基于卷积神经网络马铃薯叶片病害识别和病斑检测*

针对自然环境下马铃薯叶片病害识别率低和晚疫病斑定位难的问题,基于大田环境中采集的马铃薯叶片图像,首先对马铃薯叶片病害进行识别,对比AlexNet、VGG16、InceptionV3、ResNet50、MobileNet五种神经网络模型,结果表明InceptionV3模型的识别效果准确率最高,可达98.00%。其次对马铃薯叶片的晚疫病斑进行检测,提出一种改进型的CenterNet-SPP模型,该模型通过特征提取网络获取对象的中心点,再通过中心点回归获得中心点偏移量、目标大小等图像信息,训练后的模型在验证集下的mAP可达90.03%,以F₁为评价值分析对比其它目标检测模型,CenterNet-SPP模型的效果最好,准确率为94.93%,召回率为90.34%,F₁值为92.58%,平均检测一张图像耗时0.10 s。为自然环境下马铃薯叶片病害识别和检测提供较为全面的深度学习算法和模型研究基础。

Potato leaf disease recognition and potato leaf disease spot detection based on Convolutional Neural Network

This paper aims to solve the problems of low recognition rate of potato leaf diseases and difficulty in localization of late blight spots in the natural environment based on potato leaf images collected in the field environment. Firstly, the potato leaf disease was identified using five neural network models: ALEXNET, VGG16, InceptionV3, RESNET50, and MobileNet. The results show that the InceptionV3 model has the highest recognition accuracy, which can reach 98.00%. Secondly, the late blight spots of potato leaves were detected, and an improved CenterNet-SPP model was proposed. The model obtains the center point of the object through the feature extraction network and then obtains the image information such as the offset of the center point and the size of the target through the regression of the center point. The mAP of the trained model under the verification set was up to 90.03%.Using F₁ as the evaluation value analysis, compared with other objective detection models, the CenterNet-SPP model has the best effect, whose accuracy is 94.93%, recall rate is 90.34%, F₁ value is 92.58%, and the average detection time of an image is 0.10 s.This paper provides a relatively comprehensive deep learning algorithm and model research basis for potato disease recognition and detection in the natural environment.