Transformer augmented hybrid deep learning for explainable multi class pest classification

Agricultural pests continue to impose serious threats to global food security by causing major yield losses across diverse cropping systems, making early and accurate identification vital for effective pest management. With the growing integration of digital technologies in modern agriculture, deep-learning-based pest recognition has become a promising approach to surpass the limitations of manual scouting and conventional monitoring practices. This work presents a comprehensive experimental evaluation of multiple deep-learning architectures for multi-class pest classification via image-level classification covering 19 pest species. The study investigates classical CNNs (MobileNetV2, VGG16), compound-scaled models (EfficientNetB0/B3, EfficientNetV2-B0), residual architectures (ResNet50), automated NAS models (Xception, NASNetLarge), and novel hybrid CNN-Transformer designs including Hybrid InceptionResNetV2, Hybrid ResNet50 + CBAM, Hybrid EffNet-Transformer, and Hybrid EfficientNetV2-S + Transformer. To enhance foreground isolation and reduce background complexity in field images, segmentation-driven preprocessing is employed using GrabCut, Watershed, SLIC, and Felzenszwalb, generating structure-refined image representations for downstream classification. Results show that attention-augmented hybrid models consistently outperform standalone CNNs, with the Hybrid EfficientNetV2-S + Transformer achieving the highest performance with 0.8800 validation accuracy, 0.849 macro-F1, and 0.4560 validation loss. These findings highlight the effectiveness of combining convolutional feature hierarchies with global self-attention for reliable multi-species pest classification and offer meaningful guidance for developing intelligent precision agriculture systems.