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Article

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

by
Sahila Beegum
1,2,
Muhammad Adeel Hassan
1,3,*,
Purushothaman Ramamoorthy
4,
Raju Bheemanahalli
5,
Krishna N. Reddy
6,
Vangimalla Reddy
1 and
Kambham Raja Reddy
5,*
1
Adaptive Cropping System Laboratory, USDA-ARS, Beltsville, MD 20705, USA
2
Nebraska Water Center, Robert B. Daugherty Water for Food Global Institute, 2021 Transformation Drive, University of Nebraska, Lincoln, NE 68588, USA
3
Oak Ridge Institute for Science and Education, Oak Ridge, TN 37830, USA
4
Geosystems Research Institute, Mississippi State University, Starkville, MS 39759, USA
5
Department of Plant and Soil Sciences, Mississippi State University, Starkville, MS 39762, USA
6
USDA-ARS, Crop Production Systems Research Unit, 141 Experiment Station Road, P.O. Box 350, Stoneville, MS 38776, USA
*
Authors to whom correspondence should be addressed.
Agriculture 2024, 14(7), 1054; https://doi.org/10.3390/agriculture14071054
Submission received: 31 May 2024 / Revised: 24 June 2024 / Accepted: 27 June 2024 / Published: 29 June 2024
(This article belongs to the Special Issue Smart Agriculture Sensors and Monitoring Systems for Field Detection)
Browse Figures
Versions Notes

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.

1. Introduction

Cotton production is vital to global economies, with the United States being a significant player. It contributes approximately 15% to the worldwide cotton production and about 35% to the value of the international cotton trade [1]. Abiotic stresses (e.g., drought and heat stresses) significantly impact cotton growth, development, fiber yield, and quality worldwide [2,3,4]. These stresses affect metabolic pathways and lead to the loss of squares and bolls, resulting in lower lint yields. They also influence several physiological processes, including photosynthesis, stomatal regulation, root–shoot growth, leaf area expansion, and transpiration. After rice and wheat, cotton is considered the most water-demanding, consuming nearly 11% of global irrigation water [5]. Cotton needs 700 to 1300 mm of water to meet its requirements, which can vary with the climate and length of the total growing period (https://www.fao.org/, accessed on 3 June 2024). In addition, the availability of irrigation water for agriculture is predicted to decrease in the future due to climate change [6]. Consequently, there is a pressing need to identify cotton varieties with advanced physiological traits such as water-use efficiency (WUE) to withstand water stresses and sustain production [7].
The development of resource-efficient cultivars is the main priority of scientists in coping with future climate change severity. For this, crop breeders need to assess critical traits such as WUE of a large number of genotypes in breeding nurseries for precise selection decisions [8]. Assessing resource efficiencies in cotton can be gauged through physiological indicators like biomass, water status, and chlorophyll levels. Traditional methods, often destructive, have been used to evaluate WUE but pose limitations for rapid and accurate assessments across large genotype samples [8]. Advanced phenotyping technologies can enhance precision in data collection and accelerate crop improvement decisions. Photosynthesis-related traits and WUE, though challenging to assess visually, can be detected non-destructively through light spectrum reflectance variations [9]. Advanced phenotyping systems have recently proven effective for evaluating traits like green cover, biomass, water stress, nitrogen, chlorophyll levels, and photosynthetic rates [8]. This provides a rapid, non-destructive way to monitor and measure various phenotypic traits essential for assessing WUE [10,11,12].
Multi/hyperspectral remote sensing has revolutionized plant growth assessments by capturing various bands of the light spectrum, including blue, NIR, red, green, and red edge [13,14]. Under optimal growth conditions, healthy plants appear green because they absorb more red/blue and reflect green light. The strong correlation of these light combinations with photosynthesis, water stress, and nutrient status has been well documented [15]. Vegetation indices (VIs) like the normalized difference vegetation index (NDVI), red-edge chlorophyll index (CIRed-edge), and normalized difference red edge (NDRE) have been used in assessing the genotypes for traits such as stay-green and chlorophyll levels [16,17,18]. Multi/hyperspectral VIs are valuable tools for monitoring plant health status under varying environmental conditions. In plant phenotyping, hyperspectral data are more potent than multispectral data because they can capture a broader range of wavelengths, including narrow bands within the visible and infrared spectrum [19,20]. Since hyperspectral sensors can see substantially more information, they enable researchers to detect various plant characteristics such as leaf pigments, water content, nutrient levels, and stress responses more precisely [19]. Although the extra bands make it easier to distinguish more details, they also require eliminating redundant data, making analysis more complex and expensive. Rapid and timely assessments of crop plants help optimize crop management practices to improve crop yields and sustainability [18,21]. Phenotypic selections based on natural variations in important crop traits such as WUE help develop resource-efficient cultivars. Previously, very few studies have explored the reflectance data above the 1000 nm wavelength to track the plant water status in cotton. The goal of this study is (1) to assess the prediction accuracy of hyperspectral VIs for WUE under natural growth conditions, (2) to compare the effectiveness of hyperspectral VIs and other physiological traits for the assessment of WUE, and (3) to select elite cotton varieties with high WUE and superior fiber quality.

