基于改进Inception网络的复杂环境下小样本黄瓜叶片病害识别  

Recognition of cucumber leaf disease with small samples in complex environment based on improved Inception network

作　　者：满超 饶元[1] 张敬尧 乔焰 王胜和 MAN Chao;RAO Yuan;ZHANG Jingyao;QIAO Yan;WANG Shenghe(School of Information and Computer,Anhui Agricultural University,Hefei 230036,China;Anhui Academy of Public Security Education,Hefei 230031,China;School of Computer Science and Information Engineering,Hefei University of Technology,Hefei 230601,China)

机构地区：[1]安徽农业大学信息与计算机学院,合肥230036 [2]安徽省公安教育研究院,合肥230031 [3]合肥工业大学计算机与信息学院,合肥230601

出　　处：《华中农业大学学报》2023年第3期152-160,共9页Journal of Huazhong Agricultural University

基　　金：安徽省自然科学基金项目(2008085MF203);安徽省重点研究和开发计划面上攻关项目(201904A06020056);安徽省高校自然科学研究重点项目(2022AH053088)。

摘　　要：为解决田间复杂环境下小样本黄瓜叶片病害识别中模型泛化能力差、识别准确率不高的问题,将自注意力机制模块引入激活重建生成对抗网络(activation reconstruction GAN,AR-GAN),采用Smooth L_(1)正则化作为损失函数,设计改进激活重建生成对抗网络IAR-GAN(improved AR-GAN)增广黄瓜叶片病害图像。通过在Inception网络基础上加入空洞卷积和形变卷积,设计空洞和形变卷积神经网络(dilated and deformable convolutional neural network,DDCNN)用于黄瓜叶片病害识别。试验结果显示,提出的IAR-GAN有效缓解了过拟合现象,丰富了生成样本的多样性;所提出的DDCNN对黄瓜炭疽病、斑靶病和霜霉病的平均识别准确率均达到96%以上,比Inception-V3模型提高了9个百分点。以上结果表明,本研究提出的数据增广方法和病害识别模型可为复杂环境下小样本的作物叶部病害的准确识别提供新思路。In order to solve the problems of poor generalization ability and low recognition accuracy in the identification of cucumber leaf disease with small samples under complex field environment,the self-attention mechanism module was introduced into the activation reconstruction network AR-GAN(activation reconstruction GAN),and the smooth L_(1) regularization was used as the loss function to design and improve the activation reconstruction network IAR-GAN(improved AR-GAN) to expand the cucumber leaf disease image.By adding void convolution and deformation convolution on the basis of the Inception network,the void and deformation convolution neural network(DDCNN) was designed for cucumber leaf disease identification.The test results showed that the proposed IAR-GAN effectively alleviated the over-fitting phenomenon and enriched the diversity of generated samples.The average recognition accuracy of the proposed DDCNN for cucumber anthracnose,spot target disease and downy mildew was more than 96%,which is 9% higher than the Inrception-V3 model.The above results showed that the data expansion method and disease identification model proposed in this paper can provide new ideas for the accurate identification of crop leaf diseases with small samples in complex environments.

关 键 词：病害识别 小样本 生成对抗网络 深度卷积神经网络 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] S435[自动化与计算机技术—计算机科学与技术]