基于改进Yolov5植物病害检测算法研究

苹果和番茄是日常生活非常常见的果蔬，准确地识别病害能够提升作物产量，减少经济损失。针对现有的植物病害检测方法不能准确且快速地检测植物叶片中病害区域的问题，设计一种基于改进Yolov5的深度学习方法，用于检测苹果、番茄叶片常见病害。通过数据增强和图像标注技术构建苹果、番茄叶片病害数据集，利用K means算法对初始锚框进行调整，在此基础上使用复合主干网增强Yolov5主干网对病害特征的提取能力，使用Varifocal Loss函数提高对密集感染区域的识别精度。试验结果表明：改进后的Yolov5病害检测算法mAP达到95.7%，在原来Yolov5模型基础上mAP提升1.7%，平均检测一张图像耗时0.033 s，为苹果、番茄叶片病害检测提供一种高性能的解决方案，能够以较高的准确率对植物叶片病害进行分类与定位。

Research on plant disease detection algorithm based on improved Yolov5

Apples and tomatoes are very common fruits and vegetables in our daily life. Accurate identification of diseases can improve crop yields and reduce economic losses. Aiming at the problem that existing plant disease detection methods cannot accurately and quickly detect diseased areas in plant leaves, a deep learning method based on improved Yolov5 is designed to detect common diseases of apple and tomato leaves. The data set of apple and tomato leaf disease was constructed by data enhancement and image annotation technology, and the k means algorithm was used to adjust the initial anchor frame. On this basis, the composite backbone network was used to enhance the disease feature extraction ability of Yolov5 backbone network, and the Varifocal Loss function was used to improve the identification accuracy of densely infected areas. The test results show that the mAP of the improved Yolov5 disease detection algorithm reaches 95.7%, and the mAP is increased by 1.7% on the basis of the original Yolov5 model. The average detection time of an image is 0033 s, which provides a high performance method for apple and tomato leaf disease detection. The solution can classify and locate plant leaf diseases with high accuracy.