Impact of nitrogen and environmental conditions on corn as detected by hyperspectral reflectance

Strachan, Ian B.; Pattey, E.; Boisvert, J. B.

doi:10.1016/s0034-4257(01)00299-1

Cited by 251 publications

(146 citation statements)

References 29 publications

Supporting

Mentioning

135

Contrasting

Unclassified

Order By: Relevance

“…This indicates that the sensor is better at detecting where nutrients are limited rather than where they are in excess. This study attained comparable accuracy with the hyperspectral work of Strachan et al (2002) who used canonical discriminant analysis to accurately classify different levels of crop nutrition (varying rates of nitrogen) using reflectance data with overall success rates varying from 70-93%, depending on timing during the season. The PLS regression results showed that it is possible to predict grain yield using digital camera imagery obtained from a balloon platform.…”

Section: Discriminant Function Analysis To Predict the Amount Of Fertmentioning

confidence: 64%

See 1 more Smart Citation

Detecting the attributes of a wheat crop using digital imagery acquired from a low-altitude platform

Jensen

Apan

Young

et al. 2007

Computers and Electronics in Agriculture

View full text Add to dashboard Cite

A low-altitude platform utilising a 1.8-m diameter tethered helium balloon was used to position a multispectral sensor, consisting of two digital cameras, above a fertiliser trial plot where wheat (Triticum spp.) was being grown. Located in Cecil Plains, Queensland, Australia, the plot was a long-term fertiliser trial being conducted by a fertiliser company to monitor the response of crops to various levels of nutrition. The different levels of nutrition were achieved by varying nitrogen application rates between 0-120 units of N at 40 unit increments. Each plot had received the same application rate for 10 years. Colour and nearinfrared images were acquired that captured the whole 2 ha plot. These images were examined and relationships sought between the captured digital information and the crop parameters imaged at anthesis and the at-harvest quality and quantity parameters. The statistical analysis techniques used were correlation analysis, discriminant analysis, and partial least squares regression. A high correlation was found between the image and yield (R 2 = 0.91) and a moderate correlation between the image and grain protein content (R 2 = 0.66). The utility of the system could be extended by choosing a more mobile platform. This would increase the potential for the system to be used to diagnose the causes of the variability and allow remediation, and/or to segregate the crop at harvest to meet certain quality parameters.Keywords: grain protein, grain yield, low altitude, digital camera This is the authors' final pre-print version of: Jensen, Troy and Apan, Armando and Young, Frank and Zeller, Lez (2007) Detecting the attributes of a wheat crop using digital 3D imagery acquired from a low-altitude platform.

show abstract

Section: Discriminant Function Analysis To Predict the Amount Of Fertmentioning

confidence: 64%

“…The DA procedure generates one or more discriminant functions based on linear combinations of the predictor variables that provide the best discrimination between groups. It has been used successfully in spectral discrimination studies (Strachan, et al 2002). The entire sample set of 120 was used in the DA calculations.…”

Section: Data Pre-processing and Analysismentioning

confidence: 99%

Detecting the attributes of a wheat crop using digital imagery acquired from a low-altitude platform

Jensen

Apan

Young

et al. 2007

Computers and Electronics in Agriculture

View full text Add to dashboard Cite

show abstract

“…There are numerous experimental studies [49,54,66,86,90,93,96] that were devoted to the analysis of the connection between PRI and photosynthetic parameters. The results require theoretical investigations that analyze the current experimental data.…”

Section: Discussionmentioning

confidence: 99%

Connection of the Photochemical Reflectance Index (PRI) with the Photosystem II Quantum Yield and Nonphotochemical Quenching Can Be Dependent on Variations of Photosynthetic Parameters among Investigated Plants: A Meta-Analysis

