ZHANG Shanwen,ZHANG Qingqing,LI Ping.Apple disease identification based on improved deep convolutional neural network[J].Journal of Forestry Engineering,2019,4(04):107-112.[doi:10.13360/j.issn.2096-1359.2019.04.016]





Apple disease identification based on improved deep convolutional neural network
郑州西亚斯学院,郑州 451150
ZHANG Shanwen ZHANG Qingqing LI Ping
Zhengzhou SIAS University, Zhengzhou 451150, China
苹果病害 图像识别 深度卷积神经网络 全局平均池化
apple disease image recognition deep convolutional neural networks(DCNNs) global average pooling
TP391.4; S431.9
Deep convolutional neural networks(DCNNs)have achieved impressive performance in the field of image classification.In the classical DCNNs, there are often two or three fully connected layers to be used to map the learned feature maps and concatenate them as a one-dimensional feature vector, and then are input into the Softmax classifier to classify the images.The drawbacks of these DCNNs models are the over-fitting phenomenon, long time to train their parameters and weak generalization ability due to too many parameters in the fully connected layers, especially in the last fully connected layer.Because there are too many parameters in the model and limited training samples in practice, the trained model is easy to produce over-fitting.Over-fitting problem can be solved by Dropout.Because the weight update obtained by Dropout is no longer dependent on the joint action of hidden nodes with fixed relations, preventing certain features from having effects only under other specific features, therefore the network can learn more robust features.Although dropout is effective to handle the over-fitting, one of the disadvantages is that the cost function is no longer clearly defined.Because in iterations, some points are randomly removed, it is hard to check the gradient descent performance.In order to improve the performance of DCNNs-based image recognition method, an improved DCNNs(IDCNNs)model is proposed and applied to apple disease identification.Different from the classical DCNNs, the proposed model employs one global average pooling layer instead of all the full connection layers and utilizes the modified Softmax classifier replacing the Softmax classifier of DCNNs.IDCNNs have two advantages.One is that there are few parameters needing to be optimized in the global pooling layer, thus over-fitting is naturally avoided at this layer.The other is that the global pooling sums up the spatial information, hence it is more robust to spatial translation of the input images.Moreover, IDCNNs is more consistent with the working structure of DCNNs.Each feature map is correlated with the category output, rather than the unit of the feature map is directly correlated with the category output.Experimental results on an actual apple diseased leaf image database show that the proposed method can address the overfitting problem, improve the recognition rate of disease recognition and greatly reduce the training and recognition time of the model.


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 ZHANG Xizhi,LI Lijun*.Research of image recognition of camellia oleifera fruit based on improved convolutional auto-encoder[J].Journal of Forestry Engineering,2019,4(04):118.[doi:10.13360/j.issn.2096-1359.2019.03.018]


收稿日期:2018-12-30 修回日期:2019-02-22
基金项目:国家自然科学基金(61473273); 河南省教育厅科技攻关项目(182102210544,182102311094)。
作者简介:张善文,男,教授,研究方向为智能算法处理及其应用。E-mail: wjdw716@163.com
更新日期/Last Update: 2019-07-10