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 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 semantic and detail infusion (SDI) feature fusion 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 proposed foliar fluorescent droplet deposition detection method based on the improved YOLOv8 enables effective acquisition and accurate identification of droplet deposition images on leaf surfaces. The method is simple, reliable, and provides a practical solution for spray deposition evaluation and precision plant protection applications.