Quantification of leaf pigments in soybean (Glycine max (L.) Merr.) based on wavelet decomposition of hyperspectral features

Singh, Shardendu K.; Hoyos‐Villegas, Valerio; Ray, Jeffery D.; Smith, James R.; Fritschi, Felix B.

doi:10.1016/j.fcr.2013.04.019

Cited by 30 publications

(42 citation statements)

References 30 publications

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“…In soybean crop, studies using proximal spectral sensors, i.e., with the plant canopy measured within a distance of less than 2 m, have been correlated with the plant biochemistry (Singh et al 2013) and also used for weed discrimination (Koger et al 2003, Safanelli et al 2014. Studies on the in situ crop monitoring using proximal sensors within precision agriculture approaches have also been conducted with wheat, barley and forage crops (Abrahão et al 2009, Mahajan et al 2014, Xu et al 2014.…”

Section: Abstract Resumomentioning

confidence: 99%

Vegetation indexes and delineation of management zones for soybean1

Kuiawski

Safanelli

Bottega

et al. 2017

Pesqui. Agropecu. Trop.

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The delimitation of site-specific management zones may be an operational and economically feasible approach in precision agriculture. This study aimed at investigating the spatial correlations between spectral indexes sampled during different growth stages of soybean and crop yield. Soil attributes stratified in each zone and the influence of altitude were also assessed. The simple ratio index, normalized difference vegetation index and soil-adjusted vegetation index were calculated for soybean at the V6, R5 and R5.5 stages. Spatial dependence analysis via semivariogram was performed for the vegetation indexes, soybean yield and terrain elevation. The crop yield map was taken as a reference to assess the spatial agreement with the different maps generated from the spectral indexes. The average values for chemical and granulometric soil attributes were calculated and analyzed by their means among the zones delineated. The field division into two management zones, due to the combination of altitude, simple ratio index of the V6 stage and soil-adjusted vegetation index of the R5.5 stage, showed the highest agreement with the soybean yield map. Differences between the delineated zones were identified for the phosphorus, clay and silt contents.

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Section: Abstract Resumomentioning

confidence: 99%

Vegetation indexes and delineation of management zones for soybean1

Kuiawski

Safanelli

Bottega

et al. 2017

Pesqui. Agropecu. Trop.

View full text Add to dashboard Cite

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“…Based on the Table 1 which is from the previous study, the total chlorophyll at a mature leaf were the highest sampling [4]. …”

Section: A Chlorophyll Concentrationmentioning

confidence: 98%

“…Wavelet (wavelet family) is a function of the change (or down) the original reflectance spectrum into sub-components of the spectrum carries information from the original spectra [4] [6] [7]. Wavelet transform can be implemented as a continuous wavelet transforms (CWT).…”

Section: Iispectral Reflectancementioning

confidence: 99%

“…Descriptive statistic must be calculated to determine the relation of spectra reflectance and chlorophyll [4]. Based on the Table 1 which is from the previous study, the total chlorophyll at a mature leaf were the highest sampling [4].…”

Section: A Chlorophyll Concentrationmentioning

confidence: 99%

“…The new approachment which is wavelet transformation has proven that produced a better result compared to untransformed spectra [4] …”

Section: B Reflectance Models Developmentmentioning

confidence: 99%

See 2 more Smart Citations

A review on quantification of tree leaf pigment using wavelet analysis and remote sensing

Mustafa

Ya’acob

Latif

2014

2014 IEEE 5th Control and System Graduate Research Colloquium

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Accurate prediction of leaf pigment from spectral reflectance is important because it allows non destructive and rapid assessment of oil palm tree. There are many technique can be use to transform spectra reflectance for instance Derivatives Analysis, Artificial Neural Network, and Mathemathical formulation by adding, substracting or dividing the wavebands. Among the methods, wavelet analysis have been exploited for determine predictive capability of spectral reflectance model development for chlorophyll quantification. Thus with the improvement of spectral and spatial resolutions of image satellite, remote sensing technique are applicable in monitoring chlorophyll. The selected wavebands for monitoring chlorophyll consisted of the visible (400-750nm) as compared to the NIR (750-1350nm). This research can be useful to develop models for chlorophyll concentration estimation based on leaf spectra reflectance using wavelet analysis and covering large area for monitoring chlorophyll using remote sensing technique.

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Seasonal critical concentration and relationships of leaf phosphorus and potassium status with biomass and yield traits of soybean

Singh

Reddy

Sicher

2018

J. Plant Nutr. Soil Sci.

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Analysis of uppermost fully expanded leaves is useful to detect a deficiency of mineral nutrients such as phosphorus (P) and potassium (K) in soybean. Although, the leaf P or K status aids in fertilizer management, information on nutrient seasonal relationships with growth and yield traits at maturity are limited. To investigate this, soybean was grown under varying P or K nutrition under ambient and elevated CO 2 concentrations. Results show significant relationships of the relative total biomass and yield-related traits with the foliar P and K concentrations measured several times in the season across CO 2 levels. However, the relationships established earlier in the season showed that the growth period between 25 and 37 d after planting (DAP), representing the beginning of flowering and pod, respectively, is the best for leaf sampling to determine the foliar P or K status. The leaf P and K status as well as the critical leaf P (CLPC) and K (CLKC) concentrations for traits such as seed yield peaked around 30 DAP (R2 stage) and tended to decline thereafter with the plant age. The CLPC and CLKC of seed yield indicate that the leaf P and K concentration of at least 2.74 mg g -1 and 19.06 mg g -1 , respectively, in the uppermost fully expanded leaves are needed between 25 and 37 DAP for near-optimum soybean yield. Moreover, the greatest impact of P and K deficiency occurred for the traits that contribute the most to the soybean yield (e.g., relative total biomass, seed yield, pod and seed numbers), while traits such as seed number per pod, seed size, and shelling percentages were the least affected and showed smaller leaf critical concentration. The CLPC or CLKC for biomass and seed yield was greater under elevated CO 2 24-25 DAP but varied thereafter. These results are useful to researchers and farmers to understand the dynamics of the relationship of pre-harvest leaf P and K status with soybean productivity at maturity, and in the determination of suitable growth stage to collect leaf samples.

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Quantification of leaf pigments in soybean (Glycine max (L.) Merr.) based on wavelet decomposition of hyperspectral features

Cited by 30 publications

References 30 publications

Vegetation indexes and delineation of management zones for soybean1

Vegetation indexes and delineation of management zones for soybean1

A review on quantification of tree leaf pigment using wavelet analysis and remote sensing

Seasonal critical concentration and relationships of leaf phosphorus and potassium status with biomass and yield traits of soybean

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