Plant leaf recognition model based on two-way convolutional neural network

Yu Huiling; Ma Junwei; Zhang Yizhuo

doi:10.13332/j.1000-1522.20180182

Journal of Beijing Forestry University > 2018 > 40(12): 132-137. > DOI: 10.13332/j.1000-1522.20180182

Yu Huiling, Ma Junwei, Zhang Yizhuo. Plant leaf recognition model based on two-way convolutional neural network[J]. Journal of Beijing Forestry University, 2018, 40(12): 132-137. DOI: 10.13332/j.1000-1522.20180182

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Plant leaf recognition model based on two-way convolutional neural network

1.
School of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, Heilongjiang, China
2.
Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, Heilongjiang, China

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Received Date: May 29, 2018
Revised Date: October 21, 2018
Published Date: November 30, 2018

Graphical Abstract

Abstract

Abstract

ObjectiveAiming at the problem that the leaf edge shape has an excessive effect on the convolution layer during the process of identifying the leaf of convolutional neural network, which leads to the error recognition of similar edge shape leaves, a plant leaf recognition model of two-way convolutional neural network was proposed.
MethodThe model considers the edge shape and internal texture features of the blade information to construct a two-way convolutional neural network structure. Wherein, the shape feature path used a network structure of 7 layers of convolution layers, the first three layers used large-size 11×11 and 5×5 convolution kernels, extracting large field of view features to complete blade shape feature extraction, the other 4 layers of convolution layer used a 3×3 small size convolution core, extracting blade detail features. The two types of feature linear transformations were merged into one-dimensional feature vectors through a fully connected layer. Finally, the fully connected layer identified the plant leaf species.
ResultThe experimental results showed that the two-way convolutional network model was compared with the single-channel convolutional network and the image recognition classification recognition model. On the Flavia leaf dataset and the expanded complex background leaf dataset, the accuracy of Top-1 recognition increased to 99.28% and 97.31%, respectively. The accuracy of Top-3 recognition increased to 99.97% and 99.74%, respectively. The standard deviation decreased to 0.18 and 0.20 compared with other identification and classification models.
ConclusionThe blade recognition and classification model proposed in this paper can effectively avoid the problems caused by the similar blade edge shape interference and improve the recognition accuracy of leaf plant species.
- plant identification,
- blade image,
- feature fusion,
- convolutional neural network

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References (11)

References

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Plant leaf recognition model based on two-way convolutional neural network