Optimizing Plant Production Through Drone-Based Remote Sensing and Label-Free Instance Segmentation for Individual Plant Phenotyping

Abstract

A crucial initial step for the automatic extraction of plant traits from imagery is the segmentation of individual plants. This is typically performed using supervised deep learning (DL) models, which require the creation of an annotated dataset for training, a time-consuming and labor-intensive process. In addition, the models are often only applicable to the conditions represented in the training data. In this study, we propose a pipeline for the automatic extraction of plant traits from high-resolution unmanned aerial vehicle (UAV)-based RGB imagery, applying Segment Anything Model 2.1 (SAM 2.1) for label-free segmentation. To prevent the segmentation of irrelevant objects such as soil or weeds, the model is guided using point prompts, which correspond to local maxima in the canopy height model (CHM). The pipeline was used to measure the crown diameter of approximately 15000 ball-shaped chrysanthemums (Chrysanthemum morifolium (Ramat)) in a 6158 m2 field on two dates. Nearly all plants were successfully segmented, resulting in a recall of 96.86%, a precision of 99.96%, and an F1 score of 98.38%. The estimated diameters showed strong agreement with manual measurements. The results demonstrate the potential of the proposed pipeline for accurate plant trait extraction across varying field conditions without the need for model training or data annotation.

Plant phenotyping relevance

UAV画像から個体分割と植物形質（冠径）を自動抽出する手法の開発・評価が研究の中心であり、精度指標と手動測定との一致も検証している。

Code and data availability

The supplied blocks describe a UAV/SAM 2.1 chrysanthemum phenotyping pipeline, but contain no authors' public dataset, imagery, code repository, or trained model release. All URLs in the text are generic library/citation links (SAM 2 arXiv, Rasterio, Shapely Zenodo, QGIS), not paper-specific assets.

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