StraTracker: A dynamic counting method for growing strawberries based on multi-target tracking

Abstract

Accurately counting fruit in orchards is a critical step for effective digital farming management. However, the variability in fruit size, overlapping shadows, and light interference present significant challenges to applying computer vision during the strawberry growth phase. To address these challenges, we propose StraTracker, a multi-object tracking (MOT) algorithm specifically designed to identify and count strawberries at various growth stages. StraTracker transforms the counting task into a frame-by-frame tracking problem, integrating both motion and appearance features. The algorithm is composed of three key components: a strawberry detector based on YOLOv8n, a feature association module, and a dual-area counting (DC) module. First, the strawberry detector accurately recognizes five growth stages, achieving an average accuracy of 91.93 % at 38.3 FPS. Next, the feature association module, incorporating the Feature Slicing Attention (FSA) and Adaptive Kalman Filtering (AKF) modules, mitigates issues such as light interference, impractical tracking frames, and ID switching (IDs). As a result, StraTracker achieves a Multi-Object Tracking Accuracy (MOTA) of 83.28 % and a Higher-Order Tracking Accuracy (HOTA) of 77.26 %, with only 259 IDs, outperforming existing baseline models. Finally, the DC module categorizes fruit counts based on the unique IDs assigned during tracking. The algorithm’s coefficient of determination ($R^2$ = 0.91) and GEH of 2.33 indicate a strong correlation between predicted and actual counts. In conclusion, StraTracker offers a promising solution for farmers to optimize planting strategies and develop more precise harvesting plans.