Use of ground based hyperspectral remote sensing for detection of stress in cotton caused by leafhopper (Hemiptera: Cicadellidae)

Prabhakar, M.; Prasad, Y. G.; Thirupathi, Merugu; Sreedevi, G.; Dharajothi, B.; Venkateswarlu, B.

doi:10.1016/j.compag.2011.09.012

Cited by 93 publications

(38 citation statements)

References 50 publications

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“…Prabhakar and others through regression analysis revealed a significant linear relation between leafhopper severity and chlorophyll. And select the sensitive bands leading to the identification of new leaf hopper indices with a potential to detect leaf hopper severity, so as to achieve effective identification of victim's cotton [19]. Minet and others propose to estimate the performance of the matched filter with respect to the number of spectral bands.…”

Section: A Overseas Research Status Of Band Selection For Hyperspectmentioning

confidence: 99%

Research Advance on Band Selection-Based Dimension Reduction of Hyperspectral Remote Sensing Images

Wang

Huang

Liu

et al. 2012

2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering

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The typical characteristics of hyperspectral remote sensing data are combining image with spectrum, high spectral resolution, many bands and much redundant information. A hyperspectral image cube can be used to obtain millions spectrum curves. Aiming at a hyperspectral remote sensing image containing huge amounts of data, removing redundant information and reducing processing dimensions are the premise and foundation for hyperspectral remote sensing processing and applications. Hyperspectral data dimension reduction techniques mainly include the feature extraction and band selection. This paper has fully studied the theories and methods of dimension reduction for band selection, analyses their advantages, disadvantages and validity, and deeply discusses the current situation and tendency of the development of band selection based on dimension reduction of hyperspectral remote sensing image at last.

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Section: A Overseas Research Status Of Band Selection For Hyperspectmentioning

confidence: 99%

Research Advance on Band Selection-Based Dimension Reduction of Hyperspectral Remote Sensing Images

Wang

Huang

Liu

et al. 2012

2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering

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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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“…For many purposes, remote sensing techniques provide means of measuring the characteristics of the objects on the Earth surface and for detecting environmental changes that occur as a result of human activities or natural processes [1], [2]. Recently high spectral resolution sensors have been developed, which allow new and more advanced applications in agriculture -spectral discrimination of crops and their genotypes [3], quantitative estimation of different biophysical and biochemical parameters through empirical and physical modelling [4], [5], assessing of abiotic and biotic stresses [6]- [8].…”

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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Use of ground based hyperspectral remote sensing for detection of stress in cotton caused by leafhopper (Hemiptera: Cicadellidae)

Cited by 93 publications

References 50 publications

Research Advance on Band Selection-Based Dimension Reduction of Hyperspectral Remote Sensing Images

Research Advance on Band Selection-Based Dimension Reduction of Hyperspectral Remote Sensing Images

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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