Abstract In the field of agricultural information, identification and prediction of rice leaf diseases has always been a research focus. Deep learning and support vector machine (SVM) technology are hot research topics in the field of pattern recognition at present. Their combination can not only solve the problem effectively, but also improve the recognition accuracy. In this study, firstly, we use convolution neural networks (CNNs) to extract the rice leaf disease images features. Then the SVM method is applied to classify and predict the specific disease. The optimal parameters of SVM model are obtained through the 10-fold cross validation method. The experimental results show that when the penalty parameter C = 1 and the kernel parameter g = 50, the average correct recognition rate of the rice disease recognition model based on deep learning and SVM is 96.8%. This accuracy is higher than that of the traditional back propagation neural networks models. This study provides a new method for the further research of crop diseases diagnosis by using deep learning.

中文翻译：

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基于深度学习和支持向量机的四种水稻叶片病害图像识别

摘要 在农业信息领域，水稻叶片病害的识别与预测一直是研究热点。深度学习和支持向量机（SVM）技术是目前模式识别领域的热门研究课题。它们的结合不仅可以有效解决问题，还可以提高识别准确率。在这项研究中，首先，我们使用卷积神经网络（CNN）来提取水稻叶片病害图像特征。然后应用SVM方法对特定疾病进行分类和预测。SVM模型的最优参数是通过10折交叉验证的方法得到的。实验结果表明，当惩罚参数C=1，核参数g=50时，基于深度学习和SVM的水稻病害识别模型平均正确识别率为96.8%。这个精度高于传统的反向传播神经网络模型。本研究为利用深度学习进一步研究作物病害诊断提供了一种新方法。