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基于改进YOLOv8n的轻量化番茄叶片小目标病害识别方法

Identifying the small target disease of tomato leaves using lightweight improved YOLOv8n

  • 摘要: 针对番茄叶片小目标病害识别中存在识别速度慢、识别率低及参数量大的问题,提出一种改进YOLOv8n的轻量化番茄叶片小目标病害识别模型。首先,在Backbone(主干网络)中引入基于动态卷积的轻量级C2f_GDC(C2f_ghost dynamic convolution)模块替换C2f(CSP bottleneck with 2 convolutions)模块,保持模型性能的同时以减少计算量和参数量;其次,在Neck(颈部网络)中添加三分支注意力机制(triplet attention),有效地提取特征信息以提高对小目标识别的准确性;最后,引入Focaler-SIoU Loss取代原有的CIoU Loss(complete intersection over union loss),以提升模型的稳定性和收敛速度。试验结果表明,改进YOLOv8n模型的mAP0.5为91.5%,其中番茄花叶病毒mAP0.5达到99.5%。与原始YOLOv8n相比,精确率、召回率、mAP0.5、mAP0.5-0.95、F1分数分别提升了2.6、2.8、3.0、2.9、2.7个百分点,推理时间缩短了31.6%,模型尺寸降低了12.9%,参数量减少了13.3%;与Faster R-CNN、RT-DETR、YOLOv5s、YOLOv6、YOLOv7和YOLOv11模型相比,改进YOLOv8n模型的mAP0.5分别提升了32.3、3.6、2.7、3.9、4.4、2.2个百分点。在真实环境下,改进YOLOv8n模型的推理时间为118.8 ms,识别准确率可达91.0%。改进YOLOv8n模型可以准确地实现对番茄叶片小目标病害的识别,且模型参数量小、识别速度快,为番茄叶片病害识别提供了模型参考。

     

    Abstract: Various diseases have often posed a serious threat on the tomato growth. The yield and quality of fruits can also be reduced, even leading to the large-scale yield reduction and economic loss. In this study, an improved YOLOv8n model was proposed to facilitate the early identification and prevention of the tomato diseases. The recognition speed and accuracy were also improved to reduce the excessive size of the model parameter. Eight prevalent diseases of the tomato leaf were selected to differentiate from the healthy leaves. Firstly, a lightweight C2f_GDC (C2f_Ghost dynamic convolution) module was designed and then implemented within the backbone network. The dynamic convolution was utilized to adjust the operations. Aiming to reduce the computational load and parameters while maintaining the model performance.Secondly, a triplet attention mechanism was seamlessly integrated into the neck of the network. The capacity of the model was further enhanced to capture the fine details for the high accuracy of recognition. The high performance was achieved to capture the interaction of the input tensors between spatial attention and channel dimensions. The feature information was effectively extracted without dimensionality reduction. Thirdly, the original CIoU Loss function was replaced with the Focaler-SIoU Loss, in order to optimize the model training. As such, the model was performed better to recognize the small targets. The Focaler-SIoU Loss function was characterized by the difficult-to-classify samples. The challenging instances were learnt more effectively for the better overall performance. The experimental results demonstrated that the effectiveness of the improved YOLOv8n model was achieved in the 91.5% mean average precision at a 0.5 threshold (mAP0.5) and a 79.5% mean average precision across 0.5-0.95 thresholds (mAP0.5-0.95), among which the mAP0.5 for the Tomato Mosaic Virus reached 99.5%. Specifically, the accuracy, recall rate, mAP0.5, mAP0.5-0.95, and F1 score increased by 2.6, 2.8, 3.0, 2.9, and 2.7 percentage points, respectively, compared with the original YOLOv8n model. Moreover, the detection time, the model size and the number of parameters were reduced by 31.6%, 12.9% and 13.3%, respectively. The improved YOLOv8n model was compared with six mainstream models, including Faster R-CNN (Region-based Convolutional Neural Network), RT-DETR (Real-Time Detection Transformer), YOLOv5s, YOLOv6, YOLOv7 and YOLOv11 models. The mAP0.5 of the improved model increased by 32.3, 3.6, 2.7, 3.9, 4.4, and 2.2 percentage points, respectively. The real environment tests show that the improved model shared a recognition accuracy of 91.0%. An inference time of 118.8 ms was 17.8% lower than the original YOLOv8n model. In summary, the improved YOLOv8n model was significantly enhanced the key features for the better recognition of subtle disease spots on the tomato leaves. Thereby the accuracy and detection precision were remarkably improved for the small targets. Its lightweight framework was effectively reduced the number of parameters and computational costs, highly applicable to the real-time scenarios, such as the automatic detection of agricultural diseases. The visualization test was also validated its effectiveness. Thus, the finding can also provide a valuable reference for the future research on the disease recognition of the tomato leaves.

     

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