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基于改进卷积神经网络的多种植物叶片病害识别
引用本文:孙 俊,谭文军,毛罕平,武小红,陈 勇,汪 龙.基于改进卷积神经网络的多种植物叶片病害识别[J].农业工程学报,2017,33(19):209-215.
作者姓名:孙 俊  谭文军  毛罕平  武小红  陈 勇  汪 龙
作者单位:1. 江苏大学电气信息工程学院,镇江,212013;2. 江苏大学江苏省现代农业装备与技术重点实验室,镇江,212013
基金项目:国家自然科学基金资助项目(No.31471413);江苏高校优势学科建设工程资助项目PAPD(苏政办发2011 6号);江苏省六大人才高峰资助项目(ZBZZ-019);江苏大学大学生科研立项资助项目(Y15A039);江苏大学大学生实践创新训练项目(No.46)
摘    要:针对训练收敛时间长,模型参数庞大的问题,该文将传统的卷积神经网络模型进行改进,提出一种批归一化与全局池化相结合的卷积神经网络识别模型.通过对卷积层的输入数据进行批归一化处理,以便加速网络收敛.进一步缩减特征图数目,并采用全局池化的方法减少特征数.通过设置不同尺寸的初始层卷积核和全局池化层类型,以及设置不同初始化类型和激活函数,得到8种改进模型,用于训练识别14种不同植物共26类病害并选出最优模型.改进后最优模型收敛时间小于传统卷积神经网络模型,仅经过3次训练迭代,就能达到90%以上的识别准确率;参数内存需求仅为2.6 MB,平均测试识别准确率达到99.56%,查全率和查准率的加权平均分数为99.41%.改进模型受叶片的空间位置的变换影响较小,能识别多种植物叶片的不同病害.该模型具有较高的识别准确率及较强的鲁棒性,该研究可为植物叶片病害的识别提供参考.

关 键 词:病害  植物  图像处理  识别  卷积神经网络  批归一化  全局池化  深度学习
收稿时间:2017/5/17 0:00:00
修稿时间:2017/9/13 0:00:00

Recognition of multiple plant leaf diseases based on improved convolutional neural network
Sun Jun,Tan Wenjun,Mao Hanping,Wu Xiaohong,Chen Yong and Wang Long.Recognition of multiple plant leaf diseases based on improved convolutional neural network[J].Transactions of the Chinese Society of Agricultural Engineering,2017,33(19):209-215.
Authors:Sun Jun  Tan Wenjun  Mao Hanping  Wu Xiaohong  Chen Yong and Wang Long
Institution:1. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China,1. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China,2. Jiangsu Provincial Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Zhenjiang 212013, China,1. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China,1. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China and 1. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Abstract:Abstract: Plant leaf diseases are a serious problem in agricultural production. To solve this problem and prevent diseases deterioration, accurate identification of diseases types is of great significance. In this paper, we proposed a recognition model of plant leaf diseases based on convolutional neural network (CNN), which combines the batch normalization and global pooling methods. The parameters of the traditional CNN model are large and have difficulty to converge. The proposed model was modified in the traditional structure of the CNN, which could optimize the training time and achieve the higher accuracy, and also reduce the size of model. In order to speed up the training convergence, we used the batch normalization layers. We put the input of every convolutional layer in batch, calculated the mean and variance of the batch, and then normalized this batch. We reduced some feature maps of some layers and removed the last full connect layer, with the global pooling layer instead. The proposed model has 5 convolutional layers and 4 pooling layers. In the last pooling layer pool5, the same kernel size of convolutional layer Conv5 was used to take advantage of the information of Conv5''s feature map comprehensively. For the image preprocessing, we had zoomed, flipped and rotated the original pictures of dataset randomly to get the augmented dataset, and used the 80% of pictures as the train dataset and the rest as the test dataset. These pictures were quantized to 256×256 dpi for CNN training, and the original dataset and augmented dataset were used to train models. To look for the best size of the first layer kernel, in the first convolutional layer, different kernel sizes i.e. 11×11, 9×9 and 7×7 dpi were used respectively. Furthermore, we chose the type of global pooling layer, like max pooling and average pooling. Then we designed 8 models with different Conv1 kernel sizes or global pooling types. To further improve the efficiency of this model, besides using the Gaussian initialization, we used the other common type of convolutional initialization such as Xavier initialization, and also used the PRelu activation function for each convolution layer. So the optimal model could be selected to recognize the 26 kinds of leaf diseases which involved 14 kinds of plants, and then we analyzed the model''s convergence rate, memory usage and robustness. After the experiment, we compared the test accuracy between the traditional model and the proposed model based on original dataset and augmented dataset. The proposed model could accelerate the training convergence, and the test accuracy could achieve about 90% while the traditional model was only about 77% after 3 training epochs. Different kernel sizes of Conv1 had little impact on the accuracy but small kernel was proved to be more beneficial to the recognition of plant diseases, which could get more texture features than the big kernel size filter, and average pooling also made better results than max pooling. We got the best performance model which used the 9×9 dpi kernel size and global average pooling layer. To show the proposed model''s performance, we tested the accuracy on each class, and the mean accuracy of augmented test dataset was 99.56%, and the weighted average score of recall and precision rate achieved 99.41%. The proposed model had the size of only 2.6 MB. In addition, compared with the traditional methods, the change of the spatial position of the pictures had little effect on the performance of the improved model, and the proposed model could identify different diseases of various plant leaves. The results show that the model has higher recognition accuracy and stronger robustness, and can be used for the identification of plant leaf diseases.
Keywords:diseases  plants  image processing  recognition  convolutional neural network  batch normalization  global pooling  deep learning
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