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基于改进ResNet的植物叶片病虫害识别

花匠小妙招
2024-11-26 21:16

摘要： 轻量化植物叶片病虫害识别算法设计是实现移动端植物叶片病虫害识别的关键。研究提出一种基于改进ResNet模型的轻量化植物叶片病虫害识别算法SimplifyResNet。以人工采集图像和PlantVillage数据集图像为实验数据，根据移动端植物病虫害识别对准确率、速度和模型大小的实际需求，改进ResNet模型。使用5×5卷积替代7×7卷积，采用残差块的瓶颈结构代替捷径结构，采用模型剪枝处理训练后的模型。通过测试集5 786幅图像测试SimplifyResNet模型，证明5×5卷积和残差块的瓶颈结构可有效降低模型参数量，模型剪枝可有效降低训练后的模型大小。SimplifyResNet模型对测试集图像的识别准确率为92.45%，识别时间为48 ms，内存大小为36.14 Mb。与LeNet、AlexNet和MobileNet等模型相比，其准确率分别高18.3%，7.45%和1.2%。为移动端植物病虫害识别解决最重要的算法设计问题，为移动端植物病虫害识别做出有益探索。

关键词: 图像处理, 病虫害, 图像识别, 卷积神经网络, 模型剪枝

Abstract: The design of lightweight plant leaf pests and diseases identification algorithm is the key to identifying plant leaf pests and diseases on the mobile terminal. This research proposes a lightweight plant leaf pests and diseases identification algorithm SimplifyResNet based on the improved ResNet model. Using artificially collected images and PlantVillage dataset images as experimental data, the ResNet model is improved according to the actual requirements for accuracy, speed, and model size of plant pests and diseases identification on the mobile terminal. The design uses 5×5 convolution instead of 7×7 convolution, uses the bottleneck structure of the residual block to replace the shortcut structure, and uses model pruning to process the trained model. The SimplifyResNet model was tested with 5 786 images on the test set. It was proved that the bottleneck structure of 5×5 convolution and residual block can effectively reduce the number of model parameters, and model pruning can effectively reduce the model size after training. The SimplifyResNet model has an accuracy of 92.45% for image recognition in the test set, a recognition time of 48 ms, and a memory size of 36.14 Mb. Compared to models, such as LeNet, AlexNet, and MobileNet, the accuracy of this method is 18.3%, 7.45%, and 1.2% higher, respectively. This research solves the most critical algorithm design problem for mobile plant pests and diseases identification and makes functional explorations for mobile plant pests and diseases identification.

Key words: image processing, disease, image recognition, convolution neural network, model pruning

中图分类号:

TP391.4