2. Materials and Methods

2.1. Plant Material and Experimental Design

This experiment used 40 recent cotton varieties grown in the United States of America (USA) (Supplementary Table S1). This study was conducted in 2022 (5 May to 28 October) at the Environmental Plant Physiology Laboratory at the Mississippi Agricultural and Forestry Experimentation station, Mississippi State University, Mississippi, USA (33°28′ N, 88°47′ W) (Figure 1a). This region’s average maximum and minimum air temperatures are 24.1 and 11.9 °C, and relative humidity ranges from 50 to 96%. The average annual rainfall in this region is 50 to 65 inches, and the average wind direction is 189 degrees.
The plants were grown in polyvinylchloride pots with a diameter of 15.2 cm and a length of 65 cm. A drain hole was made at the bottom side of the pots to facilitate water drainage. The bottom 2.5 cm of the pot was filled with 500 g of clean pea gravel, and the rest was filled with fine sand (particle size is less than 0.3 mm) (Figure 1a). Plastic pipes and a dripper system supplied water and nutrients. Optimum irrigation was provided to maintain the pots at field capacity. Throughout the experiments, the pots were irrigated with water and Hoagland’s nutrient solution thrice daily for 120 s. The quantity of the irrigation was determined based on actual evapotranspiration rates recorded at a nearby weather station located 100 m from the experimental setup. The nutrient amounts were amended every four days during the experiments based on the plant growth stage. Four cotton seeds were sown in each pot and thinned to one plant per pot upon emergence. A total of 40 varieties with three replications and three plants per replication were planted (a total of 360 pots) (Figure 1a). The seedlings were thinned to one per pot at the first leaf stage. The weather (air temperature, incident solar radiation, rainfall, and wind) during the experimental duration is presented in Figure 1b. The total rainfall during the experimental duration was 553 mm. The averages of the daily maximum and minimum air temperatures were 30.4 and 18.0 °C. The average incident solar radiation and wind velocity were 19.0 MJ/m2/day and 5.0 km/hour, respectively.

2.2. Measurements

2.2.1. Hyperspectral Vegetation Indices

The crop reflectance was measured using a portable handheld (Portable Spectroradiometer) PSR+ 3500 spectroradiometer (Spectral Evolution, Haverhill, MA, USA, spectral range: 350–2500 nm) attached to a leaf clip assembly with fiber optical cable and an internal light source (https://spectralevolution.com/, accessed on 3 June 2024). The instrument’s spectral resolution was 2.8 nm at 700 nm, 8 nm at 1500 nm, and 6 nm at 2100 nm full width at half maximum. A white reference panelboard was used to calibrate the instrument. A white reference measurement was taken at the beginning of the measurements; each measurement was then radiometrically calibrated based on the white reference. Ten instantaneous spectral reflectance measurements were recorded from the adaxial surface of the 3rd and 4th leaf for each variety by keeping the leaf vertical to the optical probe. The measurements were taken between 10:00 a.m. and 12.00 p.m. 45 days after sowing. The average plant height during the measurement ranged from 100 to 165 cm.
In this study, we utilized four hyperspectral VIs, which are water-sensitive VIs and indirectly linked with WUE: the simple ratio water index (SRWI), the normalized difference water index (NDWI), the normalized difference water index centered at 1640 nm (NDWI1640), and the normalized difference water index centered at 2130 nm (NDWI2130) (Table 1).

2.2.2. Plant Physiological Measurement

Plant physiological measurements (photosynthesis and transpiration) were measured using the LI-6800 photosynthesis system. The temperature was set at 30 °C, CO2 at 420 ppm, photosynthetically active radiation (PAR) at 1500 µmol m−2 s−1, and relative humidity at 60% while taking the measurements. The measurements were taken on the 3rd or 4th leaf from the top of each plant 45 days after sowing. Photochemical and fluorescence parameters were measured using the LI-6800 photosynthesis system. Photosystem II (PSII) effective chlorophyll fluorescence (Fv′/Fm′) was calculated using the equation (Fv′/Fm′) = (Fm′ − Fo′)/Fm′. Fm′ is the maximal fluorescence of light-adapted leaves, and Fo′ is the minimal fluorescence of a light-adapted leaf that has momentarily been darkened [25]. The electron transport rate (ETR) was calculated using the equation ((Fm′ − Fs)/Fm′) × fIαleaf, where Fs is the steady-state fluorescence, f is the fraction of absorbed quanta that PSII uses, I is the incident photon flux density (µmol m−2 s−1), and αleaf is the leaf absorbance. The PSII actual photochemical quantum yield or photosystem efficiency (PhiPS2) was calculated using the equation (Fv′ − Fs)/Fm′. Fs is the steady-state fluorescence [26,27].

