Journal of Imaging

22 pages, 26190 KB

Open AccessArticle

Non-Destructive Mangosteen Volume Estimation via Multi-View Instance Segmentation and Hybrid Geometric Modeling

by Wattanapong Kurdthongmee, Arsanchai Sukkuea, Md Eshrat E Alahi and Qi Zeng

J. Imaging 2026, 12(1), 1; https://doi.org/10.3390/jimaging12010001 - 19 Dec 2025

In precision agriculture, accurate, non-destructive estimation of fruit volume is crucial for quality grading, yield prediction, and post-harvest management. While vision-based methods provided some usefulness, fruits with complex geometry—such as mangosteen (Garcinia mangostana L.)—are difficult due to their large calyx, which may [...] Read more.

In precision agriculture, accurate, non-destructive estimation of fruit volume is crucial for quality grading, yield prediction, and post-harvest management. While vision-based methods provided some usefulness, fruits with complex geometry—such as mangosteen (Garcinia mangostana L.)—are difficult due to their large calyx, which may lead to difficulties in solving using traditional form-modeling methods. Traditional geometric solutions such as ellipsoid approximations, diameter–height estimation, and shape-from-silhouette reconstruction often fail because the irregular calyx generates asymmetric protrusions that violate their basic form assumptions. We offer a novel study framework employing both multi-view instance segmentation and hybrid geometrical feature modeling to quantitatively model mangosteen volume with traditional 2D imaging. A You Only Look Once (YOLO)-based segmentation model was employed to explicitly separate the fruit body from the calyx. Calyx inclusion resulted in dense geometric noise and reduced model performance (

R^{2} < 0.40

). We trained eight regression models on a curated and augmented 900 image dataset (

N = 720

, test

N = 180

). The models used single-view and multi-view geometric regressors (

V \propto A^{1.5}

), polynomial hybrid configurations, ellipsoid-based approximations, as well as hybrid feature formulations. Multi-view models consistently outperformed single-view models, and the average predictive accuracy improved from

R^{2} = 0.6493

to

R^{2} = 0.7290

. The best model is indeed a hybrid linear regression model with side- and bottom-area features—(

A_{s}^{1.5}

,

A_{b}^{1.5}

)—combined with ellipsoid-derived volume estimation—(

V_{ellipsoid}

)—which resulted in

R^{2} = 0.7290

, a Mean Absolute Percentage Error (MAPE) of 16.04%, and a Root Mean Square Error (RMSE) of 31.9

{cm}^{3}

on the test set. These results confirm the proposed model as a low-cost, interpretable, and flexible model for real-time fruit volume estimation, ready for incorporation into automated sorting and grading systems integrated in post-harvest processing pipelines. Full article

(This article belongs to the Section Computer Vision and Pattern Recognition)

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Journal of Imaging

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