Authors

Abstract

This research is dedicated to enhancing the accuracy and processing speed of grape disease recognition. As a result, a real-time grape disease detection model named MSCI-YOLOv8s, based on an improved YOLOv8s framework is proposed. The primary innovation of this model lies in replacing the backbone network of the original YOLOv8s with the more efficient MobileNetV3. This alteration not only strengthens the ability of the model to capture features of various disease manifestations in grape leaf images but also improves its generalization capabilities and stability. Additionally, the model incorporates the SPPFCSPC pyramid pooling structure, which maintains the stability of the receptive field while significantly enhancing processing speed. The integration of the CBAM attention mechanism further accentuates the ability of the model to identify key features, substantially increasing the accuracy of disease detection. Moreover, the model employs Inner-SIoU as the loss function, optimizing the precision of bounding box regression and accelerating model convergence, thereby further enhancing detection efficiency. Rigorous testing has shown that the MSCI-YOLOv8s model achieves an impressive average precision (mAP) of 97.7%, with an inference time of just 37.2 milliseconds and a memory footprint of 39.3 MB. These advancements render the MSCI-YOLOv8s not only highly efficient but also extremely practical for real-time grape disease detection, meeting the actual demands of grape orchard disease identification and demonstrating significant potential for application.

Abstract in Chinese

本研究致力于提升葡萄病害识别的准确性与处理速度，因此提出了一款基于改进的YOLOv8s模型的实时葡萄病害检测模型，命名为MSCI-YOLOv8s。该模型的核心创新在于将原YOLOv8s的主干网络替换为更高效的MobileNetV3，此举不仅增强了模型捕捉葡萄叶片图像中不同尺度病害特征的能力，同时也提高了其泛化性与稳定性。此外，模型整合了SPPFCSPC金字塔池化结构，不仅保持了感受野的稳定性，还实现了处理速度的显著提升。引入的CBAM注意力机制进一步加强了模型对关键特征的识别，显著提高了病害检测的准确度。模型还采用了Inner-SIoU作为损失函数，以优化边界框回归的精度并加快模型收敛，从而进一步提升了检测效率。经过严格测试，MSCI-YOLOv8s模型实现了97.7%的平均精度（mAP），推理时间仅需37.2毫秒，内存占用为39.3MB，表现卓越。这些改进使得MSCI-YOLOv8s在实时葡萄病害检测方面不仅效率高，且具备强大的实用性，完全符合葡萄果园病害识别的实际需求，展现了巨大的应用前景。