Near‐Remote Green: Red Perpendicular Vegetation Index Ground Cover Fraction Estimation in Cotton

Sharma, Bablu; Ritchie, Glen L.; Rajan, Nithya

doi:10.2135/cropsci2014.09.0625

Cited by 16 publications

(5 citation statements)

References 36 publications

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“…The high irrigation treatments had differences in NAWF among different cultivars, but maximum NDRE values occurred with canopy closure, rather than when the crop had completed growth. Sharma et al (2015b) observed that ground‐cover fraction approached 1.0 as the crop reached complete canopy coverage in the higher irrigation treatments. Ritchie et al (2010) reported similar results, where NDRE was insensitive to changes in ground cover fraction as it approached 1.0, and Ritchie and Bednarz (2005), where NDRE was insensitive to leaf area index values >2.0.…”

Section: Resultsmentioning

confidence: 96%

Using Normalized Difference Red Edge Index to Assess Maturity in Cotton

Thompson

Guo

Sharma³

et al. 2019

Crop Science

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Temporal remote sensing measurements of plant growth may give breeders a better understanding of crop growth habits, yield, fiber quality, and maturity in cotton (Gossypium hirsutum L.) genotypes. The objective of this research was to derive a spectral index maturity scoring method based on the normalized difference red edge (NDRE) index. Seasonal NDRE measurements were collected from 2015 to 2017. A growth inflection point (GIP) was generated based on a quadratic fit of the NDRE growth curves for nine commercial cotton cultivars under three irrigation treatments. This GIP was the number of heat units associated with the inflection point of the NDRE values during the season. It was compared with manual measurements of crop maturity, including nodes above white flower (NAWF), percentage open boll (POB), and end‐of‐season plant mapping indices. Each year had environmental conditions that changed growth habits and maturity of the cultivars. Cultivar and irrigation affected maturity in all 3 yr. The GIP correlated with each maturity assessment, with the highest correlations found with NAWF (r2 from 0.38 to 0.88) by irrigation. In most cases, the relationship between NAWF and GIP was not cultivar specific, suggesting that GIP may be used across multiple cotton genotypes within multiple growing environments. The GIP method provides a method to more rapidly and objectively evaluate maturity characteristics of cotton cultivars, as well as the effects of management on these characteristics.

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Section: Resultsmentioning

confidence: 96%

Using Normalized Difference Red Edge Index to Assess Maturity in Cotton

Thompson

Guo

Sharma³

et al. 2019

Crop Science

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“…Sharma et al. (2015a) observed that ground cover fraction, which is the number of pixels containing vegetation versus scene (vegetation and soil pixels), approached 1.0 as the crop reached complete canopy coverage in the higher irrigation treatments. Ritchie et al.…”

Section: Resultsmentioning

confidence: 99%

“…The assessment of growth and development in cotton ( Gossypium hirsutum L.) relies on the measurement of phenotypic characteristics such as leaf structure, leaf area index (LAI), leaf and canopy temperature, and canopy height (Sharma, Ritchie, & Rajan, 2015a; Wiegand, Richardson, Escobar, & Gerbermann, 1991). Most of these measurements have traditionally been conducted on a small‐scale level due to the intensive labor requirements (Rahaman, Chen, Gillani, Klukas, & Chen, 2015).…”

Section: Introductionmentioning

confidence: 99%

Time‐based remote sensing yield estimates of cotton in water‐limiting environments

