基于图像识别的苹果叶片病害识别模型对比研究

为实现苹果叶片病害图像自动识别,展开苹果叶片病害识别模型研究。通过整理网络开源植物病害数据,获取苹果赤霉病、苹果雪松锈病和苹果灰斑病3种苹果病害叶片图像,以1种健康叶片图像作为研究对象,随机抽取4433张图像建立数据集用于模型训练,采用离线增强和在线增强2种手段对数据进行预处理,扩充图像样本并保证各类样本均衡。在Resnet 50、Mobilenet v2、Vgg16、Vgg19、Inception v3等5种预训练模型的基础上,对迁移模型进行一系列的参数调整。5种模型训练比对结果表明,优化后的Resnet50模型能够达到0.9770的准确率。优化后的训练模型具有识别速度快、准确率提高的特点,可以准确、快速地识别出病害类型,为植物病害的自动诊断提供支撑。

Comparative Study on Apple Leaf Disease Recognition Models Based on Image Recognition

To achieve automatic recognition of apple leaf disease images, this paper studied the recognition model of apple leaf disease. According to the data set of plant disease collected through the network open-source, three kinds of leaf images were obtained with apple scab, apple cedar rust and apple gray disease respectively, and one healthy leaf image was used as the study object. 4433 images were selected randomly to build the data set for model training, and all of these samples were expanded and averaged. The data were preprocessed with offline and online augmentation. On the basis of five pre-training models (Resnet50, Mobilenet v2, Vgg16, Vgg19, Inception v3), the parameters were adjusted and optimized for the migration model. Comparing the training results of the five models, the optimized Resnet50 model could achieve 0.9770 of accuracy. Featured by rapid recognition and high accuracy, the optimized training model could identify the disease types accurately and rapidly, and provide support for the automatic diagnosis of plant diseases.