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Smart Agriculture ›› 2019, Vol. 1 ›› Issue (4): 42-49.doi: 10.12133/j.smartag.2019.1.4.201908-SA002

• 信息感知与获取 • 上一篇    下一篇

基于深度残差网络的番茄叶片病害识别方法

吴华瑞1,2   

  1. 1. 国家农业信息化工程技术研究中心,北京 100097
    2. 农业农村部农业信息技术重点实验室,北京 100097
  • 收稿日期:2019-08-14 修回日期:2019-11-07 出版日期:2019-10-30 发布日期:2019-12-24
  • 基金资助:
    国家自然科学基金(61871041);北京市自然科学基金(4172024)
  • 作者简介:通信作者:吴华瑞(1975-),男,博士,研究员,研究方向:农业智能系统,电话:13910293903,Email:wuhr@nercita.org.cn

Method of tomato leaf diseases recognition method based on deep residual network

Wu Huarui1,2   

  1. 1. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
    2. Key Laboratory of Information Technologies in Agriculture, Ministry of Agriculture and Rural Affairs, Beijing 100097, China
  • Received:2019-08-14 Revised:2019-11-07 Online:2019-10-30 Published:2019-12-24

摘要:

传统深度学习模型在用于蔬菜病害图像识别时,存在由于网络梯度退化导致的识别性能下降问题。为此,本文研究了一种基于深度残差网络模型的番茄叶片病害识别方法。该方法首先利用贝叶斯优化算法自主学习网络中难以确定的超参数,降低了深度学习网络的训练难度。在此基础上,通过在传统深度神经网络中添加残差单元,解决了由于梯度爆炸/消失造成的过深层次病害识别网络模型性能下降的问题,能够实现番茄叶片图像的高维特征提取,根据该特征可进行有效病害鉴定。试验结果表明,本研究中基于超参数自学习构建的深度残差网络模型在番茄病害公开数据集上取得了良好的识别性能,对白粉病、早疫病、晚疫病和叶霉病等4种番茄叶片常见病害的识别准确率达到95%以上。本研究可为快速准确识别番茄叶片病害提供参考。

关键词: 设施蔬菜, 病害智能识别, 深度学习, 残差网络, 贝叶斯优化

Abstract:

Intelligent recognition of greenhouse vegetable diseases plays an important role in the efficient production and management. The color, texture and shape of some diseases in greenhouse vegetables are often very similar, it is necessary to construct a deep neural network to judge vegetable diseases. Based on the massive image data of greenhouse vegetable diseases, the depth learning model can automatically extract image details, which has better disease recognition effect than the artificial design features. For the traditional deep learning model of vegetable disease image recognition, the model recognition accuracy can be improved by increasing the network level. However, as the network level increases to a certain depth, it will lead to the degradation / disappearance of the network gradient, which degrades the recognition performance of the learning model. Therefore, a method of vegetable disease identification based on deep residual network model was studied in this paper. Firstly, considering that the super parameter value in the deep network model has a great influence on the accuracy of network identification, Bayesian optimization algorithm was used to autonomously learn the hyper-parameters such as regularization parameters, network width, stochastic momentum et al, which are difficult to determine in the network, eliminate the complexity of manual parameter adjustment, and reduce the difficulty of network training and saves the time of network construction. On this basis, the gradient could flow directly from the latter layer to the former layer through the identical activation function by adding residual elements to the traditional deep neural network. The deep residual recognition model takes the whole image as the input, and obtains the optimal feature through multi-layer convolution screening in the network, which not only avoids the interference of human factors, but also solves the problem of the performance degradation of the disease recognition model caused by the deep network, and realizes the high-dimensional feature extraction and effective disease recognition of the vegetable image. Relevant simulation results show that compared with other traditional models for vegetable disease identification, the deep residual neural network shows better stability, accuracy and robustness. The deep residual network model based on hyperparametric self-learning achievesd good recognition performance on the open data set of tomato diseases, and the recognition accuracy of 4 common diseases of tomato leaves reached more than 95%. The researth can provide a basic methed for fast and accurate recognition of tomato leaf diseases.

Key words: facility vegetable, disease intelligent recognition, deep learning, residual network, Bayesian optimization

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