DELP-YOLOv12: a lightweight deployable model for maize pest and disease detection

BACKGROUND: Maize is one of the world's most important cereal crops for both food and feed, yet its yield is severely threatened by pests and diseases. The complexity of field environments, the variability of illumination, and the diversity of pest and disease types make accurate detection a challenging task. Although existing maize pest and disease detection models have achieved substantial progress, they still face difficulties in small-object recognition, feature perception, and efficient deployment on edge devices. To address these limitations, this study proposes a deployable and lightweight detection framework, DELP-YOLOv12, based on the YOLOv12 architecture. RESULTS: A Dynamic RepConvBlock with NAM (DRN) module was designed and integrated into the C3k structure to form C3k_DRN, enabling multi-branch training and inference-time fusion to enhance both feature representation and computational efficiency. Additionally, a Lightweight Feature Enhancement Detection Head (LFEDH) was developed to refine feature extraction for small-scale pests and irregular lesions. To further improve feature discrimination, an Efficient Channel Attention (ECA) mechanism was incorporated to highlight lesion- and pest-related responses while suppressing background noise. Considering edge-device constraints, a Layer-Adaptive Sparsity for Magnitude-based Pruning (LAMP) strategy combined with fine-tuning was applied to compress the model while maintaining accuracy. Experimental results on the maize pest and disease dataset demonstrate that DELP-YOLOv12 achieved a precision of 94.1%, a recall of 89.6%, and an mAP50 of 94.2%, outperforming the baseline across all metrics. Meanwhile, the parameter count and computation cost were reduced by approximately 68% and 60%, respectively, while preserving real-time inference capability on embedded hardware such as the NVIDIA Jetson Orin NX. CONCLUSIONS: The proposed DELP-YOLOv12 effectively balances accuracy, efficiency, and deployability for field-based maize pest and disease detection. Its integration of DRN, LFEDH, and LAMP modules enhances recognition of small and irregular targets while maintaining low computational demand, offering a practical solution for real-time agricultural monitoring and intelligent pest management.