Using normalized difference vegetation index to estimate sesame drydown and seed yield

Dong, Xuejun; Guo, Feng; Zemach, Itay

doi:10.1080/15427528.2020.1846101

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…SVI can assist by tracking the plant's physiological status (Sarić et al, 2022). The use of NDVI and NDRE has been studied in other sesame populations (Dong et al, 2021;Petsoulas et al, 2022), and they reported similar results when these two indices reached the highest values at the peak of the reproductive stage (57 to 70 DAS) and then decreased as the plants started senescence. This suggests that our HTP platform is reliable for monitoring longitudinal traits in sesame, although additional analysis is required to dissect these traits in the vegetative phase.…”

Section: Time-series Analysis Promotes the Detection Of Sesame Develo...mentioning

confidence: 86%

Leveraging genomics and temporal high-throughput phenotyping to enhance association mapping and yield prediction in sesame

Sabag,

Bi,

Sahoo

et al. 2024

Preprint

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Sesame (Sesamum indicum) is an important oilseed crop with rising demand due to its high oil quality. To meet these future demands, there is an urgent need to develop and integrate new breeding strategies. While genomic resources have advanced genetic research in sesame, implementation of high-throughput phenotyping and genetic analysis of longitudinal traits remains limited. Here, we combined high-throughput phenotyping and random regression models to investigate the dynamics of plant height, leaf area index, and five spectral vegetation indices throughout the sesame growing seasons in a diversity panel. Modeling the temporal phenotypic and additive genetic trajectories revealed distinct patterns corresponding to the sesame growth cycle. We also conducted longitudinal genomic prediction and association mapping of plant height using various models and cross-validation schemes. Moderate prediction accuracy was obtained when predicting new genotypes at each time point, and moderate to high values were obtained when forecasting future phenotypes. Association mapping revealed three genomic regions in linkage groups 6, 8, and 11 conferring trait variation over time and growth rate. Furthermore, we leveraged correlations between the temporal trait and seed-yield and applied multi-trait genomic prediction. We obtained an improvement over single-trait analysis, especially when phenotypes from earlier time points were used, highlighting the potential of using a high-throughput phenotyping platform as a selection tool. Our results shed light on the genetic control of longitudinal traits in sesame and underscore the potential of high-throughput phenotyping to detect a wide range of traits and genotypes that can inform sesame breeding efforts to enhance yield.

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Section: Time-series Analysis Promotes the Detection Of Sesame Develo...mentioning

confidence: 86%

Leveraging genomics and temporal high-throughput phenotyping to enhance association mapping and yield prediction in sesame

Sabag,

Bi,

Sahoo

et al. 2024

Preprint

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“…In this study, our analysis was focused on the use of NDVI to indicate LWP of selected crop plants, although a number of other vegetation indices can also be derived from the same sensor as used here. Using the same active sensor, Dong, Feng & Zemach (2021) showed that, similar to NDVI, the red band reflectance and re-normalized difference vegetation index (RDVI) explained >62% of the variation in shoot water content of sesame during drydown. Zarco-Tejada, González-Dugo & Berni (2012) found that RDVI was more strongly related to LWP than was NDVI in a citrus orchard ( R 2 = 0.44 vs. R 2 = 0.24).…”

Section: Discussionmentioning

confidence: 99%

“…Recently, NDVI has been used to infer the effect of water availability and water stress in plants ( Irmak et al, 2011 ; Aguilar et al, 2012 ; Hunink et al, 2015 ; Bronson et al, 2017 ; Hunsaker et al, 2007 ). NDVI was also used to characterize changes in shoot water content of sesame during drydown ( Dong, Feng & Zemach, 2021 ). For most instances, however, what NDVI actually captures is most likely the accumulated effect of changed leaf water status on plant growth, as indirectly indicated by structures involving water such as leaf area index, or green biomass, instead of the leaf water potential directly .…”

Section: Introductionmentioning

confidence: 99%

Leaf water potential of field crops estimated using NDVI in ground-based remote sensing—opportunities to increase prediction precision

