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

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...