RFEcv algorithm flowchart.

<div><p>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 param...

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Bibliographic Details
Main Author:	Xinrui Wang (488330) (author)
Other Authors:	Yingming Shen (18982004) (author), Peng Tian (119465) (author), Mengyao Wu (808514) (author), Zhaowen Li (3727747) (author), Jiawei Zhao (363439) (author), Jihong Sun (344786) (author), Ye Qian (7400186) (author)
Published:	2025
Subjects:	Ecology Inorganic Chemistry Plant Biology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified recursive feature elimination monitoring plant height leaf area index key growth indicators greenhouse cultivation conditions support vector regressor random forest regressor decision tree regressor best individual model adaptive ensemble model ndvi ), spad vis ): ndvi initially gathered multi div >< p 2 </ sup rfecv model outperforms ph ), spad spectral image fusion gndvi ), lai gerbera using multi lai ), xgboost regressor rfecv model gndvi ), spectral images five vis textural features test dataset study aims showing improvements results indicate precise method linear regression highest accuracy ground biomass fusion features findings confirm collected images altitude uav
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RFEcv algorithm flowchart.

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