Sukhova

Sukhov

2018

Remote Sensing

View full text Add to dashboard Cite

Abstract:The development of spectral methods of remote sensing, including measurement of a photochemical reflectance index (PRI), is a prospective trend in precision agriculture. There are many works which have investigated the connection between photosynthetic parameters and PRI; however, their results varied and were sometimes contradictory. For this paper, we performed a meta-analysis of works in this field. Here, only linear correlations of PRI with photosynthetic parameters-including quantum yield of photosystem II (∆F/Fm'), nonphotochemical quenching of chlorophyll fluorescence (NPQ), and light use efficiency (LUE)-were investigated. First, it was shown that the correlations were dependent on conditions of PRI measurements (leaf or canopy; artificial light or sunlight). Second, it was shown that a minimal level of the photosynthetic stress, and the variation of this level among investigated plants, can influence the linear correlation of PRI with ∆F/Fm' and NPQ; the effect was dependent on conditions of measurements. In contrast, the distribution of LUE among plants did not influence its correlation with PRI. Thus, the meta-analysis shows that the distribution of photosynthetic parameters among investigated plants can be an important factor that influences the efficiency of remote sensing on the basis of the PRI measurement.

show abstract

“…Vegetation indices are combinations of surface reflectance values at two or more wavelengths designed to highlight a particular property of vegetation. Different theoretical models based on leaf reflectance can be used to predict leaf Chl content, water content, and other variables associated with vegetative structure (Devitt et al, 2005;Strachan et al, 2002). Normalized differentiation vegetation index (NDVI) is a sensitive indicator of canopy structure, green biomass, green leaf area index, Chl content, and foliar N concentration (Bajwa et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Using Chlorophyll Fluorescence and Vegetation Indices to Predict the Timing of Nitrogen Demand in Pentas lanceolata

Wu¹,

Lin²,

Lee³

et al. 2015

HST

View full text Add to dashboard Cite

The objective of this study was to predict the timing of nitrogen (N) demand through analyzing chlorophyll fluorescence (ChlF), soil-plant analysis development (SPAD), and normalized difference vegetation index (NDVI), which are positively correlated with foliar N concentration in star cluster (Pentas lanceolata). The plants were grown in potting soil under optimal conditions for 30 d, followed by weekly irrigation with five concentrations (0, 4, 8, 16, and 24 mM) of N for an additional 30 d. These five N application levels corresponded to leaf N concentrations of 2.62, 3.48, 4.00, 4.23, and 4.69%, respectively. We measured 13 morphological and physiological parameters, as well as the responses of these parameters to various N-fertilizer treatments. The general increases in Dickson's quality index (DQI), above-ground dry weight (DW), total DW, flowering rate, △F/Fm', and qP in response to treatment with 0 to 8 mM N were similar to those of SPAD, NDVI, and Fv/Fm. Consistent and strong correlations (R 2 = 0.60 to 0.85) were observed between leaf N concentration (%) and SPAD, NDVI, △F/Fm', and above-ground DW. Validation of leaf SPAD, NDVI, and △F/Fm' revealed that these vegetation indices are accurate predictors of leaf N concentration that can be used for non-destructive estimation of the proper timing for N-solution irrigation of P. lanceolata. Moreover, irrigation with 8 mM N-fertilizer is recommended when leaf N concentration, SPAD, NVDI, and △F/Fm' ratios are reduced from their saturation values of 4.00, 50.68, 0.64, and 0.137%, respectively. Additional key words:Dickson quality index (DQI), non-destructive, normalized differentiation vegetation index (NDVI), photosystem II (PSII), reflectance spectra

show abstract

Impact of nitrogen and environmental conditions on corn as detected by hyperspectral reflectance

Cited by 251 publications

References 29 publications

Detecting the attributes of a wheat crop using digital imagery acquired from a low-altitude platform

Detecting the attributes of a wheat crop using digital imagery acquired from a low-altitude platform

Connection of the Photochemical Reflectance Index (PRI) with the Photosystem II Quantum Yield and Nonphotochemical Quenching Can Be Dependent on Variations of Photosynthetic Parameters among Investigated Plants: A Meta-Analysis

Using Chlorophyll Fluorescence and Vegetation Indices to Predict the Timing of Nitrogen Demand in Pentas lanceolata

Contact Info

Product

Resources

About