Classification model of spraying deposition on citrus canopy based on small-scale data set

XU Xianghua; LIN Jiahan; LU Jianqiang; CHEN Hongze; YANG Ruifan

doi:10.7671/j.issn.1001-411X.202101025

Journal of South China Agricultural University > 2021 > 42(5): 127-132. > DOI: 10.7671/j.issn.1001-411X.202101025

XU Xianghua, LIN Jiahan, LU Jianqiang, et al. Classification model of spraying deposition on citrus canopy based on small-scale data set[J]. Journal of South China Agricultural University, 2021, 42(5): 127-132. DOI: 10.7671/j.issn.1001-411X.202101025

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Classification model of spraying deposition on citrus canopy based on small-scale data set

1.
Engineering Fundamental Teaching and Training Center, South China Agricultural University, Guangzhou 510642, China
2.
College of Electronic Engineering/College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
3.
National Precision Agriculture International Joint Research Center of Aerial Application Technology, Guangzhou 510642, China

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Received Date: January 13, 2021
Available Online: May 17, 2023

Graphical Abstract

Abstract

Abstract

Objective
The study was aimed to improve the intelligent management level of citrus orchards, quickly and non-destructively evaluate the spraying quality on citrus canopy, and solve the overfitting problem of the spraying quality classification model caused by small-scale data set.

Method
We proposed a classification model of spraying quality on citrus canopy based on convolutional neural network: Visual geometry group citrus model (VGG_C model). The model was constructed based on the core idea of the VGG model. Through optimization of the cross-entropy loss function, the iterative process of probability distribution and true distribution was accelerated. The uncertainty measurement calculation was introduced at the output end and the Droupout method was inserted in the downsampling module to reduce the probability of overfitting due to small amount of data.

Result
The loss value of VGG_C model for the training set was 0.44%, which was 87% and 91% lower than that of ResNet and VGG respectively. The accuracy of VGG_C model for the training set was 95.3%, which was 5% and 10% higher than that of ResNet and VGG respectively. The average accuracy of the verification set was 96.4%.

Conclusion
VGG_ C model can effectively extract the features of citrus canopy thermal infrared image through multi-layer convolution model and improve the training and testing superiority of citrus canopy application classification model in small data set by optimizing the output structure. VGG_ C model can provide an effective reference for the intelligent judgment of pesticide application on citrus trees.
- citrus,
- pesticide spraying,
- infrared thermal image,
- convolutional neural network,
- cross entropy error function

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References (22)

References

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Classification model of spraying deposition on citrus canopy based on small-scale data set