2.2.3. Water-Use Efficiency

Water-use efficiency is estimated as a ratio of photosynthesis and transpiration [28]. This is an indirect measure of carbon gain per unit of water loss [29]. Photosynthesis and transpiration were measured using the LI-6800 photosynthesis system (Section 2.2.2).

2.2.4. Individual Seed Weight and Number of Seeds per Boll

The individual seed weight (ISW) and number of seeds per boll (SNB) were measured manually at harvest (140 days after sowing). The open bolls were allowed to dry at room temperature, followed by separating them into burr, lint, and seed. The individual seed was weighed, and the number of seeds in each boll was counted [30].

2.2.5. Fiber Quality Parameters

Lint samples were assessed for quality using high-volume instrumentation (HVI) by the Fiber and Biopolymer Research Institute at Texas Tech University, Lubbock, TX, as described by Davidonis and Hinojosa (1994) and Lokhande and Reddy (2014) [31,32]. Fiber properties measured on HVI were fiber length, strength, micronaire, and uniformity.

2.3. Statistical Analysis

All statistical analyses were performed in RStudio [33]. Linear regressions and the correlation matrix were calculated to evaluate the relationship between all observed parameters. A mixed linear model was used to test the significance of variation between genotypes for all traits. The results were considered significant at p ≤ 0.05. Repeatability was calculated according to Segal et al. [34]. The repeatability of each component indicates the consistency of traits in a particular environment. Principal component analysis (PCA) was used for multivariate analysis to assess the diversity in varieties for traits [16]. Figures in the study were created using RStudio.

3. Results

3.1. Hyperspectral Reflectance from Plant Leaves

Hyperspectral reflectance showed a significant difference among the 40 varieties for phytochemical responses across the light spectrum (Figure 2). The red-edge, NIR, and short-wave infrared (SWIR) spectrum showed higher variations than blue and green. Vegetation indices from this wavelength can be used to explore variations among the varieties. Spectral variations at 1240 nm, 1640 nm, and 2130 nm wavelengths can be used to observe water status. We calculated four water-sensitive-related VIs to assess the WUE in 40 cotton varieties (Table 1).

3.2. Phenotypic Variation and Repeatability of Traits

Vegetation indices such as the SRWI and NDWI showed significant variations (p < 0.05) among the 40 varieties (Table 2). The varieties also varied significantly (p < 0.05) for most of the physiological traits, yield, and fiber-quality-related traits. Hyperspectral and handheld-instrument-based measurements were taken at the same time and day and on the same plants; therefore, the environmental and genetic variances can be neglected. Broad-sense heritability was used to check the repeatability and level of precision of the measurements/instruments, which could only be the significant source of error variance [35]. Overall, the repeatability of the VIs was high, ranging between 0.79 and 0.89, while the repeatability for other physiological traits was up to 0.88 (Figure 3). The heritability/repeatability for yield and fiber-quality-related traits ranged between 0.80 and 0.98, which is a high repeatability for any breeding trials (Table 2).

3.3. Relevance of VIs with Physiological and Yield-Related Traits

High regression values (up to R2 = 0.66) were observed between the NDWI, the SRWI, and WUE, with low RMSEs compared to the NDWI1640 and NDWI2130, which showed weak R2 values up to 0.36 with WUE (Figure 4). At the same time, R2 values between WUE and photosynthesis-related traits such as PhiPS2, Fv′/Fm′, and ETR ranged from 0.21 to 0.42 (Figure 5). Strong Pearson correlations (up to r = 0.81) were observed between the SRWI, the NDWI, the NDWI1640, the NDWI2130, and WUE. These VIs also strongly correlated with photosynthesis-related traits such as PhiPS2 (up to r = 0.51), ETR (up to r = 0.51), Fv′/Fm′ (up to r = 0.38), A (up to r = 0.35). In comparison, the SRWI and NDWI showed higher correlations ranging from 0.63 to 0.21 with ISW and SNB compared to any physiological traits (Figure 6). Correlations between photosynthesis-related traits and WUE were up to r = 0.66. Yield-related traits such as ISW and SNB also had moderate to weak correlations of r = 0.44 and r = 0.18 with WUE, respectively.

