Spectral reflectance characteristics to distinguish Malva neglecta in wheat (Triticum aestivum)
339 / 96
Keywords:
Grain yield, Malva neglecta, NDVI, Radiance Ratio, Remote Sensing, Triticum aestivumAbstract
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
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
Published
Issue
Section
License
Copyright (c) 2014 The Indian Journal of Agricultural Sciences

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The copyright of the articles published in The Indian Journal of Agricultural Sciences is vested with the Indian Council of Agricultural Research, which reserves the right to enter into any agreement with any organization in India or abroad, for reprography, photocopying, storage and dissemination of information. The Council has no objection to using the material, provided the information is not being utilized for commercial purposes and wherever the information is being used, proper credit is given to ICAR.