Nyéki, Anikó Éva and Daróczy, Bálint Zoltán and Neményi, Miklós and Ambrus, Bálint and Teschner, Gergely and Alahmad, Tarek (2025) Predicting maize growth and biomass: Integrating gradient boosted trees with sentinel images and IoT. PROGRESS IN AGRICULTURAL ENGINEERING SCIENCES, 2025. pp. 1-13. ISSN 1786-335X 10.1556/446.2025.00202

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Abstract

Agricultural big data and high-performance computing have significantly improved crop yield modeling. Maize growth dynamics and yield prediction are crucial for sustainable agriculture. This study introduces an advanced modeling approach utilizing Gradient Boosted Decision Trees (GBDT) combined with a feature selection strategy to predict maize biomass production. A dataset of 200 unique maize plants was observed throughout the vegetation season. Our approach integrates manual measurements, meteorological data, and vegetation indices along with Internet of Things (IoT) field sensors to perform spatio-temporal analysis. Results indicate that maize stalk thickness and height are the most reliable predictors of biomass yield, while environmental variables show minimal impact. The most effective model, period-dependent GBDT, demonstrated superior predictive performance, achieving an average error of 4.39 mm in plant growth predictions. Notably, stalk thickness and height can be estimated six weeks before harvest, while biomass yield two weeks before harvest. This research underscores the potential of machine learning and remote sensing to enhance precision agriculture decision-making.

Item Type:	Article
Subjects:	Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions:	Artificial Intelligence Laboratory
SWORD Depositor:	MTMT Injector
Depositing User:	MTMT Injector
Date Deposited:	13 Jan 2026 16:18
Last Modified:	13 Jan 2026 16:18
URI:	https://eprints.sztaki.hu/id/eprint/11055

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