# Autonomous Detection of _Spodoptera frugiperda_ by Feeding Symptoms Directly from UAV RGB Imagery
## 4 Discussion  
In this study, we proposed the deep learning model to detect the invasion of _Spodoptera frugiperda_ by the features of the damaged leaves. At the same time, four different neural network structures are used to verify the feasibility of the proposed method. In addition, in order to verify the ability of this feature against maize morphological change, the four models were trained on the images at the jointing stage and tested on the images at the heading stage. The appearance of stamen and the fall of stamen in corn leaves images at the heading stage has a certain influence on the overall structure and color of the image. However, the neural network based on features of the damaged leaves still has good accuracy. Accuracy, Sensitivity, Specificity, Precision, F1 score, and Kappa were used to demonstrate the recognition ability of maize leaves with holes (see Table 4) (TSW: Translucent silver window, IW: Irregular wormhole).  
It can be seen from the table that the models based on four different network structures all have a good ability to identify the infected leaves from the corn images at the heading stage. However, compared with the original valid accuracy, the current accuracy has a degree of decline, respectively 89.39%, 81.88%, 86.21%, and 84.21%. The split-attention  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Model** & **Class** & **Health** & **TSW** & **IW** & **Accuracy** & **Sensitivity** & **Specificity** & **Precision** & **F1 Score** & **Kappa** \\ \hline \multirow{3}{*}{ResNeSt50} & Health & 473 & 47 & 12 & \multirow{3}{*}{89.39\%} & 0.89 & 0.97 & 0.94 & 0.91 & \\  & TSW & 7 & 398 & 12 & 89.39\% & 0.950.86 & 0.90 & 0.97 & 0.86 & 0.84 \\  & IW & 22 & 64 & 510 & \multirow{3}{*}{81.88\%} & 0.94 & 0.97 & 0.96 & 0.90 & \\  & Health & 390 & 37 & 105 & & 0.73 & 0.97 & 0.94 & 0.82 & \\ ResNet50 & TSW & 14 & 349 & 54 & 81.88\% & 0.84 & 0.91 & 0.79 & 0.81 & 0.72 \\  & IW & 13 & 57 & 526 & & 0.88 & 0.82 & 0.77 & 0.82 & \\  & Health & 456 & 39 & 37 & & 0.86 & 0.95 & 0.91 & 0.88 & \\ \multirow{3}{*}{EfficientNet} & TSW & 18 & 377 & 22 & \multirow{3}{*}{86.21\%} & 0.90 & 0.90 & 0.77 & 0.83 & 0.79 \\  & IW & 25 & 72 & 499 & & 0.84 & 0.93 & 0.89 & 0.86 & \\ \multirow{3}{*}{RegNet} & Health & 419 & 30 & 83 & & 0.79 & 0.97 & 0.94 & 0.86 & \\  & TSW & 15 & 342 & 60 & 84.21\% & 0.82 & 0.93 & 0.82 & 0.82 & 0.76 \\  & IW & 10 & 46 & 540 & & 0.91 & 0.84 & 0.79 & 0.84 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test results—based on images at the heading stage.  
Figure 9: Visualization of model’s outputs. The upper part of the figure is the original image, the lower part is the image after adding the infected color block, and the left and right yellow image cells are the enlarged image of the infected area.  
models outperformed the origin ResNet50 structures and the classical network model on the performance in terms of Accuracy, Precision, etc. ReNest50 also achieves the best results on the Kappa coefficients and F1 Score. What is more, we can see the differences between these networks more clearly in the CAM (Class Activation Map based on the average gradient) in Figure 10 below. Compared with the results of other network structures, the split-attention network can identify the target more accurately and closely.