Rapid and inexpensive assessment of soil total iron using Nix Pro color sensor

• Type of Material: Article
• Authors: Gaurav Jha, University of California Davis; Debjani Sihi, Emory University; Biswanath Dari, North Carolina Agricultural and Technical State University; Harpreet Kaur, Emory University; Mallika Arudi Nocco, University of California Davis; April Ulery, New Mexico State University Las Cruces; Kevin Lombard, New Mexico State University Las Cruces
• Language: English
• Date: 2021-07-12
• Publisher: American Society of Agronomy; Crop Science Society of America; Soil Science Society of America
• Publication Version: Final Published Version
• Copyright Statement: © 2021 The Authors. Agricultural & Environmental Letters published by Wiley Periodicals LLC on behalf of American Society of Agronomy, Crop Science Society of America, andSoil Science Society of America
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1002/ael2.20050
• Title of Journal or Parent Work: Agricultural and Environmental Letters
• Volume: 6
• Issue: 3
• Grant/Funding Information: This research was supported in part by the University Research Committee of Emory University and Emory College of Arts and Sciences.
• Keywords: color sensor; Nix Pro; Nix Sensor Ltd; agriculture; soil iron content; prediction models
• Research Categories: Agriculture, Soil Science

Abstract

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 R2 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.