Enhanced grape tracking (using deep neural networks) with an extended matching algorithm for SORT and DeepSORT

Vineyard managers traditionally count grape clusters manually for yield estimation, a process that is both time-consuming and labor-intensive. Recent advances in computer vision enable autonomous tracking, yet state-of-the-art methods often rely on re-identification networks that require expensive, hard-to-obtain instance ID annotations. This study addresses this challenge by evaluating the real-time tracking performance and counting accuracy of SORT, DeepSORT, ByteTrack, and the newly proposed SORT+ and DeepSORT+ algorithms. SORT+ and DeepSORT+ incorporate a novel matching cascade that leverages the complementary strengths of Mahalanobis, IoU, and Euclidean distances. Crucially, this approach allows SORT+ to achieve robust performance without the need for additional training data. The extended matching cascade offers large improvements for SORT, making the training-free SORT+ comparable to the deep-learning-based DeepSORT. It increases MOTA and IDF1 by 5% to 6%, while decreasing ID switches by 62%. SORT+ improves the counting accuracy from 33% to 96%. DeepSORT+ shows further performance gains, decreasing ID switches by 12% compared to DeepSORT. This work illustrates the feasibility of using unmanned aerial vehicles (UAVs) to autonomously track and count grape clusters in challenging real-world vineyard settings. By potentially improving yield estimation and non-destructive robotic farming, these findings support sustainable farming practices and economic growth.