# Autonomous Detection of _Spodoptera frugiperda_ by Feeding Symptoms Directly from UAV RGB Imagery
used the UNet-based BLSNet to automatic identify and segment the diseased region of Rice bacterial leaf streak from the camera photos [24]. The appearance of the attention mechanism also further improves the performance of the network [22; 25]. The following are studies based on UAV imagery combined with machine learning or deep learning: [PERSON] et al. detected soybean foliar diseases subjected to biological stress based on the simple linear iterative clustering segmentation method through foliar physical properties using RGB imagery captured by the low-cost unmanned aerial vehicle model DJI Phantom 3 [26]. [PERSON] et al. used an unmanned aerial vehicle (UAV) to acquire high-resolution images in the field, and they built an automated, high-throughput system based on a convolutional neural network (CNN) for the detection of northern leaf blight of maize plants [27]. [PERSON] et al. proposed a computerized system based on CNN to process images captured by UAVs at low altitudes, which can detect _Fusarium_ wilt of radish with high accuracy [28]. [PERSON] et al. used CNN to implement a detection method of virus-infected plants in a potato seed production field, with UAV RGB images being captured at an altitude of 5-10 m from the ground [7]. Compared with disease studies, insect pests are more flexible. There are two primary approaches to insect identification [29]: (i) direct, focusing on the ontology of the insects, and (ii) indirect, which focus on the damage caused by the insects [6]. For example, [PERSON] et al. used a field insect light trap to obtain images and combined the CNN and attention mechanism to construct a direct classification model for insect identification [30]. [PERSON] focused on the significant change in the plant's leaf area index caused by _Spodoptera frugiperda_ to indirectly monitor the infestation [31]. On the other hand, using the camera to closely capture pest images is also a widely used method, such as [PERSON] et al. integrating Convolutional Neural Network (CNN) and non-maximum inhibition for positioning and counting aphids in rice images obtained by a close view camera, achieving 0.93 accuracy and 0.885 mAP by optimizing key parameters and feature extraction network [13]. The above methods may have defects in accuracy or cannot be applied to large area practice. Thanks to the development of UAV technology, the pest identification based on UAV images is worth further research [32]. [PERSON] et al. carry small aircraft RGB camera drones to obtain the vineyard plant image, and the application of geometric vision and computer vision technology, combined with landform factors on the influence of pests on the vineyard of the quantitative analysis for the farm digital management provides accurate low-cost information, which helps in the implementation and improvement of farm management and decision-making processes [33]. [PERSON] et al. also used the corn leaves damaged by _Spodoptera frugiperda_ and applied VGG16 and InceptionV3 to detect the infected corn leaves captured by the UAV (UAV) remote sensing technology while using \begin{table}
\begin{tabular}{c c c c} \hline CNN & Convolutional Neural Networks & UAV & Unmanned Aerial Vehicle \\ \hline RS & Remote Sensing & RGB & R (red), G (green), B (blue) \\ \hline \end{tabular}
\end{table} 
Table 1: Abbreviations. the angular detection method in computer vision to strengthen the feature representation and improve the detection accuracy [34]. This paper presents a CNN-based deep learning system for the automatic detection of maize leaves infected by _Spodoptera frugiperda_; from RGB UAV remote sensing images at high spatial resolution. UAV remote sensing images have excellent potential for agricultural data acquisition, while deep learning has agricultural data processing potential. Through the combination, this study is based on a multi-stage pest detection classification model applied to actual maize production environmental characteristics based on the ResNest model.