Varietal discrimination of common dry bean (<i>Phaseolus vulgaris</i>L.) grown under different watering regimes using multitemporal hyperspectral data

Rajah, Perushan; Odindi, John; Abdel‐Rahman, Elfatih M.; Mutanga, Onisimo; Modi, Albert T.

doi:10.1117/1.jrs.9.096050

Cited by 12 publications

(4 citation statements)

References 19 publications

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“…ViewSpecProR software was used for the computation 42 . The spectral regions between 350 and 399 nm, 1355 and 1420 nm, 1810 and 1940 nm, and 2470 and 2500 nm have been suggested to be noisy and were thus removed from the analysis 39 , 43 , 44 . On the resulting 1873 bands, iPLSR in forward mode was applied to select the best spectral intervals for estimating LAI at different periods (onset, middle, and end) within summer.…”

Section: Methodsmentioning

confidence: 99%

Comparison of partial least squares and support vector regressions for predicting leaf area index on a tropical grassland using hyperspectral data

Kiala

Odindi

Mutanga

et al. 2016

J. Appl. Remote Sens

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.Leaf area index (LAI) is a key biophysical parameter commonly used to determine vegetation status, productivity, and health in tropical grasslands. Accurate LAI estimates are useful in supporting sustainable rangeland management by providing information related to grassland condition and associated goods and services. The performance of support vector regression (SVR) was compared to partial least square regression (PLSR) on selected optimal hyperspectral bands to detect LAI in heterogeneous grassland. Results show that PLSR performed better than SVR at the beginning and end of summer. At the peak of the growing season (mid-summer), during reflectance saturation, SVR models yielded higher accuracies (R2=0.902 and RMSE=0.371 m2 m−2) than PLSR models (R2=0.886 and RMSE=0.379 m2 m−2). For the combined dataset (all of summer), SVR models were slightly more accurate (R2=0.74 and RMSE=0.578 m2 m−2) than PLSR models (R2=0.732 and RMSE=0.58 m2 m−2). Variable importance on the projection scores show that most of the bands were located in the near-infrared and shortwave regions of the electromagnetic spectrum, thus providing a basis to investigate the potential of sensors on aerial and satellite platforms for large-scale grassland LAI prediction.

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Section: Methodsmentioning

confidence: 99%

Comparison of partial least squares and support vector regressions for predicting leaf area index on a tropical grassland using hyperspectral data

Kiala

Odindi

Mutanga

et al. 2016

J. Appl. Remote Sens

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“…To account for any changes in the atmospheric condition and the sun irradiance, reflectance measurements were recorded with frequent normalisation using the standard panel. 34 In total, 15 replicates of canopy reflectance within each subplot were collected and averaged, allowing for elimination of measurement noise arising from soil background.…”

Section: Data Collectionmentioning

confidence: 99%

Potential of interval partial least square regression in estimating leaf area index

Kiala¹,

Odindi²,

Mutanga³

2017

S. Afr. J. Sci.

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ARTICLE INCLUDES:× Supplementary material × Data set FUNDING:None Leaf area index (LAI) is a critical parameter in determining vegetation status and health. In tropical grasslands, reliable determination of LAI, useful in determining above ground biomass, provides a basis for rangeland management, conservation and restoration. In this study, interval partial least square regression (iPLSR) in forward mode was compared to partial least square regression (PLSR) to estimate LAI from in-situ canopy hyperspectral data on a heterogeneous grassland at different periods (onset, mid and end) during summer. The performance of the two techniques was determined using the least relative root mean square error to the mean (nRMSEP) and the highest coefficients of determination (R 2 p ) between the predicted and the measured LAI. Results show that iPLSR models could explain LAI variation with R 2 p values ranging from 0.81 to 0.93 and low nRMSEP from 9.39% to 24.71%. The highest accuracies for estimates of LAI using iPLSR were at mid-and end of summer (R 2 p = 0.93 and nRMSEP = 9.39%; R 2 p = 0.89 and nRMSEP = 10.50%, respectively). Pooling data sets from the three assessed periods yielded the highest prediction error (nRMSEP=24.71%). Results show that iPLSR performed better than PLSR, which yielded R 2 pand RMSEP values ranging from 0.36 to 0.65 and from 28.44% to 33.47%, respectively. Overall, this study demonstrates the value of iPLSR in predicting LAI and therefore provides a basis for more accurate mapping and monitoring of canopy characteristics of tropical grasslands. Significance:• The relationship between LAI and canopy reflectance can be used in iPLSR modelling to provide more accurate mapping and monitoring of canopy characteristics for land management and conservation.

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“…Applications in plant-based industries include fruit, grain and seed quality, particularly pathogen infestation and varietal purity, as well as chemical composition. The list of applications continues to grow rapidly [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Near-Infrared Hyperspectral Imaging Pipelines for Pasture Seed Quality Evaluation: An Overview

Reddy

Guthridge

Panozzo

et al. 2022

Sensors

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Near-infrared (800–2500 nm; NIR) spectroscopy coupled to hyperspectral imaging (NIR-HSI) has greatly enhanced its capability and thus widened its application and use across various industries. This non-destructive technique that is sensitive to both physical and chemical attributes of virtually any material can be used for both qualitative and quantitative analyses. This review describes the advancement of NIR to NIR-HSI in agricultural applications with a focus on seed quality features for agronomically important seeds. NIR-HSI seed phenotyping, describing sample sizes used for building high-accuracy calibration and prediction models for full or selected wavelengths of the NIR region, is explored. The molecular interpretation of absorbance bands in the NIR region is difficult; hence, this review offers important NIR absorbance band assignments that have been reported in literature. Opportunities for NIR-HSI seed phenotyping in forage grass seed are described and a step-by-step data-acquisition and analysis pipeline for the determination of seed quality in perennial ryegrass seeds is also presented.

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Varietal discrimination of common dry bean (Phaseolus vulgarisL.) grown under different watering regimes using multitemporal hyperspectral data

Cited by 12 publications

References 19 publications

Comparison of partial least squares and support vector regressions for predicting leaf area index on a tropical grassland using hyperspectral data

Comparison of partial least squares and support vector regressions for predicting leaf area index on a tropical grassland using hyperspectral data

Potential of interval partial least square regression in estimating leaf area index

Near-Infrared Hyperspectral Imaging Pipelines for Pasture Seed Quality Evaluation: An Overview

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