Hyperspectral Reflectance-Based High Throughput Phenotyping to Assess Water-Use Efficiency in Cotton

Authors

Sahila Beegum, USDA Agricultural Research Service
Muhammad Adeel Hassan, USDA Agricultural Research Service
Purushothaman Ramamoorthy, Mississippi State University
Raju Bheemanahalli, Mississippi State University
Krishna N. Reddy, USDA Agricultural Research Service
Vangimalla Reddy, USDA Agricultural Research Service
Kambham Raja Reddy, Mississippi State University

ORCID

Bheemanahalli: https://orcid.org/0000-0002-9325-4901

MSU Affiliation

College of Agriculture and Life Sciences; Department of Plant and Soil Sciences; Geosystems Research Institute

Creation Date

2025-11-19

Abstract

Cotton is a pivotal global commodity underscored by its economic value and widespread use. In the face of climate change, breeding resilient cultivars for variable environmental conditions becomes increasingly essential. However, the process of phenotyping, crucial to breeding programs, is often viewed as a bottleneck due to the inefficiency of traditional, low-throughput methods. To address this limitation, this study utilizes hyperspectral remote sensing, a promising tool for assessing crucial crop traits across forty cotton varieties. The results from this study demonstrated the effectiveness of four vegetation indices (VIs) in evaluating these varieties for water-use efficiency (WUE). The prediction accuracy for WUE through VIs such as the simple ratio water index (SRWI) and normalized difference water index (NDWI) was higher (up to R² = 0.66), enabling better detection of phenotypic variations (p < 0.05) among the varieties compared to physiological-related traits (from R² = 0.21 to R² = 0.42), with high repeatability and a low RMSE. These VIs also showed high Pearson correlations with WUE (up to r = 0.81) and yield-related traits (up to r = 0.63). We also selected high-performing varieties based on the VIs, WUE, and fiber quality traits. This study demonstrated that the hyperspectral-based proximal sensing approach helps rapidly assess the in-season performance of varieties for imperative traits and aids in precise breeding decisions.

Publication Date

7-1-2024

Publication Title

Agriculture Switzerland

Publisher

MDPI

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Recommended Citation

Beegum, S.; Hassan, M.A.; Ramamoorthy, P.; Bheemanahalli, R.; Reddy, K.N.; Reddy, V.; Reddy, K.R. Hyperspectral Reflectance-Based High Throughput Phenotyping to Assess Water-Use Efficiency in Cotton. Agriculture 2024, 14, 1054. https://doi.org/10.3390/agriculture14071054