Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (6): 2304-2312.doi: 10.13229/j.cnki.jdxbgxb20200733

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Weed identification method based on deep transfer learning in field natural environment

Yan-lei XU(),Run HE,Yu-ting ZHAI,Bin ZHAO,Chen-xiao LI()   

  1. College of Information Technology，Jilin Agricultural University，Changchun 130118，China
  • Received:2020-09-22 Online:2021-11-01 Published:2021-11-15
  • Contact: Chen-xiao LI E-mail:yanleixu@jlau.edu.cn;licx@jlau.edu.cn

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Abstract:

At present, weed identification mostly uses weeds images taken in laboratory, and it is difficult to quickly and accurately identify weeds in a complex field environment. In order to improve low weed identification accuracy and slow detection speed in natural field environments, according to the characteristics of the weed image data, this study proposed a weed recognition model based on Xception lightweight convolutional network by improving the activation function and pooling layer. Firstly, the Xception was improved using Exponential Linear Unit (ELU) as the activation function of the model, and the global max pooling layer was used to downsample the convolution of the last layer. Then, the weed image was processed by background segmentation and data enhancement, and the best model was obtained by fine tuning on the pre-trained model. Compared with the four standard deep convolutional network models of VGG16, VGG19, ResNet50 and Inception-V3 under the same experimental conditions, the experimental results show that the overall performance of the model in this paper was optimal with the test accuracy rate as high as 98.63%. The scale of the model was 83.5MB, and the average time for detecting a single weed image was only 63.8ms. This study can provide theoretical basis and technical support for the implementation of precision spraying in natural field environment.

Key words: artificial intelligence, weed identification, lightweight convolutional, activation function, transfer learning

CLC Number: 

  • TP183

Fig.1

Partial image sample"

Fig.2

Background segmentation of sample images"

Fig.3

Schematic diagram of depthwiseseparable convolution"

Fig.4

Comparison of activation functionsbetween ELU and ReLU"

Fig.5

Structure diagram of weed identification model"

Fig.6

Model training process based ontransfer learning"

Table 1

Training results based on different datasets"

数据编号数据处理方法训练集准确率/%测试集准确率/%
①不做处理90.1385.02
②仅做背景分割93.8690.18
③仅做数据增强95.6794.93
④

背景分割和

数据增强

98.7898.63

Fig.7

Training curves of the model underdifferent activation functions"

Table 2

Comparison of recognition results ofdifferent global pooling layers"

全局池化类型准确率/%每轮训练 时长/s单张图像测试时间/ms
全局最大池化98.7812463.8
全局平均池化93.2912765.3

Table 3

Test results of different models ontraining set and test set"

模型类型模型大小/MB训练准确率/%测试准确率/%单张图像测试时间/ms
VGG16821.982.1280.81157.2
VGG19826.883.7682.19163.1
ResNet5094.492.8692.1781.4
Incepion-V394.793.9393.4970.1
本文模型83.598.7898.6363.8
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