基于模型压缩对番茄病害识别的应用研究  

Research on the Application of Tomato DiseaseIdentification Based on Model Compression

作　　者：段军明 杨祥[1,2] 董明刚[1,2] DUAN Junming;YANG Xiang;DONG Minggang(School of Information Science and Engineering,Guilin University of Technology,Guilin,Guangxi 541004;Guangxi Key Laboratory of Embedded Technology and Intelligent System,Guilin University of Technology,Guilin,Guangxi 541004)

机构地区：[1]桂林理工大学信息科学与工程学院,广西桂林541004 [2]桂林理工大学广西嵌入式技术与智能系统重点实验室,广西桂林541004

出　　处：《北方园艺》2023年第10期138-144,共7页Northern Horticulture

基　　金：国家自然科学基金地区资助项目(61563012);广西自然科学基金资助项目(2021GXNSFAA220074)。

摘　　要：早期发现番茄叶片患病类别,有利于快速进行诊断治疗,挽救作物损失。传统深度学习番茄病害识别方法存在模型体积较大、计算资源消耗大的问题,不适合直接部署在低计算能力和有限存储空间的便携式设备上。该研究采用知识蒸馏技术对模型进行压缩,同时使用金字塔挤压注意力模块来改进教师网络ResNet50提升网络性能。在教师网络的指导下,学生网络ShuffleNetV2取得了优异的性能。通过选取PlantVillage数据集中的番茄病害叶片进行试验。结果表明:蒸馏后的网络KD-ShuffleNetV2提高了模型的精度,与深度卷积神经网络Alexnet、Vgg11、ResNet50相比节省了更多的存储空间和计算资源。网络在番茄病害数据集上的识别准确率达95.66%,模型大小仅有4.98 MB。最后将模型移植部署到低成本的树莓派上,完成番茄叶片识别系统开发与识别应用。Early detection of tomato leaf disease category is conducive to rapid diagnosis and treatment to save crop losses.The traditional method of tomato disease identification based on deep learning has the problems of large model size and large consumption of computing resources,which is not suitable for direct deployment on portable devices with low computing power and limited storage space.In this study,knowledge distillation technology was used to compress the model,and pyramid squeeze attention module was used to improve the teacher network ResNet50to enhance the network performance.Under the guidance of the teacher network,the student network ShuffleNetV2had achieved excellent performance.By selecting tomato diseased leaves in PlantVillage dataset for experiment,the experimental results showed that the distilled network KD-ShuffleNetV2improved the accuracy of the model,and saved more storage space and computing resources than deep convolution neural networks Alexnet,Vgg11,and ResNet50.The network achieved 95.66%recognition accuracy on tomato disease dataset,and the size of the model was only 4.98 MB.Finally,the model was transplanted and deployed to the low-cost Raspberry Pi to complete the tomato leaf recognition system development and recognition application.

关 键 词：番茄病害 卷积神经网络 金字塔挤压注意力 知识蒸馏 树莓派 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]