Visible-Near Infrared and Mid Infrared Spectroscopy for Rapid Nutrient Profiling: A Comparative Assessment and Model Transferability Using Fresh and Dry-Ground Plant Tissues in Cotton

ORCID

Silva: https://orcid.org/0009-0006-4333-5128; Wijewardane: https://orcid.org/0000-0001-8962-9451; Bheemanahalli: https://orcid.org/0000-0002-9325-4901

MSU Affiliation

College of Agriculture and Life Sciences; Department of Plant and Soil Sciences; Department of Agricultural and Biological Engineering; James Worth Bagley College of Engineering

Creation Date

2026-06-30

Abstract

Cotton is the most extensively produced natural fiber worldwide, and its optimal yield relies on precise nutrient management throughout different growth stages. Traditionally, cotton nutrient estimation relies on laboratory-based analyses, which are time-consuming, costly, and destructive, which delay management decisions that influence final yield. Attenuated Total Reflectance (ATR) and Diffuse Reflectance (DR) spectroscopy provide rapid, cost-effective, and environmentally friendly alternatives to conventional laboratory methods. This study assessed the feasibility of using different visible near-infrared (VisNIR) and mid-infrared (MIR) spectrometers to estimate 11 macro (N, P, K, Ca, Mg, S) and micronutrients (Fe, Mn, B, Cu, Zn) from fresh and dried cotton plant tissues. Three modeling techniques: Partial Least Squares Regression (PLSR), Cubist regression trees, and Support Vector Regression (SVR), were evaluated using 75% of the dataset for calibration. Among these, PLSR and Cubist produced comparable results; however, PLSR was selected for its faster computation. Dry leaf models using VisNIR resulted in high accuracy for all macronutrients (R2: 0.75–0.96) and several micronutrients, including B, Mn, and Cu (R2: 0.78–0.93). In contrast, fresh leaf models were less accurate due to moisture interference, limiting their feasibility for practical field applications. Models developed for stems and burs were less robust because of the limited number of samples. To address this, dry leaf datasets were spiked with extra weights using fresh leaf and dry stem datasets, which notably improved prediction accuracy. Combining VisNIR and MIR spectra did not enhance model performance, indicating that a single spectral region acquired with one spectrometer is sufficient for reliable and rapid nutrient estimation at the field level. This study demonstrated the effectiveness of MIR-ATR spectra for cotton nutrient prediction, despite challenges posed by moisture which has not been previously explored. The calibration transfer techniques can improve prediction robustness across tissues and fresh or dry conditions.

Publication Date

5-16-2026

Publication Title

Precision Agriculture

Publisher

Springer

Rights

© Springer Nature

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Digital Object Identifier (DOI)

https://doi.org/10.1007/s11119-026-10382-1