Dong

Peng

Sieckenius

et al. 2021

PeerJ

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Remote-sensing using normalized difference vegetation index (NDVI) has the potential of rapidly detecting the effect of water stress on field crops. However, this detection has typically been accomplished only after the stress effect led to significant changes in crop green biomass, leaf area index, angle and position, and few studies have attempted to estimate the uncertainties of the regression models. These have limited the informed interpretation of NDVI data in agricultural applications. We built a ground-based sensing cart and used it to calibrate the relationships between NDVI and leaf water potential (LWP) for wheat, corn, and cotton growing under field conditions. Both the methods of ordinary least-squares (OLS) and weighted least-squares (WLS) were employed in data analysis, and measurement errors in both LWP and NDVI were considered. We also used statistical resampling to test the effect of measurement errors of LWP on the uncertainties of model coefficients. Our data showed that obtaining a high value of the coefficient of determination did not guarantee a high prediction precision in the obtained regression models. Large prediction uncertainties were estimated for all three crops, and the regressions obtained were not always significant. The best models were obtained for cotton with a prediction uncertainty of 27%. We found that considering measurement errors for both LWP and NDVI led to reduced uncertainties in model coefficients. Also, reducing the sample size of LWP measurement led to significantly increased uncertainties in the coefficients of the linear models describing the LWP-NDVI relationship. Finally, potential strategies for reducing the uncertainty relative to the range of NDVI measurement are discussed.

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“…Some vegetation indices have been used for the assessment and monitoring of vegetation health, vigor, and chlorophyll content, including tomato crops. The Normalized Difference Vegetation Index (NDVI) is used to measure vegetation greenness and vigor and indicates the overall health and growth of plants (Dong et al, 2021). The Green Normalized Difference Vegetation Index (GNDVI) is similar to NDVI but…”

Section: Introductionmentioning

confidence: 99%

Use of different vegetation indices for the evaluation of the kinetics of the cherry tomato (Solanum lycopersicum var. cerasiforme) growth based on multispectral images by UAV

Chávez-Martínez,

Monjardin-Armenta,

Rangel-Peraza

et al. 2023

Preprint

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This study evaluated seven vegetation indices for the monitoring of a cherry tomato crop using an Unmanned Aerial Vehicle with a multispectral camera that measures in the Green, Red, and Near Infrared spectral bands. A photogrammetric flight plan was designed to capture the spectral images every 2 weeks in two agricultural parcels identified as Treatment 1 (\({T}_{1}\)) and Treatment 2 (\({T}_{2}\)). A total of 7 photogrammetric flights were carried out for the crop monitoring and the corresponding orthophotographs were obtained using digital photogrammetry techniques. Subsequently, vegetation indices were calculated for these orthophotographs. The mean and standard deviation of these indices were extracted, and a statistical analysis was performed to compare the vegetation indices and to analyze their behavior over time. Analysis of variance (ANOVA) showed that Ratio Vegetation Index (RVI), Green Vegetation Index (GVI), Normalized Difference Vegetation Index (NDVI), Infrared Percentage Vegetation Index (IPVI), Green Normalized Difference Vegetation Index (GNDVI), and Optimized Soil-Adjusted Vegetation Index (OSAVI) indices showed significant variation (P-value < 0.05) over time. No statistically significant difference between the two treatments was found. IPVI, NDVI, and OSAVI showed less variation in pixel values. The RVI, GVI, NDVI, IPVI, GNDVI, and OSAVI indices proved to be valuable tools for monitoring field crops since these indices responded to the crop growth kinetics.

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Using normalized difference vegetation index to estimate sesame drydown and seed yield

Cited by 7 publications

References 17 publications

Leveraging genomics and temporal high-throughput phenotyping to enhance association mapping and yield prediction in sesame

Leveraging genomics and temporal high-throughput phenotyping to enhance association mapping and yield prediction in sesame

Leaf water potential of field crops estimated using NDVI in ground-based remote sensing—opportunities to increase prediction precision

Use of different vegetation indices for the evaluation of the kinetics of the cherry tomato (Solanum lycopersicum var. cerasiforme) growth based on multispectral images by UAV

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