3.4. Selection of Varieties Based on Vegetation Indices

A principal component analysis (PCA) biplot was used to examine relationships between the VIs and physiological traits for selecting the varieties with high fiber quality, as well as high performance of the VIs and physiological traits (Figure 7). For this, 40 cultivars were plotted for hyperspectral VIs, photosynthesis, and WUE traits in the PCA biplot. In Figure 7a, the first two principal components capture 69.09% of the variance, revealing a strong positive correlation between the traits. All VIs showed positive correlations with WUE. In Figure 7b, the first two components capture 68.53% of the variance, with fiber traits such as Len, Str, and Elo showing significant correlations. We observed that some varieties such as V5, V7, V8, V12, V20, V28, and V31 had high WUE with a high SRWI, NDWI, NDWI1640, and NDWI2130 while maintaining moderate SNP, SNB, and high values for some of the fiber quality traits (Figure 7 and Figure 8). For example, V5, V7, and V20 had good fiber Mic, while V28 showed high Len, and V31 had both high fiber Str and Unif traits. At the same time, some cultivars like V15, V36, and V40 were selected with high performance for photosynthesis-related traits such as PhiPS2, ETR, and Fv′/Fm′ with high fiber-quality-related traits (Figure 8).

4. Discussion

4.1. Potential of Hyperspectral-Based Indices to Assess WUE

Manual phenotyping methods are considered a bottleneck in crop research because they are time-consuming, costly, and prone to errors [14]. Considering this, more advanced, rapid, and accurate technologies are needed to assess physiological traits like WUE in crops. Hyperspectral-based phenotyping emerges as a promising alternative to traditional phenotyping approaches to assess WUE [36,37]. Hyperspectral VIs utilize spectral information to provide insights into how efficiently plants use water resources. By analyzing the unique signatures of light reflected or emitted by plants across various wavelengths, hyperspectral techniques can capture subtle changes related to water stress, photosynthetic activity, and overall plant health [11,38].
Previous studies in wheat have demonstrated the effectiveness of multi/hyperspectral data in predicting physiological traits and grain yield [8,36]. Our study further strengthens this notion, as we found strong correlations between VIs and traditional indicators of plant physiological traits, particularly WUE. Notably, our data showed higher correlations between hyperspectral-based traits and WUE compared to ground-based physiological data from other handheld sensors, underscoring the superiority of the hyperspectral sensing method. The hyperspectral indices also showed promising correlations with yield-related traits. Using linear regression models, we achieved notable prediction accuracy for WUE (R2 = 0.66) with a low RMSE, highlighting the potential of sensing in efficiently detecting within-season WUE (Figure 4 and Figure 5). Given these results, integrating hyperspectral-based non-destructive phenotyping platforms into large breeding programs could revolutionize traditional approaches, leading to cost and labor savings while enhancing assessments of biomass and resource efficiencies in cotton.

4.2. Comparison of Hyperspectral and Photosynthesis Traits for Assessment of WUE

Visible indicators of efficient water supply include high greenness and chlorophyll levels in plant tissues. Spectral bands are closely linked to physiological indicators like green cover, chlorophyll content, and water levels, offering insights into water status in plants [39,40]. However, the diverse characteristics of spectral bands can lead to limitations in precisely capturing physiological information at specific growth stages [41]. For instance, the NIR band excels in detecting a wide range of variations in green biomass, while the SWIR bands’ reflectance ranges are sensitive to variations in water status [41,42]. In our study, we used NIR, SWIR, and wavelengths beyond to calculate the water-sensitive VIs and predict the WUE. The hyperspectral-based indices demonstrated high repeatability and reliability in predicting WUE. Similar results have been reported in previous studies using multispectral and hyperspectral data in wheat and maize (Figure 3) [8,36]. Hyperspectral indices derived from NIR and SWIR wavelengths showed strong associations with measured WUE and moderate to weak correlations with photosynthesis and yield-related traits (Figure 6). Notably, the SRWI and NDWI exhibited significantly high correlations with WUE and also demonstrated the variation among the cotton cultivars with high repeatability (Table 2). Meanwhile, the NDWI1640 and NDWI2130 showed weak correlations with WUE in comparison with the SRWI and NDWI. Similarly, low to moderate correlations were observed for photosynthesis-related traits with WUE. Since NIR and SWIR bands are known for their ability to detect water stress severity, the SRWI and NDWI are more reliable in predicting WUE and genotype variations [43,44]. Therefore, the SRWI and NDWI can be used to predict the WUE and variations among the genotypes in future breeding trials to develop resource-efficient cultivars in cotton (Figure 6). Overall, these findings highlight the potential of hyperspectral-based VIs in evaluating WUE in plants and their applicability in monitoring cotton plants for WUE across the different growth stages and environmental conditions.

