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.

Background:Cotton fiber maturity is an important property that partially determines the processing and performance of cotton.Due to difficulties of obtaining fiber maturity values accurately from every plant of a genetic population,cotton geneticists often use micronaire(MIC) and/or lint percentage for classifying immature phenotypes from mature fiber phenotyp es although they are complex fiber traits.The recent development of an algorithm for determining cotton fiber maturity(M_(IR))from Fourier transform infrared(FT-IR)spectra explores a novel way to measure fiber maturity efficiently and accurately.However,the algorithm has not been tested with a genetic population consisting of a large number of progeny pla,nts.Results:The merits and limits of the MIC-or lint percentage-bas ed phenotyping method were demonstrated by comparing the observed phenotypes with the predicted phenotypes based on their DNA marker genotypes in a genetic population consisting of 708 F_2 plants with various fiber maturity.The observed MIC-based fiber phenotypes matched to the predicted phenotypes better than the observed lint percenta ge-based fiber phenotypes.The lint percentage was obtained from each of F_2 plants,whereas the MIC values were unable to be obtained from the entire population since certain F_2 plants produced insufficient fiber mass for their measurements.To test the feasibiility of cotton fiber infrared maturity(M_(IR))as a viable phenotyping tool for genetic analyses,we me asured FT-IR spectra from the second population composed of 80 F_2 plants with various fiber maturities,determined M_(IR) values using the algorithms,and compared them with their genotypes in addition to other fiber phenotypes.The results showed that M_(IR) values were successfully obtained from each of the F_2 plants,and the observed M_(IR)-based phenotypes fit well to the predicted phenotypes based on their DNA marker genotypes as well as the observed phenotypes based on a combination of MIC and lint percentage.Conclusions:The M,R value obtained from FT-IR spectra of cotton fibers is able to accurately assess fiber maturity of all plants of a population in a quantitative way.The technique provides an option for cotton geneticists to determine fiber maturity rapidly and efficiently.

Background:Manganese(Mn) is an essential microelement in cottonseeds,which is usually determined by the techniques relied on hazardous reagents and complex pretreatment procedures.Therefore a rapid,low-cost,and reagent-free analytical way is demanded to substitute the traditional analytical method.Results:The Mn content in cottonseed meal was investigated by near-infrared spectroscopy(NIRS) and chemometrics techniques.Standard normal variate(SNV) combined with first derivatives(FD) was the optimal spectra pre-treatment method.Monte Carlo uninformative variable elimination(MCUVE) and successive projections algorithm method(SPA)were employed to extract the informative variables from the full NIR spectra.The linear and nonlinear calibration models for cottonseed Mn content were developed.Finally,the optimal model for cottonseed Mn content was obtained by MCUVE-SPA-LSSVM,with root mean squares error of prediction(RMSEP) of 1.994 6,coefficient of determination(R2) of 0.949 3,and the residual predictive deviation(RPD) of 4.370 5,respectively.Conclusions:The MCUVE-SPA-LSSVM model is accuracy enough to measure the Mn content in cottonseed meal,which can be used as an alternative way to substitute for traditional analytical method.