Tehnicki Glasnik | 2026
Authors: Radočaj D.; Subašić D.G.; Plaščak I.; Jurišić M.
DOI: 10.31803/tg-20241002104404
Journal: Tehnicki Glasnik
Year: 2026
Publisher: University North
Document Type: Article
Open Access: All Open Access; Gold Open Access
Cited by: 0
Various factors that influence chlorophyll levels in indoor plants were analysed. The input dataset consisted of 52 samples, which represent 10 distinct plant types. To non-invasively measure the chlorophyll content in plant leaves, Soil-Plant Analysis Development (SPAD) sensor was used, measuring the absorbance of specific light wavelengths, allowing for the assessment of chlorophyll concentration. The dataset was supplemented by covariates from soil electrical conductivity (EC) sensing at depths of 5 cm, 10 cm, and 15 cm, along with the multispectral Plant-O-Meter sensor. Four covariates in the model, including plant type, EC (5 cm), EC (15 cm), and normalized difference red-edge index (NDRE), showed minimal correlation with other variables, highlighting their independence. To predict leaf chlorophyll content, Random Forest and Extreme Gradient Boosting machine learning models were utilized, with Random Forest achieving higher average coefficient of determination of 0.458. The study underscored the potential of a complementary dataset for evaluating the complex relationship among root-soil dynamics and leaf for optimizing indoor plant health. © 2026 University North. All rights reserved.
extreme gradient boosting; multispectral sensor; random forest; soil electroconductivity; Soil-Plant Analysis Development (SPAD)