SASP: Segment any strawberry plant, an end-to-end strawberry canopy volume estimation

Abstract

This study presents an end-to-end workflow Segment Any Strawberry Plant (SASP) for estimating strawberry canopy volume from multi-view images. The approach utilizes several recent advances in computer vision and 3D reconstruction. First, a Planar-based Gaussian Splatting Reconstruction (PGSR) method is employed to generate high-fidelity 3D point clouds of strawberry plants, offering improved geometric consistency compared to standard 3D Gaussian Splatting. Next, the Segment Any 3D Gaussians (SAGA) framework is adapted with fully automated prompts derived from YOLO (You Only Look Once) detection and color-based prompt selection which can eliminate the need for manual user input in the segmentation process. The resulting point clouds of plant canopies are calculated via concave hull to estimate their volumes. A reference box of known volume is included in the scene as a calibration object, mapping computed volumes from the virtual 3D space into real-world measurements. Experimental evaluations show that the proposed method achieves high segmentation quality and offers volume estimates across multiple plant shapes. This end-to-end pipeline addresses both the labor-intensive nature of manual canopy measurements and the computational complexity of large-scale 3D reconstructions, offering a potential for high-throughput phenotyping and yield prediction in future strawberry cultivation studies.

Plant phenotyping relevance

イチゴ植物の3D画像から樹冠体積を推定するエンドツーエンド手法を開発・評価しており、植物形態形質の取得が研究の中心である。

Code and data availability

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