Global climate change and agricultural complexity have exacerbated crop diseases, posing significant threats to food security and agricultural productivity. Deep learning-based approaches offer promising solutions for the intelligent monitoring crop growth in real time. However, existing models often encounter excessive computational complexity, and their fine-grained features extraction accuracy is insufficient, hampering their deployment in resource-constrained agricultural environments. This study introduces LAF-YOLO, a novel lightweight network that balances detection accuracy with computational efficiency. The LAF-YOLO architecture incorporates two key innovations: a novel lightweight detection head (LGHD) that significantly reduces model parameters and computational load, and an advanced feature encoding and reconstruction module that integrates Haar Wavelet transform technology to enhance feature extraction efficiency while preserving high-resolution details. Experiments on a self-built reliable field complex background dataset encompassing nine major diseases of four primary crops demonstrate that LAF-YOLO is remarkably efficient, with only 1.97 million parameters and 4.5 GFLOPs. Achieving state-of-the-art mAP50, mAP50-95, and recall of 97.4%, 85.8%, and 94.0%, respectively. LAF-YOLO offers a robust solution for precise and efficient detection of crop diseases, contributing to sustainable crop health and food security management.