The assessment of litchi flowering growth based on remote sensing image of convolutional neural network

WEN Fei; MO Jiawei; HU Yuqi; LAN Yubin; CHEN Xin; LU Jianqiang; DENG Xiaoling

doi:10.7671/j.issn.1001-411X.202203040

Journal of South China Agricultural University > 2023 > 44(1): 123-133. > DOI: 10.7671/j.issn.1001-411X.202203040

WEN Fei, MO Jiawei, HU Yuqi, et al. The assessment of litchi flowering growth based on remote sensing image of convolutional neural network[J]. Journal of South China Agricultural University, 2023, 44(1): 123-133. DOI: 10.7671/j.issn.1001-411X.202203040

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The assessment of litchi flowering growth based on remote sensing image of convolutional neural network

WEN Fei^1,,
MO Jiawei¹,
HU Yuqi¹,
LAN Yubin^{1, 2},
CHEN Xin^{1, 2},
LU Jianqiang^{1, 2},
DENG Xiaoling^{1, 2, ,}

1.
College of Electronic Engineering, South China Agricultural University/National Center for International Collaboration Research on Precision Agricultural Aviation Pesticides Spraying Technology, Guangzhou 510642, China
2.
Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China

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Received Date: January 11, 2022
Available Online: May 17, 2023

Graphical Abstract

Abstract

Abstract

Objective
In order to provide decision-making basis for subsequent litchi flower thinning, fruit retention and precise fertilization application, this work evaluated the flowering rate of each litchi by analyzing UAV remote sensing images of litchi canopy.

Method
The remote sensing image of each litchi canopy was segmented through instance segmentation algorithm. The flowering rates were classified into four categories combining with comprehensive judgment of horticultural experts, which were 0, 10%−20%, 50%−60%, 80% and above. ResNet50, ResNeXt50 and ShuffleNetv2 were adopted to compare flowering rate classification. Due to the great advantages in recognition accuracy, number of parameters, training and verification time, ShuffleNetv2 was adopted as the instance segmentation algorithm, and optimized by introducing the spatial attention module (SAM) to increase the model’s learning of location information, and improve the accuracy of litchi canopy flowering classification without significantly increasing the number of parameters.

Result
Through comparison of the mainstream deep learning algorithms, the classification accuracy of ResNet50, ResNeXt50 and ShuffleNetv2 reached 85.96%, 87.01% and 86.84% respectively, and the improved ShuffleNetv2 reached 88.60%, higher than the above three algorithms. The verification time of single canopy image on test set using ResNet50, ResNeXt50, ShuffleNetv2 and the improved ShuffleNetv2 were 8.802, 9.116, 7.529 and 7.507 ms respectively, showing that the improved ShuffleNetv2 single canopy image got the shortest verification time.

Conclusion
The improved ShuffleNetv2 can excavate and learn more detailed flowering information of litchi canopy, with high recognition accuracy and great advantages in the evaluation of litchi flowering, providing an intelligent decision support for flower protection and sparseness, and precise control of production.
- UAV remote sensing,
- Litchi,
- Florescence growth,
- Convolutional neural network,
- Image processing,
- Attention module

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References (28)

References

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