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

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.