A Novel Object Detection Model Based on Faster R-CNN for Spodoptera frugiperda According to Feeding Trace of Corn Leaves

• Published in: Agriculture (Basel) Vol. 12; no. 2; p. 248
• Main Authors: Du, Lei, Sun, Yaqin, Chen, Shuo, Feng, Jiedong, Zhao, Yindi, Yan, Zhigang, Zhang, Xuewei, Bian, Yuchen
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2022
• Subjects: Ablation; Accuracy; agriculture; Cereal crops; Classification; Color imagery; Convolution; Corn; Costs; Crop diseases; Crop production; Crop yield; Crops; data collection; Datasets; Deep learning; Deformation effects; Error detection; Feeding; Formability; Generalized linear models; Image acquisition; Insects; juveniles; Laboratories; Localization; Machine learning; maize Spodoptera frugiperda; migratory behavior; Model accuracy; Monitoring; object detection; Object recognition; Pest control; Pests; Plant diseases; Remote sensing; rice; Spodoptera frugiperda; Unmanned aerial vehicles; Vegetation; Visual fields
• DOI: 10.3390/agriculture12020248
• ISSN: 2077-0472

The conventional method for crop insect detection based on visual judgment of the field is time-consuming, laborious, subjective, and error prone. The early detection and accurate localization of agricultural insect pests can significantly improve the effectiveness of pest control as well as reduce the costs, which has become an urgent demand for crop production. Maize Spodoptera frugiperda is a migratory agricultural pest that has severely decreased the yield of maize, rice, and other kinds of crops worldwide. To monitor the occurrences of maize Spodoptera frugiperda in a timely manner, an end-to-end Spodoptera frugiperda detection model termed the Pest Region-CNN (Pest R-CNN) was proposed based on the Faster Region-CNN (Faster R-CNN) model. Pest R-CNN was carried out according to the feeding traces of maize leaves by Spodoptera frugiperda. The proposed model was trained and validated using high-spatial-resolution red–green–blue (RGB) ortho-images acquired by an unmanned aerial vehicle (UAV). On the basis of the severity of feeding, the degree of Spodoptera frugiperda invasion severity was classified into the four classes of juvenile, minor, moderate, and severe. The degree of severity and specific feed location of S. frugiperda infestation can be determined and depicted in the frame forms using the proposed model. A mean average precision (mAP) of 43.6% was achieved by the proposed model on the test dataset, showing the great potential of deep learning object detection in pest monitoring. Compared with the Faster R-CNN and YOLOv5 model, the detection accuracy of the proposed model increased by 12% and 19%, respectively. Further ablation studies showed the effectives of channel and spatial attention, group convolution, deformable convolution, and the multi-scale aggregation strategy in the aspect of improving the accuracy of detection. The design methods of the object detection architecture could provide reference for other research. This is the first step in applying deep-learning object detection to S. frugiperda feeding trace, enabling the application of high-spatial-resolution RGB images obtained by UAVs to S. frugiperda-infested object detection. The proposed model will be beneficial with respect to S. frugiperda pest stress monitoring to realize precision pest control.