Spatio-Temporal Metric-Semantic Mapping for Persistent Orchard Monitoring: Method and Dataset

Monitoring orchards at the individual tree or fruit level throughout the growth season is crucial for plant phenotyping and horticultural resource optimization, such as chemical use and yield estimation. We present a 4D spatio-temporal metric-semantic mapping system that integrates multi-session measurements to track fruit growth over time. Our approach combines a LiDAR-RGB fusion module for 3D fruit localization with a 4D fruit association method leveraging positional, visual, and topology information for improved data association precision. Evaluated on real orchard data, our method achieves a 96.9% fruit counting accuracy for 1,790 apples across 60 trees, a mean fruit size estimation error of 1.1 cm, and a 23.7% improvement in 4D data association precision over baselines. We publicly release a multimodal dataset covering five fruit species across their growth seasons atthis https URL

@article{lei2025_2409.19786,
  title={ Spatio-Temporal Metric-Semantic Mapping for Persistent Orchard Monitoring: Method and Dataset },
  author={ Jiuzhou Lei and Ankit Prabhu and Xu Liu and Fernando Cladera and Mehrad Mortazavi and Reza Ehsani and Pratik Chaudhari and Vijay Kumar },
  journal={arXiv preprint arXiv:2409.19786},
  year={ 2025 }
}