MSA-ResNet: A Neural Network for Fine-Grained Instar Identification of Spodoptera frugiperda Larvae in Smart Agriculture

The Spodoptera frugiperda (fall armyworm), a globally significant agricultural pest, poses severe threats to crop production. Accurate identification of larval instar stages is crucial for implementing precise control measures and reducing pesticide use. However, traditional identification methods suffer from low efficiency and heavy reliance on expert knowledge, while existing deep learning models still face challenges such as insufficient feature extraction and high computational complexity in fine-grained instar classification. To address these issues, this study proposes a novel network model, termed Multi-Scale Improved Self-Attention ResNet (MSA-ResNet), which integrates large convolutional kernels (LCK), atrous spatial pyramid pooling (ASPP), and an improved self-attention (ISA) mechanism into the ResNet50 backbone. These enhancements enable the model to more effectively capture and discriminate subtle morphological details of larvae. Experiments conducted on a self-constructed dataset comprising 24,179 images across six instar stages demonstrate that MSA-ResNet achieves an accuracy of 96.81% on the test set, significantly outperforming mainstream models such as ResNet50, VGG16, and MobileNetV3. In particular, the precision for the first instar increased by 12.94%, while the recall rates for the second and fourth instars improved by 16% and 8.97%, respectively. Ablation studies further validate the effectiveness of each module and the optimal embedding strategy. This research presents a high-precision and efficient intelligent solution for larval instar identification of S. frugiperda, offering a transferable reference for fine-grained image recognition tasks in agricultural pest management.