Work

A Multidimensional Data Fusion Method Incorporating a Temporal Feature for Estimating Tomato Aboveground Biomass

Year: 2026

Type: article

Abstract: Aboveground biomass (AGB) serves as a key indicator of tomato seedling growth status and provides a critical basis for guiding precision management. However, existing AGB estimation methods are typically confined to single-time-point assessments and fail to account for phenotypic variations across different growth ages. To address this limitation, this study proposes a growth-stage-aware estimation method for tomato seedling AGB utilizing cross-sectional data collected across multiple growth stages. The proposed method constructs multidimensional input variables from stem phenotypic traits, RGB vegetation indices, and image texture features, and incorporates plant age (days after sowing) as a temporal feature to capture stage-dependent variations during growth. In this study, Bayesian Ridge Regression (BR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) were integrated into an ensemble learning framework to leverage the complementary strengths of these models. The proposed stacking ensemble approach yields improved estimation performance, showing potential to mitigate overfitting effects and prediction bias compared with single base models under this evaluation setting. The method achieved an R2 of 0.8559 and an RMSE of 0.0363 for tomato AGB estimation. This study provides a novel technical strategy for accurate and multidimensional growth status estimation of greenhouse tomato seedlings.

Source: Sensors

DOI: 10.3390/s26206563

Authors: Fahui Yuan, Rongtao Li, Yong He, Yufei Liu

Institutions: Zhejiang University, Ministry of Agriculture and Rural Affairs

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $3,137