Open AccessRapid and inexpensive assessment of soil total iron using Nix Pro color sensor
Author(s) -
Jha Gaurav,
Sihi Debjani,
Dari Biswanath,
Kaur Harpreet,
Nocco Mallika Arudi,
Ulery April,
Lombard Kevin
Publication year - 2021
Publication title -
agricultural and environmental letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.681
H-Index - 12
ISSN - 2471-9625
DOI - 10.1002/ael2.20050
Subject(s) - cyan , magenta , rgb color model , color space , lightness , mean squared error , mathematics , water content , environmental science , soil science , statistics , computer science , artificial intelligence , engineering , physics , optics , speech recognition , geotechnical engineering , inkwell , image (mathematics)
In this study, an inexpensive Nix Pro (Nix Sensor Ltd.) color sensor was used to develop prediction models for soil iron (Fe) content. Thirty‐eight soil samples were collected from five agricultural fields across the Animas watershed to develop and validate soil Fe prediction models. We used color space models to develop three different parameter sets for Fe prediction with Nix Pro. The different color space sets were used to develop three new predictive models for Nix Pro‐based Fe content against the lab‐based inductively coupled plasma analyzed Fe content. The model performances were assessed using the coefficient of determination, root mean square error, and model p ‐value. Three models (International Commission on Illumination's lightness, ±a axis (redness to greenness), and ± b axis (yellowness to blueness) [CIEL*a*b]; red, green, blue [RGB]; and cyan, magenta, yellow, key [black] [CMYK]) were significant in predicting the Fe content using colorimetric variables with R 2 ranging from 0.79 to 0.81. The mean square prediction error (MSPE) and Kling–Gupta efficiency (KGE) Index were calculated to validate models and CMYK was predicted to be a better model (MSPE = 0.13; KGE = 0.601) than CIEL*a*b and RGB models. The results suggest Nix Pro is useful in predicting soil Fe content.