TEA-LWNet: a lightweight multispectral feature enhancement network based on RGB–NIR fusion for detection of tea leaf diseases and pests

To address the low contrast and mild blur in aerial tea plantation imagery, this study introduces an RGB–NIR dataset composed of UAV-acquired RGB and near-infrared images. The RGB–NIR representation provides complementary visible and near-infrared information, enhancing the contrast between damaged and healthy leaf regions while reducing interference caused by shadows and highlights. This improves the separability of small pest-damaged areas and early disease lesions in weakly textured tea canopy scenes. Based on this, we propose TEA-LWNet, a lightweight multi-scale detection framework. Its backbone, Multi-FENet, leverages a DS-MBS module for simultaneous high-order semantic expansion and low-level detail preservation. The neck features an Adaptive Enhancement (AE) module for spatial-semantic alignment and an MDFF structure with embedded Gated Fusion (GF) to suppress background diffusion during upsampling. Optimization is driven by SAB-Loss—combining IoU, Distributed Focal Loss, and BCE—enforcing scale-invariant geometric constraints and sub-pixel boundary calibration to prioritize small target learning. Evaluations on a self-built RGB–NIR dataset demonstrate that TEA-LWNet achieves an mAP@0.5 of 93.48% under strict parameter and FLOP constraints. Ablation studies confirm the functional complementarity of the proposed modules in cross-layer noise suppression and boundary refinement. Overall, TEA-LWNet achieves a favorable balance between detection accuracy and model complexity, indicating its potential for resource-constrained agricultural monitoring systems. However, its runtime performance on UAV or embedded edge hardware remains to be further validated.