| 基于改进YOLOv5s的自然环境下茶叶病害识别方法 |
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| 引用本文： | 陈禹,吴雪梅,张珍,闫建伟,张富贵,喻丽华.基于改进YOLOv5s的自然环境下茶叶病害识别方法[J].农业工程学报,2023,39(24):185-194. |
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| 作者姓名： | 陈禹 吴雪梅 张珍 闫建伟 张富贵 喻丽华 |
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| 作者单位： | 贵州大学机械工程学院, 贵阳 550025 |
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| 基金项目： | 国家重点研发计划（课题）（2021YFD1100307-3）；贵州省农业产业技术体系建设专项经费；贵州省科技创新基地建设项目（黔科合中引地[2023]010） |
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| 摘 要： | 针对现有目标检测模型对自然环境下茶叶病害识别易受复杂背景干扰、早期病斑难以检测等问题，该研究提出了YOLOv5-CBM茶叶病害识别模型。YOLOv5-CBM以YOLOv5s模型为基础，在主干特征提取阶段，将一个带有Transformer的C3模块和一个CA（coordinate attention）注意力机制融入特征提取网络中，实现对病害特征的提取。其次，利用加权双向特征金字塔（BiFPN）作为网络的Neck，通过自适应调节每个尺度特征的权重，使网络在获得不同尺寸特征时更好地将其融合，提高识别的准确率。最后，在检测端新增一个小目标检测头，解决了茶叶病害初期病斑较小容易出现漏检的问题。在包含有3种常见茶叶病害的数据集上进行试验，结果表明，YOLOv5-CBM对自然环境下的初期病斑检测效果有明显提高，与原始YOLOv5s模型相比，对早期茶饼病和早期茶轮斑病识别的平均精度分别提高了1.9和0.9个百分点，对不同病害检测的平均精度均值达到了97.3%，检测速度为8ms/幅，均优于其他目标检测算法。该模型具有较高的识别准确率与较强的鲁棒性，可为茶叶病害的智能诊断提供参考。
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| 关 键 词： | 图像识别 茶叶 病害 注意力机制 目标检测 yolov5 BiFPN |
| 收稿时间： | 2023/6/1 0:00:00 |
| 修稿时间： | 2023/11/20 0:00:00 |
Method for identifying tea diseases in natural environment using improved YOLOv5s |
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| CHEN Yu,WU Xuemei,ZHANG Zhen,YAN Jianwei,ZHANG Fugui,YU Lihua.Method for identifying tea diseases in natural environment using improved YOLOv5s[J].Transactions of the Chinese Society of Agricultural Engineering,2023,39(24):185-194. |
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| Authors: | CHEN Yu WU Xuemei ZHANG Zhen YAN Jianwei ZHANG Fugui YU Lihua |
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| Institution: | School of Mechanical Engineering, Guizhou University, Guiyang 550025, China |
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| Abstract: | Tea is easily affected by diseases in the growing process, leading to the decline of tea yield and quality. The conventional visual judgement on the disease cannot fully meet the large-scale production in recent years, due to the low accuracy, time-consuming and laborious. Therefore, an accurate knowledge of tea diseases is in high demand for timely and effective prevention and control measures, in order to reduce the abuse of pesticides for the stable development of tea industry. The existing research is focused mainly on the classification and recognition of tea diseases. However, it is difficult to achieve an accurate detection of tea diseases in different periods, especially early diseases. The reason is that there is a complex background in tea images taken in the natural environment, together with the small early spots of tea diseases. In this study, the YOLOv5-CBM model was proposed to recognize tea diseases in the natural environment. Firstly, a C3 module of YOLOv5-CBM was integrated with a Transformer and a Coordinate Attention (CA) mechanism into the feature extraction network in the stage of backbone feature extraction, in order to realize the extraction of important features of diseases. Secondly, the weight of each scale feature was adjusted adaptively using the weighted bidirectional feature pyramid (BiFPN) as the Neck of the network. The network was also better integrated with the features of different sizes for better recognition accuracy. Finally, a small target detection head was added to the detection end to reduce the missing detection in the early stage of tea disease. The disease data set was collected from the tea garden of Babu Purple Tea in Tielu Village, Wangmo County, Qianxinan Prefecture, Guizhou Province. Three common diseases were contained, namely Pestalotiopsis theae, tea anthracnose and Tea blister blight. The number of samples was 6,092 after data enhancement. The training set, verification set and test set were randomly divided, according to the ratio of 8:1:1. The experiment was conducted to verify the improved network. The pytorch 1.9.1 deep learning framework was selected to evaluate the Accuracy, Precision, Recall, AP and mAP. Firstly, the weights were initialized using the YOLOv5s pre-trained model, and the data set was then trained, according to hyperparameters. Then an ablation experiment was designed to verify the effectiveness of the added modules in the improved model. Finally, three main target detection models were selected for the comparative test. The performance of the improved model detection was further verified. The results showed that the improved YOLOv5s model significantly improved the detection of early disease spots in a natural environment. The AP value of early tea cake disease and early tea wheel spot disease increased by 1.9 and 0.9 percentage points, respectively, compared with YOLOv5s. The mAP detection of different diseases reached 97.3%, with a detection speed of 8ms/ sheet. Both models were superior to other target detection models. The improved model realized the detection and classification of three kinds of tea diseases. The detection accuracy and speed can provide a strong reference for the intelligent diagnosis of tea diseases. |
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| Keywords: | image recognition tea disease attention mechanism target detection Yolov5 BiFPN |
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