Improved Self-Attention for Spodoptera Frugiperda Larval Instar Stages Identification

Spodoptera frugiperda (fall armyworm, FAW) is a pest that poses a significant threat to global agriculture, with its larvae exhibiting unique morphological characteristics and varying degrees of harm at different instar stages. This study proposes a method that incorporates an improved self-attention mechanism to achieve rapid and accurate identification of FAW larval instars. This method can be seamlessly integrated into convolutional neural networks (CNNs), enhancing recognition performance by focusing on the global features of images. Furthermore, this research has constructed an image dataset of FAW larvae at various developmental stages under field and laboratory conditions to validate the effectiveness of the proposed method. Experimental results demonstrate that our method improves the recognition accuracy across various CNN architectures, achieving accuracy enhancements of 5.49%, 7.38%, 5.79%, and 5.59% on Alexnet, vgg16, resnet50, and resnet101, respectively. Our approach provides technicians with a convenient, intelligent, and precise tool to identify FAW larval instar stages.