YieldNet: A Lightweight YOLOv8n Enhancement for Immature Green Tomato Detection in UAV Images: Real-Time Edge Demonstration Toward Pre-Harvest Yield Estimation

Accurate early-stage monitoring of greenhouse tomatoes is essential for reducing post-harvest losses, with reliable detection of immature green tomatoes being the core challenge. However, these fruits are small, heavily occluded, and chromatically highly similar to foliage, making detection from low-altitude UAV imagery extremely difficult, while onboard edge processors impose stringent power and weight constraints. To address this, we propose YieldNet, a lightweight framework that introduces targeted enhancements to vanilla YOLOv8n: the backbone is replaced with ShuffleNetV2 to reduce computation; Efficient Channel Attention (ECA) modules are embedded after the P3–P5 layers in the neck for channel recalibration; and PIoU v2 loss is adopted for bounding-box regression. This study focuses on immature green tomatoes in low-altitude UAV imagery and evaluates the detector on an RK3588 edge device. The model is evaluated on both a self-collected real-world UAV dataset comprising 600 low-altitude green-tomato images and a public multi-ripeness benchmark. Compared with the YOLOv8n baseline, YieldNet achieves relative improvements in mAP@50-95, Recall, and F1-score by 18.9%, 6.1%, and 5.8%, respectively, on the Tomato-Recog public validation set, and enhances Recall, F1-score, and Precision by relative gains of 4.3%, 4.0%, and 3.8%, respectively, on the GreenTomato-UAV validation set, while increasing parameters only from 3.0 M to 3.3 M and reducing FLOPs from 8.1 G to 8.0 G. A representative live camera-to-display reading on the Orange Pi 5 Max was 37.6 FPS with 86 ms end-to-end latency. YieldNet supplies countable detections for future pre-harvest yield-estimation studies; UAV flight deployment, fruit-size estimation, and harvest-weight validation were not evaluated.