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Sustainability
Volume 17
Issue 22
10.3390/su172210120
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Tomato Growth Monitoring and Phenological Analysis Using Deep Learning-Based Instance Segmentation and 3D Point Cloud Reconstruction

by
Warut Timprae
1,
Tatsuki Sagawa
2,
Stefan Baar
1,
Satoshi Kondo
1,
Yoshifumi Okada
1,
Kazuhiko Sato
1,
Poltak Sandro Rumahorbo
1,
Yan Lyu
1,
Kyuki Shibuya
3,
Yoshiki Gama
3,
Yoshiki Hatanaka
3 and
Shinya Watanabe
1,*
1
Graduate School of Engineering, Muroran Institute of Technology, 27-1 Mizumoto-cho, Muroran 050-8585, Japan
2
Hitachi Solutions, Co., Ltd., 4-12-7 Higashishinagawa, Shinagawa-ku, Tokyo 140-0002, Japan
3
Asai Nursery, Inc., Tsu 514-2221, Japan
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(22), 10120; https://doi.org/10.3390/su172210120 (registering DOI)
Submission received: 2 October 2025 / Revised: 7 November 2025 / Accepted: 7 November 2025 / Published: 12 November 2025
(This article belongs to the Special Issue Green Technology and Biological Approaches to Sustainable Agriculture)
Review Reports Versions Notes

Abstract

Accurate and nondestructive monitoring of tomato growth is essential for large-scale greenhouse production; however, it remains challenging for small-fruited cultivars such as cherry tomatoes. Traditional 2D image analysis often fails to capture precise morphological traits, limiting its usefulness in growth modeling and yield estimation. This study proposes an automated phenotyping framework that integrates deep learning-based instance segmentation with high-resolution 3D point cloud reconstruction and ellipsoid fitting to estimate fruit size and ripeness from daily video recordings. These techniques enable accurate camera pose estimation and dense geometric reconstruction (via SfM and MVS), while Nerfacto enhances surface continuity and photorealistic fidelity, resulting in highly precise and visually consistent 3D representations. The reconstructed models are followed by CIELAB color analysis and logistic curve fitting to characterize the growth dynamics. When applied to real greenhouse conditions, the method achieved an average size estimation error of 8.01% compared to manual caliper measurements. During summer, the maximum growth rate (gmax) of size and ripeness were 24.14%, and 95.24% higher than in winter, respectively. Seasonal analysis revealed that winter-grown tomatoes matured approximately 10 days later than summer-grown fruits, highlighting environmental influences on phenological development. By enabling precise, noninvasive tracking of size and ripeness progression, this approach is a novel tool for smart and sustainable agriculture.
Keywords: tomato growth estimation; instance segmentation; ellipsoid fitting; photogrammetry tomato growth estimation; instance segmentation; ellipsoid fitting; photogrammetry

Share and Cite

MDPI and ACS Style

Timprae, W.; Sagawa, T.; Baar, S.; Kondo, S.; Okada, Y.; Sato, K.; Rumahorbo, P.S.; Lyu, Y.; Shibuya, K.; Gama, Y.; et al. Tomato Growth Monitoring and Phenological Analysis Using Deep Learning-Based Instance Segmentation and 3D Point Cloud Reconstruction. Sustainability 2025, 17, 10120. https://doi.org/10.3390/su172210120

AMA Style

Timprae W, Sagawa T, Baar S, Kondo S, Okada Y, Sato K, Rumahorbo PS, Lyu Y, Shibuya K, Gama Y, et al. Tomato Growth Monitoring and Phenological Analysis Using Deep Learning-Based Instance Segmentation and 3D Point Cloud Reconstruction. Sustainability. 2025; 17(22):10120. https://doi.org/10.3390/su172210120

Chicago/Turabian Style

Timprae, Warut, Tatsuki Sagawa, Stefan Baar, Satoshi Kondo, Yoshifumi Okada, Kazuhiko Sato, Poltak Sandro Rumahorbo, Yan Lyu, Kyuki Shibuya, Yoshiki Gama, and et al. 2025. "Tomato Growth Monitoring and Phenological Analysis Using Deep Learning-Based Instance Segmentation and 3D Point Cloud Reconstruction" Sustainability 17, no. 22: 10120. https://doi.org/10.3390/su172210120

APA Style

Timprae, W., Sagawa, T., Baar, S., Kondo, S., Okada, Y., Sato, K., Rumahorbo, P. S., Lyu, Y., Shibuya, K., Gama, Y., Hatanaka, Y., & Watanabe, S. (2025). Tomato Growth Monitoring and Phenological Analysis Using Deep Learning-Based Instance Segmentation and 3D Point Cloud Reconstruction. Sustainability, 17(22), 10120. https://doi.org/10.3390/su172210120

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