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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Adaptive Cropping Systems Laboratory » Research » Publications at this Location » Publication #415809
Research Project: Sustainable and Resilient Crop Production Systems Based on the Quantification and Modeling of Genetic, Environment, and Management Factors

Location: Adaptive Cropping Systems Laboratory

Title: Hyperspectral-based high-throughput phenotyping to assess water use efficiency in cotton

Author
item BEEGUM, SAHILA - University Of Nebraska
item HASSAN, MUHAMMAD ADEEL - Orise Fellow
item RAMAMOORTHY, PURUSHOTHAMAN - Mississippi State University
item BHEEMANAHALLI, RAJU - Mississippi State University
item Reddy, Krishna
item Reddy, Vangimalla
item REDDY, KAMBHAM RAJA - Mississippi State University

Submitted to: Journal of Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/27/2024
Publication Date: 6/29/2024
Citation: Beegum, S., Hassan, M., Ramamoorthy, P., Bheemanahalli, R., Reddy, K.N., Reddy, V., Reddy, K. 2024. Hyperspectral-based high-throughput phenotyping to assess water use efficiency in cotton. Journal of Agriculture. 14(7):1054. https://doi.org/10.3390/agriculture14071054.
DOI: https://doi.org/10.3390/agriculture14071054

Interpretive Summary: This study investigates the use of hyperspectral remote sensing to enhance the breeding of climate-resilient cotton cultivars. By carrying out an experimental study on forty cotton cultivars, the study found that hyperspectral vegetation indices like SWRI and NDWI could be used to accurately assess water use efficiency and other crucial traits. The findings suggest that this technology allows for quick, accurate assessments of cultivar performance, aiding breeders in selecting cultivars with superior water use efficiency, yield, and fiber quality. This approach could significantly improve breeding strategies for cotton in the face of climate change.

Technical Abstract: Cotton is a pivotal global commodity, underscored by its economic value and widespread use. In the face of climate change, breeding resilient cultivars to variable environmental conditions becomes increasingly essential. However, the process of phenotyping, crucial for breeding efforts, is often considered a bottleneck because of the low throughput of traditional methods. To address this limitation, this study leverages hyperspectral remote sensing, a promising tool to assess crucial crop traits across forty cotton cultivars. Results from this study demonstrated the effectiveness of four vegetation indices (VIs) in evaluating these cultivars for water use efficiency (WUE). Prediction accuracy for WUE through VIs such as simple ratio water index (SRWI) and normalized difference water index (NDWI) was higher (up to R2 = 0.66), enabling better detection of phenotypic variations (.05) among the cultivars compared to physiological-related traits (from R2 = 0.21 to R2= 0.42) with high repeatability and 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 cultivars based on VIs, WUE, and fiber quality traits. This study demonstrated that the hyperspectral-based proximal sensing approach helps rapidly assess the in-season performance of cultivars for important traits and aids in precise breeding decisions for developing climate-resilient cotton cultivars.