Using hyperspectral remote sensing techniques to monitor nitrogen, phosphorus, sulphur and potassium in wheat (Triticum aestivum L.)

Mahajan, Gulshan; Sahoo, Rabi Narayan; Pandey, Rakesh; Gupta, Vinod Kumar; Kumar, Dinesh

doi:10.1007/s11119-014-9348-7

Cited by 143 publications

(114 citation statements)

References 33 publications

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“…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.

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“…In contrast, HSI with remote sensing examines many contiguous, narrow spectral channels (Campbell, 1996); thus, it is able to provide additional information, and no critical data are lost. HSI with remote sensing is better than MSI and demonstrates versatility for a variety of crop monitoring applications (Hunt et al, 1989;Blackburn, 1998;Champagne et al, 2003;Zhu et al, 2006;Wang et al, 2008;Prabhakar et al, 2011;Ranjan et al, 2012;Pradhan et al, 2014;Mahajan et al, 2014;Prasannakumar et al, 2014). HSI has already proven to be very effective in defense, agricultural research, and environmental applications (Lu and Chen, 1999;Ustin et al, 2004;Farley et al, 2007).…”

Section: Hsi In Agriculturementioning

confidence: 99%

Outdoor Applications of Hyperspectral Imaging Technology for Monitoring Agricultural Crops: A Review

Ahmed¹,

Yasmin²,

Mo³

et al. 2016

Journal of Biosystems Engineering

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Background: Although hyperspectral imaging was originally introduced for military, remote sensing, and astrophysics applications, the use of analytical hyperspectral imaging techniques has been expanded to include monitoring of agricultural crops and commodities due to the broad range and highly specific and sensitive spectral information that can be acquired. Combining hyperspectral imaging with remote sensing expands the range of targets that can be analyzed. Results: Hyperspectral imaging technology can rapidly provide data suitable for monitoring a wide range of plant conditions such as plant stress, nitrogen status, infections, maturity index, and weed discrimination very rapidly, and its use in remote sensing allows for fast spatial coverage. Conclusions: This paper reviews current research on and potential applications of hyperspectral imaging and remote sensing for outdoor field monitoring of agricultural crops. The instrumentation and the fundamental concepts and approaches of hyperspectral imaging and remote sensing for agriculture are presented, along with more recent developments in agricultural monitoring applications. Also discussed are the challenges and limitations of outdoor applications of hyperspectral imaging technology such as illumination conditions and variations due to leaf and plant orientation.

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“…Because leaf spectral reflectance is a function of the illumination conditions, tissue optical properties and biochemical content (chlorophyll -Chl, water, dry matter, etc.) it may be used to collect information on some fundamental biophysical variables such as colour, vegetation biomass, vegetation chlorophyll absorption characteristics, vegetation moisture content, soil moisture content, temperature and texture/surface roughness [14], [15]. Hybrid variables can be derived from fundamental variables (e.g., vegetation stress can be derived from vegetation chlorophyll absorption characteristics and moisture content) [16].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing of the Influence of Biotic Stress on Plant Biophysical Variables

Krezhova¹,

Maneva²,

Petrov³

et al. 2017

RadJ

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Abstract. Hyperspectral remote sensing provides for significant advancement in the evaluation of the subtle changes in biophysical and biochemical attributes of the crop plants. Accurate estimates of leaf pigments, nitrogen, dry matter, water content, and leaf area index (LAI) from remotely sensed data can assist in determining the vegetation physiological state. In this paper, hyperspectral remote sensing measurements of the leaf reflectance were applied for assessing the effect of biotic stress (viral infection) on the spectral behaviour and biophysical variables of young potato plants, cultivar Agata, infected with Potato Virus Y (PVY). Spectral reflectance data were collected by means of a portable fiber-optics spectrometer in the visible and near infrared spectral ranges (350-1100 nm) with a spectral resolution of 1.5 nm. For the assessment of differences between the reflectance data of healthy and infected plant data processing techniques, such as Student's t-test, first derivative analyses, and estimation of vegetation indices, were applied. The analyses were performed in green, red, red edge and near infrared spectral ranges (450-850 nm) where the differences were the most significant and give information about changes in the chlorophyll and pigment content, moisture content, cells structures, and plant stress. Several vegetation indices (NDVI -Normalized Difference Vegetation Index, fD -Disease Index, SR -Simple Ratio, TCARI -Transformed Chlorophyll Absorption Reflectance Index, etc.) were computed and the best results for assessing the changes in the physiological state of the plants gave TCARI. A strong relationship was found between the results of the spectral analyses and the serological test DAS-ELISA was applied to assess the presence and the degree of the PVY infection.

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Using hyperspectral remote sensing techniques to monitor nitrogen, phosphorus, sulphur and potassium in wheat (Triticum aestivum L.)

Cited by 143 publications

References 33 publications

Vegetation indexes and delineation of management zones for soybean1

Vegetation indexes and delineation of management zones for soybean1

Outdoor Applications of Hyperspectral Imaging Technology for Monitoring Agricultural Crops: A Review

Remote Sensing of the Influence of Biotic Stress on Plant Biophysical Variables

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