Assessment of growth, leaf N concentration and chlorophyll content of sweet sorghum using canopy reflectance

Singh, Shardendu K.; Houx, James H.; Maw, Michael J.W.; Fritschi, Felix B.

doi:10.1016/j.fcr.2017.04.009

Cited by 32 publications

(24 citation statements)

References 35 publications

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“…However, as hyperspectral cameras are becoming miniaturized and more affordable, they are becoming a viable alternative to the point spectrometers that can be mounted on ground (Busemeyer et al ., ; Deery et al ., ; Virlet et al ., ) and aerial phenotyping platforms (Gonzalez‐Dugo et al ., ; Habib et al ., ). Recent examples have demonstrated the potential of hyperspectral imaging for estimating N content in maize ( Zea mays ) and wheat (Vigneau et al ., ; Gabriel et al ., ; Singh et al ., ; Camino et al ., ). However, the workflow of imaging spectroscopy presents some challenges (Aasen et al ., ), such as the radiometric correction required to turn images into reflectance or the geometric corrections required to obtain georeferenced images.…”

Section: Biomass Radiation Use Efficiency and Photosynthesis: New Fmentioning

confidence: 98%

Field crop phenomics: enabling breeding for radiation use efficiency and biomass in cereal crops

et al. 2019

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Summary Plant phenotyping forms the core of crop breeding, allowing breeders to build on physiological traits and mechanistic science to inform their selection of material for crossing and genetic gain. Recent rapid progress in high‐throughput techniques based on machine vision, robotics, and computing (plant phenomics) enables crop physiologists and breeders to quantitatively measure complex and previously intractable traits. By combining these techniques with affordable genomic sequencing and genotyping, machine learning, and genome selection approaches, breeders have an opportunity to make rapid genetic progress. This review focuses on how field‐based plant phenomics can enable next‐generation physiological breeding in cereal crops for traits related to radiation use efficiency, photosynthesis, and crop biomass. These traits have previously been regarded as difficult and laborious to measure but have recently become a focus as cereal breeders find genetic progress from ‘Green Revolution’ traits such as harvest index become exhausted. Application of LiDAR, thermal imaging, leaf and canopy spectral reflectance, Chl fluorescence, and machine learning are discussed using wheat and sorghum phenotyping as case studies. A vision of how crop genomics and high‐throughput phenotyping could enable the next generation of crop research and breeding is presented.

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Section: Biomass Radiation Use Efficiency and Photosynthesis: New Fmentioning

confidence: 98%

Field crop phenomics: enabling breeding for radiation use efficiency and biomass in cereal crops

et al. 2019

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“…The use of other variables for N fertilization recommendation, such as shoot biomass production and amount of accumulated N in the plant, is limited because of the delay in the evaluation procedure, and because this requires laboratory analyses (BREDEMEIER et al, 2016); although, these variables are reliable indicators of the response to top dressed nitrogen application. Contrastingly, the evaluation of canopy reflectance can help estimate the nutritional status of plants in relation to N, determining its spatial variability in a field (SINGH et al, 2017). Optical reflectance canopy sensors mounted on agricultural machines, such as Greenseeker ® , N-Sensor ® , and Crop Circle ® , enable real-time reflectance readings with high spatial resolution, permitting variable rate nitrogen fertilization (COLAÇO & BRAMLEY, 2018).…”

Section: Soil Sciencementioning

confidence: 99%

“…Increase in N concentration produces changes in spectral reflectance Vian et al that can be detected using remote sensors. Leaves with low N accumulation, and consequently lower chlorophyll content have higher reflectance in the visible region of the electromagnetic spectrum (400-700nm) and low reflectance in the near-infrared region (DIACONO et al, 2013;SINGH et al, 2017), causing a decrease in the Normalized difference vegetation index (NDVI). Increase in the amount of accumulated N promotes an increase in chlorophyll content, and consequently higher absorption and lower reflectance of the red spectrum (SHANAHAN et al, 2008).…”

Section: Models For Estimation Of Shoot Biomass and Amount Of Accumulmentioning

confidence: 99%

Nitrogen management in wheat based on the normalized difference vegetation index (NDVI)

et al. 2018

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Biomass production and nitrogen (N) accumulated in wheat shoots may be used for quantifying optimal topdressing nitrogen doses. The objective of this study was to develop and validate models for estimating the amount of biomass and nitrogen accumulated in shoots and the N topdressing dose of maximum technical efficiency in wheat using the normalized difference vegetation index (NDVI) measured by an active optical canopy sensor. Field experiments were carried out in two years and treatments consisted of N doses applied at plant emergence and as topdressing. NDVI, shoot biomass and N accumulated in shoots at the growth stage of six fully expanded leaves and grain yield were evaluated, being determined the topdressing N dose of maximum technical efficiency (DMTE). The NDVI was positively correlated to shoot biomass and N content in shoots and models for the relationship between these variables were developed and validated. The DMTE was negatively correlated with the NDVI value evaluated at the moment of N topdressing application. Thus, NDVI evaluation by an active optical canopy sensor can be used for nitrogen fertilization in variable rate, allowing the adjustment of applied N doses in different areas within a field.

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“…The nutrient analysis of whole plants or organs such as leaf, petiole, composition of sap or activity of certain enzymes in plant, and the estimation of leaf color or photosynthetic pigments are some of the methods used to detect nutrient stress ( Scaife and Bray , ; Besford , ; Bell et al, ; Singh et al, ). Moreover, utilization of the leaf spectral reflectance properties has also been proposed to detect a nutrient deficiency in crops ( Bausch and Diker , ; Singh et al, ). However, some nutrient stresses, such as K deficiency, in soybean often limit growth and productivity before any visible symptoms occur ( Singh and Reddy , ), which limits early detection of stress by assessing leaf color, photosynthetic pigments, or leaf spectral properties.…”

Section: Introductionmentioning

confidence: 99%

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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Assessment of growth, leaf N concentration and chlorophyll content of sweet sorghum using canopy reflectance

Cited by 32 publications

References 35 publications

Field crop phenomics: enabling breeding for radiation use efficiency and biomass in cereal crops

Field crop phenomics: enabling breeding for radiation use efficiency and biomass in cereal crops

Nitrogen management in wheat based on the normalized difference vegetation index (NDVI)

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

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