Objective To guide the rational application of pesticides, this study aims to develop a simple and reliable detection method to acquire real-time information on the deposition and distribution of pesticide droplet on crop leaves.

Method This study proposed a method for detecting foliar fluorescent droplet deposition based on an improved YOLOv8. By screening fluorescent tracers and optimizing their mass concentrations, suitable experimental conditions for leaf droplet image acquisition were determined, and a corresponding dataset was constructed. Based on YOLOv8-seg, the AdamW optimizer was introduced, an efficient multi-scale attention (EMA) mechanism was embedded into the backbone network, and a semantics and detail infusion (SDI) module was incorporated into the neck structure to enhance detection performance.

Result The fluorescence tracer screening results showed that a 1.0 g/L fluorescein (a yellow-green fluorescent tracer) solution clearly revealed the spatial distribution of droplets on leaf surfaces. Field experiment results demonstrated that the improved model achieved mAP@0.50 and mAP@0.50–0.95 values of 95.4% and 73.1%, respectively, in object detection tasks. For segmentation mask evaluation, the mAP@0.50 and mAP@0.50–0.95 reached 92.5% and 61.3%, respectively, outperforming the baseline model in overall performance.

Conclusion The improved YOLOv8 based method for fluorescent droplet deposition detection on leaf surface enables accurate droplet recognition and distribution analysis. It features simple operation and high stability, offering technical support for spray quality assessment and precision pesticide application