Introduction to the five VIs text. 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.t001

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

Data Mining

Agricultural Engineering

Artificial Intelligence

Forestry

Support Vector Machine

Concepts

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

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OpenAlex ID: https://openalex.org/W7110961679

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DOI: https://doi.org/10.1371/journal.pone.0322851.t001

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Title: Introduction to the five VIs text.

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

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abstract_inverted_index.Elimination	103
abstract_inverted_index.Regression,	87
abstract_inverted_index.conditions.	27
abstract_inverted_index.cultivation	26
abstract_inverted_index.effectively	213
abstract_inverted_index.indicators.	110
abstract_inverted_index.integrating	82
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