ToRLNet: A Lightweight Deep Learning Model for Tomato Detection and Quality Assessment Across Ripeness Stages
Huihui Sun, Xi Xi, Alex Wu, Rui-Feng Wang
Abstract
This study proposes ToRLNet, a lightweight tomato ripeness detector designed for real-time deployment in resource-constrained agricultural settings. Built on YOLOv12n, ToRLNet integrates three self-constructed modules (WaveFusionNet for frequency–spatial enhancement and feature extraction, ETomS for efficient context-aware encoding, and SFAConv for selective multi-scale downsampling) to address subtle inter-stage color transitions, small fruit instances, and cluttered canopies. We benchmark ToRLNet against lightweight and small-scale YOLO baselines (YOLOv8–YOLOv12) and conduct controlled ablations isolating each module’s contribution. ToRLNet attains Precision 90.27%, Recall 86.77%, F1-score 88.49%, mAP50 91.76%, and mAP 78.01% with only 6.9 GFLOPs, outperforming representative nano/small YOLO variants under comparable compute budgets. Ablation results show WaveFusionNet improves spectral–textural robustness, ETomS balances the precision–recall trade-off while reducing redundancy, and SFAConv preserves fine chromatic gradients and boundary structure during downsampling; their combination yields the most balanced performance. These findings demonstrate that ToRLNet delivers a favorable accuracy–efficiency trade-off and provides a practical foundation for on-board perception in automated harvesting, yield estimation, and greenhouse management.