江西农业大学学报2023，Vol.45Issue(6)：1517-1527,11.DOI:10.13836/j.jjau.2023139
基于改进ResNet18的胡麻干旱胁迫识别与分类研究
Identification and Classification of Flax Drought Stress Based on Improved ResNet18
摘要
Abstract
[Objective]In order to realize the real-time monitoring of flax drought stress on the mobile terminal,and solve the problem of low accuracy and slow speed of traditional machine learning methods in recognizing and classifying,this study proposes a classification and recognition method of flax drought stress based on improved ResNet18.[Method]Firstly,the Convolutional Block Attention(CBAM)module is added to the network to strengthen the network's ability to extract features;secondly,the connection order of the batch standard layer,activation function,and convolutional block in the residual block is adjusted to achieve the normalization operation of the input sample data;and lastly,the ReLU activation function is replaced by the LeakyReLU activation function to avoid the phenomenon of neural death.The experiment was divided into three water stress treatments,namely,coercionless,mild coercion and severe coercion,and the images of flax leaves with different drought degrees were collected in batches,the data samples were divided into the training set and the test set based on the proportion of 3∶1,and the method of data enhancement was used to increase the diversity of the samples.[Result]The test results show that the classification accuracy of the improved ResNet18 model is as high as 98.67%,which is 6.14 and 4.87 percentage points higher than that of ResNet18 and VGG16,respectively,while the required parameter size of the model is only 42.80 MB,and the inference time for a single image is 17.50 ms.[Conclusion]The model of this study has a better classification and recognition effect on flax drought stress.It can realize the real-time requirements of flax drought stress recognition on embedded devices,thus providing technical support for the research of flax drought monitoring,mechanized production and so on.关键词
胡麻干旱胁迫/图像识别/ResNet18/迁移学习/深度学习Key words
flax drought stress/image recognition/ResNet18/transfer learning/deep learning分类
农业科技引用本文复制引用
刘芳军,李玥,武凌,吴丽丽..基于改进ResNet18的胡麻干旱胁迫识别与分类研究[J].江西农业大学学报,2023,45(6):1517-1527,11.基金项目
国家自然科学基金项目(32060437)和甘肃省科技计划-自然科学基金重点项目(23JRRA1403) Project supported by the National Natural Science Foundation of China(32060437)and Gansu Provincial Science and Technology Program-Natural Science Foundation Key Projects(23JRRA1403) （32060437）
