Smartphone-based 3D imaging for canopy and berry cluster volume estimation in wine grapes

Abstract

• A smartphone-based 3D imaging approach was developed for canopy and berry cluster volume estimation in vineyards. • Machine learning-based segmentation using Gradient Boosting for canopy and YOLO11 with Structure from Motion (SfM) for clusters enabled accurate feature extraction. • Point cloud processing was utilized for accurate surface reconstruction and volume estimation. • The method provides an affordable and accessible alternative to traditional high-cost sensors for precision viticulture applications. Accurate estimation of vine canopy and berry cluster volumes is essential for precision viticulture, as it supports better vineyard management, yield prediction, and resource allocation. Traditional methods, such as manual measurements or expensive sensor-based systems, are often inaccessible to small and mid-scale growers. This study explores the use of smartphone-based 3D imaging and advanced machine learning techniques as an affordable and accessible alternative for estimating wine grape canopy and berry cluster volumes. In this study, point cloud data was collected using an iPhone 14 Pro Max to capture the spatial structure of grape canopies and berry clusters. Two separate datasets were used to evaluate canopy and cluster volumes independently, ensuring comprehensive analysis and validation. Canopy volume estimation involved segmentation using the Gradient Boosting Classifier, followed by computation of 3D point volumes, achieving an RMSE of 0.23 m³ and 98% classification accuracy for canopy point clouds. Berry clusters were segmented using YOLO11, and 3D point clouds were reconstructed using Structure from Motion (SfM) to create watertight meshes. Cluster volumes validated by water‑displacement ground truth yielded an RMSE of 14.68 cm. These findings demonstrate the potential of smartphone-based solutions to support precision viticulture through accurate estimation of vine canopies and berry clusters, which is expected to enhance vineyard productivity and berry quality.

Plant phenotyping relevance

スマートフォン3D画像、機械学習セグメンテーション、SfM、点群処理を用いてブドウ樹冠・果房体積を推定し、実測値で検証する手法開発が中心である。

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