Spectral reflectance characteristics to distinguish Malva neglecta in wheat (Triticum aestivum)


339 / 96

Authors

  • RAMANJIT KAUR Indian Agricultural Research Institute, New Delhi 110 012
  • MANPREET JAIDKA Punjab Agricultural University, Ludhiana 141 004

https://doi.org/10.56093/ijas.v84i10.44208

Keywords:

Grain yield, Malva neglecta, NDVI, Radiance Ratio, Remote Sensing, Triticum aestivum

Abstract

A field experiment was carried out to distinguishing Malva neglecta from wheat (Triticum aestivum L.) crop based on their spectral reflectance characteristics through remote sensing during rabi seasons of 2010-11and 2011-12. The investigation consists of six treatments each having different population levels of Malva neglecta, viz. 0, 3, 6, 9, 12 plants/m2 and a treatment having pure population or solid stand of Malva neglecta. The results indicated a decreasing trend in effective tillers, number of grains/ear, 1000-grain weight and grain yield of wheat with increasing population densities of Malva neglecta from 3 to 12 plants/m2. Highest grain yield of wheat (5.75 tonnes/ha) was recorded under pure wheat treatment (solid stand) and lowest grain yield (3.24 tonnes/ha) was recorded in treatment having 12 plants of Malva neglecta/m2. Higher radiance ratio and NDVI values were recorded in pure wheat treatment and minimum in pure weed treatment. It was observed that by using radiance ratio and NDVI, pure wheat can be distinguished from pure populations of Malva neglecta after 30 DAS and remain distinguished up to 120 DAS and different levels of weed population can be discriminated amongst themselves from 60 DAS onwards. From the study it was concluded that remote sensing technology can be used for identification of different weed species and their infestations in field crops. Weed prescription maps can be prepared with Geographic Information System (GIS), on the basis of which farmers can be advised to take the preventive control measures.

Downloads

Download data is not yet available.

References

Andrew T S, Morrison I N and Penner G A. 1998. Monitoring the spread of ACCase inhibitor resistance among wild oat (Avena fatua) patches using AFLP analysis. Weed Science 46: 196–9. DOI: https://doi.org/10.1017/S004317450009041X

Anne M Smith and Robert E. Blackshaw. 2003. Weed–crop discrimination using remote sensing: A detached leaf experiment. Weed Technology 17(4): 811–20. DOI: https://doi.org/10.1614/WT02-179

Aparicio N, Villegas D, Casadesus J, Araus J L and Royo C. 2000. Spectral vegetation indices as non-destructive tools for determining durum wheat yield. Agronomy Journal 92: 83–91. DOI: https://doi.org/10.2134/agronj2000.92183x

Brown R B and Steckler J P G A. 1995. Prescription maps for spatially variable herbicide applications in no-till corn. Transactions of the American Society of Agricultural Engineers 38: 1 659–66. DOI: https://doi.org/10.13031/2013.27992

Chang J, Clay S A, Clay D E and Dalsted K. 2004. Detecting weed free and weed infested areas of a soybean field using near infrared spectral data. Weed Science 52: 642–8. DOI: https://doi.org/10.1614/WS-03-074R1

Chang Kuo-Wei, Shen Y and LoJeng Chung. 2005. Predicting rice yield using conopy reflectance measured at booting stage. Agronomy Journal 97: 872–8. DOI: https://doi.org/10.2134/agronj2004.0162

Everitt J H, Alaniz, M A, Escobar D E and Davis M R. 1992. Using remote sensing to distinguish common golden weed (Isocoma coronopifolia) and Drummond Goldenweed ( Isocoma drummondii). Weed Science 40: 621–8. DOI: https://doi.org/10.1017/S0043174500058215

Everitt J H and Deloach C J. 1990. Remote sensing of Tamarix chinensis and associated vegetation. Weed Science 38: 273–5. DOI: https://doi.org/10.1017/S0043174500056526

Francisca López-Granados, Montse Jurado-Expósito, Jose M. Peña-Barragán, and Luis García-Torres. 2006. Using remote sensing for identification of late-season grass weed patches in wheat. Weed Science 54(2): 346–53. DOI: https://doi.org/10.1614/WS-05-54.2.346

Gibson K D, Richard D, Medlin C R and Johnson L. 2004. Detection of weed species in soybean using multispectral digital images. Weed Technology 18(3): 742–9. DOI: https://doi.org/10.1614/WT-03-170R1

Lamb D W and Brown R B. 2001. Remote-sensing and mapping of weeds in crops. Journal of Agricultural Engineering Research 78(2): 117–25. DOI: https://doi.org/10.1006/jaer.2000.0630

Goel P K, Prasher S O, Landrya J A, Patel R M, Bonnell R B, Viaub A A, and Miller J R. 2003. Potential of airborne hyperspectral remote sensing to detect nitrogen de?ciency and weed infestation in corn. Computers and Electronics in Agriculture 38: 99–124. DOI: https://doi.org/10.1016/S0168-1699(02)00138-2

Meisner C A, Acevedo E, Flores D, Sayre K, Ortiz-Monastero I, Byerlee D and Limon A. 1992. Wheat production and grower practices in the Yaqui Valley, Sonora, Mexico D F.

Mortensen D A, Johnson G A, Wyse D Y and Martin A R. 1995. Managing spatially variable weed populations. (In) Proceedings of Site-Specific Management for Agricultural Systems. Roberts P C, Rust R H, and Larson W E, (Eds). 2nd Annual Conference; Minneapolis, MN, March 27–30, 1994. Madison, WI: ASA- CSSA-SSSA, pp 398–415.

Kaur Ramanjit, Mahey R K, Mukherjee J. 2010. Study of the optimum time span for distinguishing Avena ludoviciana from wheat crop based on their spectral reflectance characteristics. Journal of the Indian Society of Remote Sensing 38(3): 25–34 DOI: https://doi.org/10.1007/s12524-010-0009-7

Shepherd J D and Lee W G. 2007. Satellite mapping of Gorse atregional scales. http: //www. Landcareresearch.co.nz.

Thenkabail P S, Smith RB and Pauw E D. 2000. Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing of Environment 71(2): 158–82. DOI: https://doi.org/10.1016/S0034-4257(99)00067-X

Walia U S, Seema J, Brar L S and Singh M. 2001. Competitive ability of wheat with variable population of wild oats (Avena ludoviciana Dur.). Indian Journal of Weed Science 33: 171–3.

Yang Chwen-Mingand and S U Muh-ROng. 2000. Analysis of spectral characteristics of rice canopy under water deficiency: Monitoring changes of spectral characteristics of dehydrating rice canopy. The 21st Asian Conference on Remote Sensing, December 4-8, pp 13–8.

Downloads

Submitted

2014-10-15

Published

2014-10-15

Issue

Section

Articles

How to Cite

KAUR, R., & JAIDKA, M. (2014). Spectral reflectance characteristics to distinguish Malva neglecta in wheat (Triticum aestivum). The Indian Journal of Agricultural Sciences, 84(10), 1243–9. https://doi.org/10.56093/ijas.v84i10.44208
Citation