4.3. Significance of Hyperspectral VIs to Evaluate in-Season Variations among Cultivars

The rapid and cost-effective assessment of WUE across different genotypes is a bottleneck and has posed challenges for improving crop yield [40,45]. The traditional method can be replaced with hyperspectral-based high-throughput phenotyping, which involves non-destructive assessments, especially when dealing with a large number of genotypes. When it comes to large populations, the variation in the data collected could be influenced by both genotypes and the temporal changes in the environment, such as temperature or solar radiation, etc., in a given day or across days. This can lead to misleading conclusions during the selection process. Some studies have explored non-destructive remote sensing techniques from both ground and aerial platforms to detect the in-season phenotypic variations for water and nutrient status among genotypes [36,40,42,46]. However, there needs to be a more practical application of phenotyping platforms to predict WUE for assessing genotype variations due to low data precision. We used an active hyperspectral sensor to measure the reflectance from cotton leaves under controlled light conditions, which gives leverage in data accuracy over a passive sensor. Our study successfully predicted significant variations among cotton varieties for WUE and yield using hyperspectral indices with high repeatability. We achieved high regression values (up to R2 = 0.66) between the hyperspectral VIs and measured WUE with a low RMSE, proving the effectiveness of non-destructive hyperspectral phenotyping (Figure 4). Previously, similar results were reported by predicting the canopy water content using hyperspectral traits in maize [36]. Our results suggest that water-sensitive hyperspectral VIs can be used to predict in-season phenotypic variations among the genotypes to make early breeding decisions in breeding programs.

4.4. Significance of Hyperspectral VIs for Establishing Selection Strategy

Developing elite varieties and ensuring efficient water supply are crucial to achieving high yields [47,48]. It is challenging to repeatedly phenotype a large sample size using destructive measurements under varying environmental conditions. Adopting a non-destructive approach to assess the specific growth requirements of different genotypes accurately is crucial for potential advancements in crop improvement. Different cultivars can have varying water demands at particular growth stages. Predicting subtle changes in water status and their effects on biomass and chlorophyll levels through hyperspectral data can aid in accurate genotype selection for specific environments. Therefore, hyperspectral traits could serve as a rapid and cost-effective replacement for traditional traits in accurately selecting genotypes [49]. In our results, cultivars such as V5, V7, V8, V12, V20, V28, and V31 were selected for high WUE using hyperspectral VIs. These cultivars also had high yield and fiber quality compared to other cultivars. The high value of VIs for these cultivars demonstrated the utility of hyperspectral-based sensing in predicting WUE. The PCA results showed that some cultivars also performed well for photosynthesis traits with high WUE (Figure 7). Therefore, cultivar selection based on multiple characteristics can be more precise compared to a single-trait-based selection. Our findings suggest that hyperspectral-based data could play a vital role in establishing effective selection strategies for the selection of elite cultivars for desired traits.

5. Conclusions

In this study, we demonstrated that using hyperspectral-based VIs can effectively predict variations among cultivars for WUE and the selection of elite cotton cultivars. We identified the SRWI and NDWI as efficient VIs for detecting water status with significant correlations with photosynthesis and yield traits. The SRWI and NDWI showed better prediction for WUE compared to the NDWI1640, the NDWI2130, and photosynthesis-related traits. Our findings also highlighted the usefulness of hyperspectral data in selecting water-use-efficient cultivars with high fiber quality traits. In the future, these hyperspectral traits using handheld and UAV platforms can be used to explore in-season variations among the large population for early genotypic selections, which will enhance selection accuracy in cotton breeding programs. However, there are some operational challenges for the above-mentioned hyperspectral platforms in field conditions that need to be addressed through technological advancements in the future.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/agriculture14071054/s1, Table S1. The list of cultivars used in the experiment. These are the 40 recent cotton cultivars grown in the USA.

Author Contributions

Conceptualization, S.B., M.A.H. and K.R.R.; methodology, S.B., M.A.H. and K.R.R.; software, M.A.H., S.B. and K.R.R.; validation, S.B., M.A.H. and K.R.R.; formal analysis, S.B., M.A.H. and K.R.R.; investigation, K.R.R.; resources, K.R.R. and V.R.; data curation, K.R.R. and P.R.; writing—original draft preparation, S.B., M.A.H. and K.R.R.; writing—review and editing, P.R., R.B., K.N.R. and V.R.; visualization, M.A.H.; supervision, K.R.R. and V.R.; project administration, K.R.R. and V.R.; funding acquisition, K.R.R. All authors have read and agreed to the published version of the manuscript.

