# Autonomous Detection of _Spodoptera frugiperda_ by Feeding Symptoms Directly from UAV RGB Imagery
## 5 Conclusions and Future Directions  
This study aimed to detect maize images that included leaves infected by _Spodoptera frugiperda_ in the early stages. Four different models including ResNeSt50, ResNet50, EfficientNet, and RegNet were used to verify the feasibility of using the above features for recognition and explore the split-attention mechanism to improve the accuracy and robustness of the model. The ResNeSt50 network achieved a high accuracy of 98.77% in the validation data set based on the jointing stage and of 89.39% in the test data set based on the heading stage. The model demonstrated its ability to identify infected maize leaves at various stages and allowed to classify them according to the degree of infection. In the process of model construction, methods such as data enhancement and transfer learning are adopted to speed up model construction, reduce overfitting, and improve robustness. Accurate treatment can carry out according to an image's coordinates the grade and distribution of infected leaves, which can significantly reduce the use of pesticides and assist in the implementation of biological control.  
Although the model can accurately and quickly identify and judge the maize leaves present in the image for insect pests, the following problems still need to be further studied and explored in practical application: (1) under the condition of positive projections, so that some pest leaves may be ignored due to occlusion; (2) the image acquisition parameters such as height, angle, and resolution and actual field planting conditions; (3) the overall statistical analysis of field pest distribution and subsequent application should be further explored with agronomic knowledge. In future research, we will use the model of _Spodoptera frugiperda_ based on more accurate network architecture for real-time field corn image recognition. In addition, according to the optimal resolution combination obtained, we  
Figure 10: Attention map base on grad CAM.  
will conduct a new round of data collection in this year's maize planting period to further verify the method. At the same time, we will collect more data to build a model that can identify more pests and diseases faster and more accurately.  
Conceptualization, Y.S. and J.F.; methodology, J.F.; software, J.F.; validation, Y.Z., J.F. and Y.R.; formal analysis, J.F.; investigation, S.C. and J.F.; resources, S.C.; data curation, S.C. and J.F.; writing--original draft preparation, J.F.; writing--review and editing, Y.S., K.Z., Y.C., H.Z. and Y.Z.; visualization, J.F. and Y.R.; supervision, K.Z.; project administration, Y.S. and K.Z.; funding acquisition, K.Z. and Y.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Funds for the Central Universities (Grant No. 2017 XKQY019). This work was supported by a project founded by the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions.  
Not applicable.  
The data presented in this study are available within the article.  
The authors declare no conflict of interest.