Research on UAV Agricultural Residue Detection Technology

Pesticide residue has become a key challenge to agricultural product quality and environmental health. Traditional detection methods, which rely heavily on laboratory chemical analysis, are limited by low sampling efficiency, long processing times, and restricted spatial coverage, making them unsuitable for large-scale, rapid monitoring. To address these issues, this study developed a UAV-based pesticide residue detection technology. A system integrating remote sensing, sensors, and AI algorithms was constructed, and a corresponding detection model was established. A multi-source data fusion strategy combined with intelligent algorithms was proposed to enable fast identification and quantitative analysis of pesticide residues. Experimental results show that this approach reduces detection time from days to hours, achieves over 90% accuracy, lowers pesticide use by 15–20%, and decreases per-unit-area detection costs by about 75% compared with traditional methods. Comparative analysis highlights UAV detection’s advantages in efficiency and spatial coverage, while challenges remain in improving accuracy and environmental adaptability. Further optimization is needed to support broader applications.