YOLOv8-SED for Tea Small Green Leafhopper Detection: A Handheld Prototype and Pilot Counting Study

Routine counting of the tea small green leafhopper (Empoasca onukii Matsuda) requires repeated visual inspection of the leaves, as adults and nymphs are small, mobile, and often partially concealed. A handheld device was developed in this study to take close-up pictures and run the optimized YOLOv8 detector (YOLOv8-SED) locally on a Raspberry Pi 4B. A macro lens and a fixed near-field LED were used to achieve a more stable imaging condition for single-leaf inspection. YOLOv8-SED introduces a C2f-SPD feature block, efficient channel attention (ECA), and DySample upsampling. Eight configurations were evaluated with five seeds on 785 annotated images. YOLOv8-SED achieved a mean precision, recall, F1, mAP@0.5 and mAP@0.5:0.95 of 90.3%, 97.7%, 93.9%, 95.3%, and 70.5%, respectively. Session-grouped retraining yielded 90.1% mAP@0.5, compared with 83.5% for YOLOv8n. The complete embedded image cycle averaged 1.34 s. Ten groups of 100 leaves were selected for paired field observations. Raw counts were distinguished from operator-assisted counts after offline duplicate and false-detection review by the same observer who knew the manual reference counts. The prototype supports local detection and offline recording; broader validation and complete survey-time measurements are needed before general deployment.