An Efficient Machine Learning Enabled Non-Destructive Technique for Remote Monitoring of Sugarcane Crop Health

Ekta Panwar; Anjana Naga Jyothi Kukunuri; Dharmendra Singh; Ashwani Kumar Sharma; Harish Kumar

doi:10.1109/access.2022.3190716

journal article Open Access Jan 01, 2022

An Efficient Machine Learning Enabled Non-Destructive Technique for Remote Monitoring of Sugarcane Crop Health

Ekta Panwar

Anjana Naga Jyothi Kukunuri

Dharmendra Singh

Ashwani Kumar Sharma Harish Kumar

IEEE Access Vol. 10 pp. 75956-75970 · Institute of Electrical and Electronics Engineers (IEEE)

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References

85

[1]

Arora "Oxidative stress and antioxidative system in plants" Current Sci. (2002)

[2]

Christy "Biochemical and molecular analysis of sugarcane genotypes response to salinity and drought" Int. J. Appl. Biol. Pharmaceutical Technol. (2013)

[3]

10.1080/1343943x.2016.1159520

[4]

10.1007/s11738-015-1935-3

[5]

10.5772/55255

[6]

10.1007/s12633-019-00228-z

[7]

10.1080/09291016.2019.1587838

[8]

10.1007/s11273-009-9169-z

[9]

10.3390/su9081337

[10]

10.1007/s00271-012-0382-9

[11]

10.2134/agronj1968.00021962006000060016x

[12]

10.1016/0034-4257(89)90069-2

[13]

10.1016/s0034-4257(01)00182-1

[14]

10.1016/s0034-4257(00)00113-9

[15]

Guyot "Utilisation de la haute resolution spectrale pour suivre letat des couverts vegetaux"

[16]

10.3390/s110707063

[17]

10.1016/j.isprsjprs.2013.04.007

[18]

10.1109/infocomtech.2018.8722386

[19]

10.1016/j.agwat.2018.05.017

[20]

Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications

Amanda Veloso, Stéphane Mermoz, Alexandre Bouvet et al.

Remote Sensing of Environment 10.1016/j.rse.2017.07.015

[21]

10.1016/j.jag.2006.05.003

[22]

10.1080/014311698213795

[23]

10.1029/2002gl016450

[24]

Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves

Anatoly A. Gitelson, Yuri Gritz †, Mark N. Merzlyak

Journal of Plant Physiology 10.1078/0176-1617-00887

[25]

10.2134/agronj2001.931131x

[26]

10.1093/jpe/rtm005

[27]

10.5194/bg-16-3069-2019

[28]

10.1016/j.agsy.2017.07.016

[29]

10.3390/rs71115203

[30]

10.3390/rs11141683

[31]

10.1007/s12042-011-9067-4

[32]

Plant salt tolerance

Jian-Kang Zhu

Trends in Plant Science 10.1016/s1360-1385(00)01838-0

[33]

10.1016/j.ecoenv.2004.06.010

[34]

Sliva "Use of physiological parameters to detect differences in drought tolerance among sugarcane genotypes"

[35]

10.1111/pce.13718

[36]

10.1038/srep25698

[37]

10.1016/j.anifeedsci.2013.04.016

[38]

10.1039/b307581h

[39]

10.1080/10106049.2015.1073369

[40]

10.1109/igarss39084.2020.9323317

[41]

10.1080/02757259509532298

[42]

10.1080/014311698215919

[43]

10.1016/j.rse.2022.112957

[44]

10.1016/j.rse.2017.08.029

[45]

Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications

Jinru Xue, Baofeng Su

Journal of Sensors 10.1155/2017/1353691

[46]

10.1016/s0034-4257(01)00274-7

[47]

10.1080/014311697217026

[48]

Use of a green channel in remote sensing of global vegetation from EOS-MODIS

Anatoly A. Gitelson, Yoram J. Kaufman, Mark N. Merzlyak

Remote Sensing of Environment 10.1016/s0034-4257(96)00072-7

[49]

10.1029/2002gl016772

[50]

10.3354/cr00936

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Details

Published: Jan 01, 2022
Vol/Issue: 10
Pages: 75956-75970
License: View

Authors

E

Ekta Panwar

Department of Biosciences and Bioengineering, IIT Roorkee, Roorkee, India

A

Anjana Naga Jyothi Kukunuri

Department of Electronics and Communication Engineering, IIT Roorkee, Roorkee, India

D

Dharmendra Singh

Department of Electronics and Communication Engineering, IIT Roorkee, Roorkee, India

A

Ashwani Kumar Sharma

Department of Biosciences and Bioengineering, IIT Roorkee, Roorkee, India

H

Harish Kumar

Department of Computer Science, College of Computer Science, King Khalid University, Abha, Saudi Arabia

Funding

Deanship of Scientific Research at King Khalid University, Abha, Saudi Arabia Award: R.G.P.2/198/43

Council of Scientific and Industrial Research, New Delhi

Drone Research Centre, IIT Roorkee

Cite This Article

Ekta Panwar, Anjana Naga Jyothi Kukunuri, Dharmendra Singh, et al. (2022). An Efficient Machine Learning Enabled Non-Destructive Technique for Remote Monitoring of Sugarcane Crop Health. IEEE Access, 10, 75956-75970. https://doi.org/10.1109/access.2022.3190716

An Efficient Machine Learning Enabled Non-Destructive Technique for Remote Monitoring of Sugarcane Crop Health

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