Thompson

Mills²,

Pabuayon

et al. 2020

Agronomy Journal

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The use of high-throughput phenotyping aids breeding programs in making more informed selections and advancements. This study's objectives were to determine which proximal remote sensing parameters (normalized difference red edge [NDRE], normalized difference vegetation index [NDVI], difference between canopy and air temperatures [∆T], and plant height) are robust estimators of cotton lint yield and to use a time-integrated function of one parameter as a single phenotypic measurement for predicting yield. This study evaluated remote sensing parameters (NDRE, NDVI, ∆T, and plant height) measured weekly from squaring through boll production and development. Of these measurements, NDRE was most consistent in terms of r 2 , slope, and normality in predicting yield. From these findings, a temporal analysis was calculated as NDRE integrated over the season, namely NDRE-days. Significant r 2 values were detected for the individual remote sensing measurements, with the largest r 2 occurring around peak bloom (80 d after planting). An r 2 of 0.81 was identified between ∆T and lint yield in 2015, whereas in 2017 the largest r 2 value with lint yield was with NDRE (r 2 = .71). The temporal analysis showed a significant relationship between NDRE-days and lint yield (P < .0001; r 2 = .58 in 2015 and r 2 = .68 in 2017) that was not cultivar specific. This study presents a suitable method that breeders could use to efficiently evaluate plant growth and estimate yield for variety selections while cutting resource requirements.Abbreviations: ∆T, difference between canopy temperature and air temperature; ET, evapotranspiration; FM, FiberMax; GDD 15.6 , 15.6 • C growing degree days; GPS, global positioning system; NDRE, normalized difference red edge; NDVI, normalized difference vegetation index; SDI, subsurface drip irrigation; ST, Stoneville.

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“…FVC were calculated effectively from an orthomosaic image with hundreds of three-channel RGB images by separating and classifying vegetation pixels from soil pixels and other non-vegetation pixels (Guo et al 2013;Sharma et al 2015) as demonstrated by (Duan et al 2017). FVC is the ratio of the vertical projected area of vegetation to the total ground area (Lin and Qi 2004).…”

Section: Calculation Of Fvcmentioning

confidence: 99%

Utilisation of unmanned aerial vehicle imagery to assess growth parameters in mungbean (Vigna radiata (L.) Wilczek)

Xiong,

Chiau,

Wenham

et al. 2023

Crop & Pasture Science

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Context Unmanned aerial vehicles (UAV) with red–green–blue (RGB) cameras are increasingly used as a monitoring tool in farming systems. This is the first field study in mungbean (Vigna radiata (L.) Wilzcek) using UAV and image analysis across multiple seasons. Aims This study aims to validate the use of UAV imagery to assess growth parameters (biomass, leaf area, fractional light interception and radiation use efficiency) in mungbean across multiple seasons. Methods Field experiments were conducted in summer 2018/19 and spring–summer 2019/20 for three sowing dates. Growth parameters were collected fortnightly to match UAV flights throughout crop development. Fractional vegetation cover (FVC) and computed vegetation indices: colour index of vegetation extraction (CIVE), green leaf index (GLI), excess green index (ExG), normalised green-red difference index (NGRDI) and visible atmospherically resistant index (VARI) were generated from UAV orthomosaic images. Key results (1) Mungbean biomass can be accurately estimated at the pre-flowering stage using RGB imagery acquired with UAVs; (2) a more accurate relationship between the UAV-based RGB imagery and ground data was observed during pre-flowering compared to post-flowering stages in mungbean; (3) FVC strongly correlated with biomass (R2 = 0.79) during the pre-flowering stage; NGRDI (R2 = 0.86) showed a better ability to directly predict biomass across the three experiments in the pre-flowering stages. Conclusion UAV-based RGB imagery is a promising technology to replace manual light interception measurements and predict biomass, particularly at earlier growth stages of mungbean. Implication These findings can assist researchers in evaluating agronomic strategies and considering the necessary management practices for different seasonal conditions.

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Near‐Remote Green: Red Perpendicular Vegetation Index Ground Cover Fraction Estimation in Cotton

Cited by 16 publications

References 36 publications

Using Normalized Difference Red Edge Index to Assess Maturity in Cotton

Using Normalized Difference Red Edge Index to Assess Maturity in Cotton

Time‐based remote sensing yield estimates of cotton in water‐limiting environments

Utilisation of unmanned aerial vehicle imagery to assess growth parameters in mungbean (Vigna radiata (L.) Wilczek)

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