基于卷积神经网络的植物病害识别技术
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摘要
针对植物叶片的病害识别问题,设计一种基于卷积神经网络的识别方法,针对植物前景与背景分割问题,设计一种新的灰度变换方法,之后使用Ostu法与基于Sobel算子混合切割的方法成功将植物叶片的前景与背景分离,针对38种植物病害对Alex Net进行改进,使用19000张预处理完成后的图片对神经网络迭代30次以后,在植物病害数据集上的识别率达到98.4375%,可以为卷积神经网络在植物病害识别做一个参考。
Designs a recognition method based on convolution neural network to identify diseases of plant leaves. Aiming at the problem of plant fore-ground and background segmentation, designs a new method of gray scale transformation, after that, the Ostu method and Sobel based hy-brid cutting method are used to separate the foreground of plant leaves from the background. The Alex Net model is improved according tothe 38 plant diseases, after 19000 preprocessed images, the neural network is iterated for 30 times. The recognition rate on plant disease da-ta sets reaches 98.4375%, this can be used as a reference for the recognition of plant disease by convolution neural network.
Designs a recognition method based on convolution neural network to identify diseases of plant leaves. Aiming at the problem of plant fore-ground and background segmentation, designs a new method of gray scale transformation, after that, the Ostu method and Sobel based hy-brid cutting method are used to separate the foreground of plant leaves from the background. The Alex Net model is improved according tothe 38 plant diseases, after 19000 preprocessed images, the neural network is iterated for 30 times. The recognition rate on plant disease da-ta sets reaches 98.4375%, this can be used as a reference for the recognition of plant disease by convolution neural network.
引文
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[3]程鹏飞.植物病害的图像处理及特征值提取方法的研究[D].山西农业大学,2005.
[4]齐龙.基于图像处理的作物病害诊断及叶片形态参数测量技术的研究[D].吉林大学,2006.
[5]冯洁,廖宁放,赵波,罗永道,李宝聚.常见黄瓜病害的多光谱诊断[J].光谱学与光谱分析,2009,29(02):467-470.
[6]Zeiler M D,Fergus R.Visualizing and Understanding Convolutional Networks[C].European Conference on Computer Vision.Springer,Cham,2014:818-833.
[7]Le Cun Y,Bottou L,Bengio Y,et al.Gradient-based Learning Applied to Document Recognition[J].Proceedings of the IEEE,1998,86(11):2278-2324
[8]Wen Y,Zhang K,Li Z,et al.A Discriminative Feature Learning Approach for Deep Face Recognition[C].European Conference on Computer Vision,Springer International Publishing,2016:499-515.
[9]Sladojevic S,Arsenovic M,Anderla A,et al.Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification[J].Computational Intelligence and Neuroscience,2016(6):1-11.
[10]Sermanet P,Chintala S,Lecun Y.Convolutional Neural Networks Applied to House Numbers.