Lightweight Vision Transformer with Lite-AVPSO Hyperparameter Optimization for Agricultural Disease Recognition

Plant disease identification and management are vital for crop protection, productivity, and sustainable agriculture. This paper presents RepAgrViT, a lightweight vision transformer architecture for agricultural disease recognition in Internet of Things edge environments. The proposed model integrates CNN with vision transformer principles through a novel dual-stream design comprising token mixer and channel mixer arranged in series. A Bilinear Attention Transformation module performs extensive local-global attention via direct feature position connections. This enables comprehensive capture of long-range dependencies between healthy and diseased leaf regions. We introduce an efficient parameter fusion methodology that optimizes depthwise separable convolutions while integrating normalized weights with biases, reducing model complexity. Furthermore, we propose Lite-AVPSO, a hyperparameter optimization algorithm for precise model configuration. It incorporates adaptive weighted delayed velocity optimization and neighborhood-based local search strategies. Visualization techniques using multi-channel color heatmaps and three-dimensional feature spaces provide interpretable diagnostic results. Experiments across diverse plant disease datasets demonstrate RepAgrViT efficacy in capturing discriminative disease features while maintaining computational efficiency suitable for resource-constrained agricultural monitoring systems, establishing a foundation for efficient vision-based monitoring in real-time agricultural applications.