Funding

This study is based on work supported by Mississippi State University, Mississippi, the United States Department of Agriculture, Agricultural Research Service (under Agreement No. 58-8042-9-072), the USDA-ARS NACA 58-6066-2-030, and the USDA NIFA 2019-34263 30552 and MIS 043050. The authors also received support from the University of Nebraska, Lincoln, and from the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between the U.S. Department of Energy (DOE) and the U.S. Department of Agriculture (USDA).

Data Availability Statement

Data are available upon request from the corresponding authors.

Acknowledgments

We thank David Brand, Senior Research Associate, and other students (Ranadheer Vennam and Sadikshya Poudel) for their help in data collection.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. (a) Experimental setup and (b) weather parameters observed during the experimental duration. A total of 40 varieties with three replications and three plants per replication were planted (a total of 360 pots). The picture (a) was taken 37 days after emergence. Weather data (the rainfall, incident solar radiation, air temperature, and wind) were obtained from a nearby weather station (Delta Agricultural Center, Mississippi State University Extension, North Farm) http://deltaweather.extension.msstate.edu/stations accessed on 10 May 2024.
Figure 1. (a) Experimental setup and (b) weather parameters observed during the experimental duration. A total of 40 varieties with three replications and three plants per replication were planted (a total of 360 pots). The picture (a) was taken 37 days after emergence. Weather data (the rainfall, incident solar radiation, air temperature, and wind) were obtained from a nearby weather station (Delta Agricultural Center, Mississippi State University Extension, North Farm) http://deltaweather.extension.msstate.edu/stations accessed on 10 May 2024.
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Figure 2. Mean hyperspectral reflectance of all 40 varieties (V1–V40).
Figure 2. Mean hyperspectral reflectance of all 40 varieties (V1–V40).
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Figure 3. Repeatability of hyperspectral vegetation indices (SRWI, NDWI, NDWI1640, and NDWI2130) and handheld-instrument-based physiology-related traits (PhiPS2, ETR, Fv′/Fm′, A, E, and WUE) of cotton cultivars. Abbreviations: SRWI, simple ratio water index; NDWI, normalized difference water index; NDWI1640, normalized difference water index centered at 1640 nm; NDWI2130, normalized difference water index centered at 2130 nm; PhiPS2, PSII actual photochemical quantum yield; ETR, electron transport rate; Fv′/Fm′, PSII effective chlorophyll fluorescence; A, photosynthesis; E, transpiration; WUE, water-use efficiency.
Figure 3. Repeatability of hyperspectral vegetation indices (SRWI, NDWI, NDWI1640, and NDWI2130) and handheld-instrument-based physiology-related traits (PhiPS2, ETR, Fv′/Fm′, A, E, and WUE) of cotton cultivars. Abbreviations: SRWI, simple ratio water index; NDWI, normalized difference water index; NDWI1640, normalized difference water index centered at 1640 nm; NDWI2130, normalized difference water index centered at 2130 nm; PhiPS2, PSII actual photochemical quantum yield; ETR, electron transport rate; Fv′/Fm′, PSII effective chlorophyll fluorescence; A, photosynthesis; E, transpiration; WUE, water-use efficiency.
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Figure 4. Linear regressions among hyperspectral vegetative indices (a) NDWI, (b) SRWI, (c) NDWI2130, and (d) NDWI1640 and water-use efficiency. Abbreviations: SRWI, simple ratio water index; NDWI, normalized difference water index; NDWI1640, normalized difference water index centered at 1640 nm; NDWI2130, normalized difference water index centered at 2130 nm; WUE, water-use efficiency (µmol CO2/mmol H2O); R2, coefficient of determination; RMSE, root mean squared error.
Figure 4. Linear regressions among hyperspectral vegetative indices (a) NDWI, (b) SRWI, (c) NDWI2130, and (d) NDWI1640 and water-use efficiency. Abbreviations: SRWI, simple ratio water index; NDWI, normalized difference water index; NDWI1640, normalized difference water index centered at 1640 nm; NDWI2130, normalized difference water index centered at 2130 nm; WUE, water-use efficiency (µmol CO2/mmol H2O); R2, coefficient of determination; RMSE, root mean squared error.
