2024/09/29 by Jiuzhou Lei, Lei, Jiuzhou, Ankit Prabhu +13
Agricultural and Biological Sciences · Biochemistry, Genetics and Molecular Biology · #FOS: Computer and information sciences #Horticultural and Viticultural Research #Plant Pathogens and Fungal Diseases #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.2409.19786
openalex publication_date 2024/09/29 · openalex created_date 2024/10/28 · openalex updated_date 2026/07/28
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 at https://4d-metric-semantic-mapping.org/