.8% in precision, and an F1-score of 94.5%. Compared to the original YOLOv8s model, the proposed model improves accuracy by 7.2%, precision by 6.5%, and F1-score by 5.0%, with only a modest 6MB increase in model size. Furthermore, compared to Faster RCNN, ResNet50, SSD, YOLOv3-tiny, YOLOv6, YOLOv9s, and YOLOv10m, our model demonstrates substantial improvements, with up to 30.2% higher precision and 18.0% greater accuracy. This study used CycleGAN-M and YOLOv8s-KEF methods to enhance the detection capability of apple leaf diseases.

精准诊断苹果病害，对于保障果树健康、提升果实产量以及降低经济损失至关重要。本研究针对苹果叶片病害检测中存在的数据集规模有限、样本量不足以及识别精度偏低等痛点，提出了一种基于深度学习的检测模型。该方案首先通过多尺度注意力机制对CycleGAN-M网络进行改进，生成合成样本，通过缓解病害类别分布不均衡问题，提升模型的鲁棒性与泛化能力。随后，本研究提出改进版YOLOv8s-KEF模型，以克服特征提取环节的局限性，尤其是针对自然环境下的微小病斑与复杂纹理场景的识别缺陷。该模型的主干网络将标准C2f结构替换为C2f-KanConv，显著增强了病害识别能力。此外，本研究通过高效多尺度卷积（Efficient Multi-Scale Convolution，EMS-Conv）对检测头进行优化，在提升模型微小目标检测能力的同时，保障其在多种病害类型与场景下的鲁棒性与泛化能力。引入Focal-EIoU损失函数可进一步减少漏检与误检情况，提升整体检测精度。实验结果表明，YOLOv8s-KEF模型的准确率可达95.0%、召回率为93.1%、精确率为95.8%，F1值为94.5%。相较于原始YOLOv8s模型，本研究提出的模型准确率提升7.2%、精确率提升6.5%、F1值提升5.0%，仅带来6MB的模型体积小幅增长。此外，相较于Faster RCNN、ResNet50、SSD、YOLOv3-tiny、YOLOv6、YOLOv9s以及YOLOv10m等模型，本模型实现了显著性能提升，精确率最高提升30.2%，准确率最高提升18.0%。本研究通过结合CycleGAN-M与YOLOv8s-KEF方法，强化了苹果叶片病害的检测能力。