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Abstract Recent technological advances in cotton (Gossypium hirsutum L.) phenotyping have offered tools to improve the efficiency of data collection and analysis. High-throughput phenotyping (HTP) is a non-destructive and rapid approach of monitoring and measuring multiple phenotypic traits related to the growth, yield, and adaptation to biotic or abiotic stress. Researchers have conducted extensive experiments on HTP and developed techniques including spectral, fluorescence, thermal, and three-dimensional imaging to measure the morphological, physiological, and pathological resistance traits of cotton. In addition, ground-based and aerial-based platforms were also developed to aid in the implementation of these HTP systems. This review paper highlights the techniques and recent developments for HTP in cotton, reviews the potential applications according to morphological and physiological traits of cotton, and compares the advantages and limitations of these HTP systems when used in cotton cropping systems. Overall, the use of HTP has generated many opportunities to accurately and efficiently measure and analyze diverse traits of cotton. However, because of its relative novelty, HTP has some limitations that constrains the ability to take full advantage of what it can offer. These challenges need to be addressed to increase the accuracy and utility of HTP, which can be done by integrating analytical techniques for big data and continuous advances in imaging.

Abstract Background Producing rainfed cotton (Gossypium hirsutum L.) with high fiber quality has been challenging in the Texas High Plains because of extended periods of insufficient rainfall during sensitive boll developmental stages. Genetic variation created by Ethyl MethaneSulfonate (EMS) mutagen has successfully improved fiber quality of cotton. However, little is known about the effect of water deficit environments on fiber quality. Three EMS treated populations were advanced from the first to the fourth generation (M1 to M4) as bulk harvested populations. In 2014, single-plant divergent selection was applied based on perceived morphological and agronomic differences seen during and at the end of the season. Results Analyses from these selections in 2014–2016 showed significant (P