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

Abstract

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/

Plant phenotyping relevance

果実の成長追跡・計数・サイズ推定という植物器官形質を対象に、LiDAR-RGB融合と4D対応付け手法を開発・評価し、データセットも公開しているため、フェノタイピング手法が中心です。

Code and data availability

植物フェノタイピング解析を再現する公開資産であることを、入力本文と直接リンクから確認できなかったため保留しました。

No evidence-backed public reproduction asset is currently recorded.