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Figure 5. Linear regressions among photosynthesis-related traits (a) Fv′/Fm′, (b) PhiPS2, and (c) ETR and water-use efficiency. Abbreviations: WUE, water-use efficiency (µmol CO2/mmol H2O); PhiPS2, PSII actual photochemical quantum yield (µmol µmol−1); ETR, electron transport rate (µmol m−2 s−1); Fv′/Fm′, PSII effective chlorophyll fluorescence; R2, coefficient of determination; RMSE, root mean squared error.
Figure 5. Linear regressions among photosynthesis-related traits (a) Fv′/Fm′, (b) PhiPS2, and (c) ETR and water-use efficiency. Abbreviations: WUE, water-use efficiency (µmol CO2/mmol H2O); PhiPS2, PSII actual photochemical quantum yield (µmol µmol−1); ETR, electron transport rate (µmol m−2 s−1); Fv′/Fm′, PSII effective chlorophyll fluorescence; R2, coefficient of determination; RMSE, root mean squared error.
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Figure 6. Pearson correlations between vegetation indices, physiological and yield-related traits. Abbreviations: SRWI, simple ratio water index; NDWI, normalized difference water index; NDWI1640, normalized difference water index centered at 1640 nm; NDWI2130, normalized difference water index centered at 2130 nm; WUE, water-use efficiency; PhiPS2, PSII actual photochemical quantum yield; ETR, electron transport rate; Fv′/Fm′, PSII effective chlorophyll fluorescence; ISW, individual seed weight per plant; SNB, seed number per boll; A, photosynthesis; E, transpiration.
Figure 6. Pearson correlations between vegetation indices, physiological and yield-related traits. Abbreviations: SRWI, simple ratio water index; NDWI, normalized difference water index; NDWI1640, normalized difference water index centered at 1640 nm; NDWI2130, normalized difference water index centered at 2130 nm; WUE, water-use efficiency; PhiPS2, PSII actual photochemical quantum yield; ETR, electron transport rate; Fv′/Fm′, PSII effective chlorophyll fluorescence; ISW, individual seed weight per plant; SNB, seed number per boll; A, photosynthesis; E, transpiration.
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Figure 7. Principal component analysis (PCA) biplot on 40 cultivars (V1 to V40) for (a) hyperspectral remote-sensing-based vegetation indices, physiological and yield-related traits, and (b) fiber quality traits of cotton cultivars. Abbreviations: SRWI, simple ratio water index; NDWI, normalized difference water index; NDWI1640, normalized difference water index centered at 1640 nm; NDWI2130, normalized difference water index centered at 2130 nm; WUE, water-use efficiency (µmol CO2/mmol H2O); PhiPS2, PSII actual photochemical quantum yield (µmol µmol−1); ETR, electron transport rate (µmol m−2 s−1); Fv′/Fm′, PSII effective chlorophyll fluorescence; A, photosynthesis (µmol m−2 s−1); E, transpiration (mmol m−2 s−1); ISW, individual seed weight per plant (mg); SNB, seed number per boll; Len, fiber length (inch); Str, fiber strength (g/tex); Unif, fiber uniformity (%); Elo, fiber elongation (%); Mic, micronaire (-).
Figure 7. Principal component analysis (PCA) biplot on 40 cultivars (V1 to V40) for (a) hyperspectral remote-sensing-based vegetation indices, physiological and yield-related traits, and (b) fiber quality traits of cotton cultivars. Abbreviations: SRWI, simple ratio water index; NDWI, normalized difference water index; NDWI1640, normalized difference water index centered at 1640 nm; NDWI2130, normalized difference water index centered at 2130 nm; WUE, water-use efficiency (µmol CO2/mmol H2O); PhiPS2, PSII actual photochemical quantum yield (µmol µmol−1); ETR, electron transport rate (µmol m−2 s−1); Fv′/Fm′, PSII effective chlorophyll fluorescence; A, photosynthesis (µmol m−2 s−1); E, transpiration (mmol m−2 s−1); ISW, individual seed weight per plant (mg); SNB, seed number per boll; Len, fiber length (inch); Str, fiber strength (g/tex); Unif, fiber uniformity (%); Elo, fiber elongation (%); Mic, micronaire (-).
