The original data used in this study. Article Swipe

Xinrui Wang (488330) , Yingming Shen (18982004) , Peng Tian (119465) , Mengyao Wu (808514) , Zhaowen Li (3727747) , Jiawei Zhao (363439) , Jihong Sun (344786) , Ye Qian (7400186) ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.1371/journal.pone.0322851.s001

This study aims to address the challenge of monitoring Plant Height (PH), SPAD, Leaf Area Index (LAI), and Above-Ground Biomass (AGB) in Gerbera under greenhouse cultivation conditions. We initially gathered multi-spectral images and corresponding ground truth data of these parameters at various growth stages using a low-altitude UAV. From the collected images, we derived five Vegetation Indices (VIs): NDVI, GNDVI, LCI, NDRE, and OSAVI, and extracted their textural features as fusion features. An adaptive ensemble model, OBM-RFEcv, was then developed by integrating six base models (Linear Regression, Decision Tree Regressor, Random Forest Regressor, XGBoost Regressor, and Support Vector Regressor) with Recursive Feature Elimination (RFE) to predict the key growth indicators. The results indicate that the OBM-RFEcv model outperforms the other models when using the fusion of the five VIs, particularly in the test dataset, where it achieved the highest accuracy for PH (NDVI), SPAD (GNDVI), LAI (GNDVI), and AGB (NDRE) with R² values of 0.92, 0.90, 0.89, and 0.93, respectively. The root mean square error (RMSE) values were 0.04, 0.07, 0.08, and 0.07, respectively, showing improvements over the best individual model by 0.01, 0.03, 0.01, and 0.09 in R², and reductions in RMSE by 0.01, 0.07, 0.08, and 0.03, respectively. These findings confirm that the OBM-RFEcv model, based on multi-spectral image fusion, effectively monitors key growth indicators in Gerbera, providing a non-invasive and precise method for greenhouse crop monitoring.

Related Topics

Normalized Difference Vegetation Index

Computer Science

Artificial Intelligence

Biomass (Ecology)

Gerbera

Support Vector Machine

Agricultural Engineering

Concepts

Mean squared error Greenhouse Key (lock) Vegetation (pathology) Random forest Ground truth Decision tree Mathematics Sensor fusion Leaf area index Feature (linguistics) Tree (set theory) Remote sensing Statistics Data mining Normalized Difference Vegetation Index Index (typography) Computer science Artificial intelligence Biomass (ecology) Vegetation Index Gerbera Data modeling Support vector machine Information gain ratio Base (topology) Agricultural engineering

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OpenAlex ID: https://openalex.org/W7111123978

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DOI: https://doi.org/10.1371/journal.pone.0322851.s001

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Title: The original data used in this study.

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Publication year: 2025

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Publication date: 2025-05-20

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Authors: Xinrui Wang (488330), Yingming Shen (18982004), Peng Tian (119465), Mengyao Wu (808514), Zhaowen Li (3727747), Jiawei Zhao (363439), Jihong Sun (344786), Ye Qian (7400186)

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Concepts: Mean squared error, Greenhouse, Key (lock), Vegetation (pathology), Random forest, Ground truth, Decision tree, Mathematics, Sensor fusion, Leaf area index, Feature (linguistics), Tree (set theory), Remote sensing, Statistics, Data mining, Normalized Difference Vegetation Index, Index (typography), Computer science, Artificial intelligence, Biomass (ecology), Vegetation Index, Gerbera, Data modeling, Support vector machine, Information gain ratio, Base (topology), Agricultural engineering

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abstract_inverted_index.monitoring.	229
abstract_inverted_index.outperforms	118
abstract_inverted_index.Above-Ground	19
abstract_inverted_index.improvements	176
abstract_inverted_index.low-altitude	47
abstract_inverted_index.non-invasive	222
abstract_inverted_index.particularly	130
abstract_inverted_index.R<sup>2</sup>	152
abstract_inverted_index.corresponding	34
abstract_inverted_index.respectively,	174
abstract_inverted_index.respectively.	160, 200
abstract_inverted_index.R<sup>2</sup>,	189
abstract_inverted_index.multi-spectral	31, 210
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	8
citation_normalized_percentile