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

Sahila Beegum, Muhammad Adeel Hassan (), Purushothaman Ramamoorthy, Raju Bheemanahalli, Krishna N. Reddy, Vangimalla Reddy and Kambham Raja Reddy ()
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Sahila Beegum: Adaptive Cropping System Laboratory, USDA-ARS, Beltsville, MD 20705, USA
Muhammad Adeel Hassan: Adaptive Cropping System Laboratory, USDA-ARS, Beltsville, MD 20705, USA
Purushothaman Ramamoorthy: Geosystems Research Institute, Mississippi State University, Starkville, MS 39759, USA
Raju Bheemanahalli: Department of Plant and Soil Sciences, Mississippi State University, Starkville, MS 39762, USA
Krishna N. Reddy: USDA-ARS, Crop Production Systems Research Unit, 141 Experiment Station Road, P.O. Box 350, Stoneville, MS 38776, USA
Vangimalla Reddy: Adaptive Cropping System Laboratory, USDA-ARS, Beltsville, MD 20705, USA
Kambham Raja Reddy: Department of Plant and Soil Sciences, Mississippi State University, Starkville, MS 39762, USA

Agriculture, 2024, vol. 14, issue 7, 1-15

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 R 2 = 0.66), enabling better detection of phenotypic variations ( p < 0.05) among the varieties compared to physiological-related traits (from R 2 = 0.21 to R 2 = 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.

Keywords: fiber quality; physiological traits; reflectance; remote sensing; vegetation index (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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