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Figure 8. Comparison of 40 cotton cultivars (V1 to V40) for (a) hyperspectral vegetation indices, (b) physiological traits, (c) yield, and (d) fiber-quality-related traits, (e) photosynthesis, water use efficiency and transpiration. Different colored dots indicate the average values of each trait, and bars indicate standard deviations. Abbreviations: SRWI, simple ratio water index; NDWI, normalized difference water index; NDWI1640, normalized difference water index centered at 1640 nm; NDWI2130, normalized difference water index centered at 2130 nm; WUE, water-use efficiency (µmol CO2/mmolH2O); PhiPS2, PSII actual photochemical quantum yield (µmol µmol−1); ETR, electron transport rate (µmol m−2 s−1); Fv′/Fm′, PSII effective chlorophyll fluorescence; A, photosynthesis (µmol m−2 s−1); E, transpiration (mmol m−2 s−1); ISW, individual seed weight per plant (mg); SNB, seed number per boll; Len, fiber length (inch); Str, fiber strength (g/tex); Unif, fiber uniformity (%); Elo, fiber elongation (%); Mic, micronaire (-).
Figure 8. Comparison of 40 cotton cultivars (V1 to V40) for (a) hyperspectral vegetation indices, (b) physiological traits, (c) yield, and (d) fiber-quality-related traits, (e) photosynthesis, water use efficiency and transpiration. Different colored dots indicate the average values of each trait, and bars indicate standard deviations. Abbreviations: SRWI, simple ratio water index; NDWI, normalized difference water index; NDWI1640, normalized difference water index centered at 1640 nm; NDWI2130, normalized difference water index centered at 2130 nm; WUE, water-use efficiency (µmol CO2/mmolH2O); PhiPS2, PSII actual photochemical quantum yield (µmol µmol−1); ETR, electron transport rate (µmol m−2 s−1); Fv′/Fm′, PSII effective chlorophyll fluorescence; A, photosynthesis (µmol m−2 s−1); E, transpiration (mmol m−2 s−1); ISW, individual seed weight per plant (mg); SNB, seed number per boll; Len, fiber length (inch); Str, fiber strength (g/tex); Unif, fiber uniformity (%); Elo, fiber elongation (%); Mic, micronaire (-).
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Table 1. Hyperspectral-based vegetation indices (VIs) used in the study.
Table 1. Hyperspectral-based vegetation indices (VIs) used in the study.
Vegetation IndexCalculationReference
Simple ratio water index (SRWI)R860/R1240[22]
Normalized difference water index (NDWI)(R860 − R1240)/(R860 + R1240)[23]
Normalized difference water index centered at 1640 nm (NDWI1640)(R858 − R1640)/(R858 + R1640)[24]
Normalized difference water index centered at 2130 nm (NDWI2130)(R858 − R2130)/(R858 + R2130)[24]
Table 2. Analysis of variance for VIs of 40 cotton cultivars’ physiology and yield-related traits.
Table 2. Analysis of variance for VIs of 40 cotton cultivars’ physiology and yield-related traits.
Traits SRWINDWINDWI1640NDWI2130WUEPhiPS2ETRFv′/Fm′A
CultivarF-values2.052.051.010.982.342.422.231.279.039
p-values000.470.510000.18<0.0001
Repeatability 0.890.890.810.820.880.880.870.830.87
Traits EISWSNBMicLenUnifStrElo
CultivarF-values1.9825.781.35.28.531.687.1114.98
p-values0.01<0.00010.16<0.0001<0.00010.03<0.0001<0.0001
Repeatability0.870.950.80.950.960.860.950.98
SRWI, simple ratio water index; NDWI, normalized difference water index; NDWI1640, normalized difference water index centered at 1640 nm; NDWI2130, normalized difference water index centered at 2130 nm; WUE, water-use efficiency; PhiPS2, PSII actual photochemical quantum yield; ETR, electron transport rate; Fv′/Fm′, PSII effective chlorophyll fluorescence; ISW, individual seed weight; SNB, number of seeds per boll; Mic, micronaire; Len, fiber length; Unif, fiber uniformity; Str, fiber strength; Elo, fiber elongation; A, photosynthesis; E, transpiration.
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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

AMA Style

Beegum S, Hassan MA, Ramamoorthy P, Bheemanahalli R, Reddy KN, Reddy V, Reddy KR. Hyperspectral Reflectance-Based High Throughput Phenotyping to Assess Water-Use Efficiency in Cotton. Agriculture. 2024; 14(7):1054. https://doi.org/10.3390/agriculture14071054

Chicago/Turabian Style

Beegum, Sahila, Muhammad Adeel Hassan, Purushothaman Ramamoorthy, Raju Bheemanahalli, Krishna N. Reddy, Vangimalla Reddy, and Kambham Raja Reddy. 2024. "Hyperspectral Reflectance-Based High Throughput Phenotyping to Assess Water-Use Efficiency in Cotton" Agriculture 14, no. 7: 1054. https://doi.org/10.3390/agriculture14071054

APA Style

Beegum, S., Hassan, M. A., Ramamoorthy, P., Bheemanahalli, R., Reddy, K. N., Reddy, V., & Reddy, K. R. (2024). Hyperspectral Reflectance-Based High Throughput Phenotyping to Assess Water-Use Efficiency in Cotton. Agriculture, 14(7), 1054. https://doi.org/10.3390/agriculture14071054

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