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S. K. Singh Sujay Dutta Nishith Dharaiya

Abstract

Detection of crop stress is one of the major applications of remote sensing in agriculture. Many researchers have confirmed the ability of remote sensing techniques for detection of pest/disease on cotton. The objective of the present study was to evaluate the relation between the mealybug severity and remote sensing indices and development of a model for mapping of mealybug damage using remote sensing indices. The mealybug-infested cotton crop had a significantly lower reflectance (33%) in the near infrared region and higher (14%) in the visible range of the spectrum when compared with the non-infested cotton crop having near infrared and visible region reflectance of 48 % and 9% respectively. Multiple Linear regression analysis showed that there were varying relationships between mealybug severity and spectral vegetation indices, with coefficients of determination (r2) ranging from 0.63 to
0.31. Model developed in this study for the mealybug damage assessment in cotton crop yielded significant relationship (r2=0.863) and was applied on satellite data of 21st September 2009 which revealed high severity of mealybug and it was low on 24th September 2010 which confirmed the significance of the model and can be used in the identification of mealybug infested cotton zones. These results indicate that remote sensing data have the potential to distinguish damage by mealybug and quantify its abundance in cotton.

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Keywords

LST, Mealybug, MPSI-2, MPSI-8, Remote sensing, Severity index, TVDI

References
Aggarwal, P.K., Kalra, N., Chander, S. and Pathak, H. (2006). Info Crop: a dynamic simulation model for the assessment of crop yields, losses due to pests, and envi-ronmental impact of agro-ecosystems in tropical envi-ronments. I. Model description. Agricultural systems, 89(1): 1-25
Blackburn, G.A. (1998a). Spectral indices for estimating photosynthetic pigment concentrations: a test using senescent tree leaves. International Journal of Remote Sensing, 19(4): 657-675
Charleston, K., Addison, S., Miles, M. and Maas, S. (2010). The Solenopsis mealy bug outbreak in Emerald. The Australian Cotton Grower, 31: 18–22
Dutta, S., Singh, S.K. and Khullar, M. (2014a). A case study on forewarning of yellow rust affected areas on wheat crop using satellite data. Journal of the Indian Society of Remote Sensing, 42(2): 335-342
Dutta, S., Singh, S.K. and Panigrahy, S. (2014b). Assessment of late blight induced diseased potato crops: A Case Study for West Bengal District Using Temporal AWiFS and MODIS Data. Journal of the Indian Society of Remote Sensing, 42(2): 353-361
El-Khawas, M. and El-Khawas, M. (2008). Interactions be-tween Aphis gossypii (Glov.) and the common preda-tors in eggplant and squash fields, with evaluating the physiological and biochemical aspects of biotic stress induced by two different aphid species, infesting squash and cabbage plants. Australian Journal of Basic and Applied Sciences, 2(2): 183-193
Franzen, L.D., Gutsche, A.R., Heng-Moss, T.M., Higley, L. G., Sarath, G. and Burd, J.D. (2007). Physiological and biochemical responses of resistant and susceptible wheat to injury by Russian wheat aphid. Journal of Economic Entomology, 100(5): 1692-1703
Gamon, J. and Surfus, J. (1999). Assessing leaf pigment content and activity with a reflecto meter. New Phytolo-gist, 143(1): 105-117.
Gates, D. M. (1970). Physical and physiological properties of plants. Remote Sensing with Special Reference to Agriculture and Forestry: With Special Reference to Agriculture and Forestry, 224-252
Gausman, H. and Hart, W. (1974). Reflectance of sooty mold fungus on citrus leaves over the 2.5 to 40-micrometer wavelength interval. Journal of Economic Entomology, 67(4): 479-480
Jhala, R., Bharpoda, T. and Patel, M. (2008). Phenacoccus solenopsis Tinsley (Hemiptera: Pseudococcidae), the mealy bug species recorded first time on cotton and its alternate host plants in Gujarat, India. Uttar Pradesh Journal of Zoology, 28(3): 403-406
Jiao, H., Zha, Y., Gao, J., Li, Y., Wei, Y. and Huang, J. (2006). Estimation of chlorophyll?a concentration in Lake Tai, China using in situ hyper spectral data. Inter-national Journal of Remote Sensing, 27(19): 4267-4276
Kranthi, K.R., Kranthi, S., Kumar, R., Nagrare, V.S., and Barik, A. (2009a). Advances in Cotton IPM. Technical Bulletin, Central Research Institute for Cotton Re-search, Nagpur, India. pp. 26
Mirik, M., Michels, G., Kassymzhanova-Mirik, S., Elliott, N., Catana, V. and Jones, D. (2006a). Using digital image analysis and spectral reflectance data to quantify damage by greenbug (Hemitera: Aphididae) in winter wheat. Computers and Electronics in Agriculture, 51(1): 86-98
Mirik, M., Michels, G.J., Kassymzhanova-Mirik, S., Elliott, N.C. and Bowling, R. (2006b). Hyperspectral spectrom-etry as a means to differentiate uninfested and infested winter wheat by greenbug (Hemiptera: Aphididae). Journal of Economic Entomology, 99(5): 1682-1690
Monga, D., Kumar, R., Pal, V. and Jat, M. (2009). Mealy-bug, a new pest of cotton crop in Haryana-a survey. Journal of Insect Science, 22: 100-103
Murugesan, N. and Kavitha, A. (2010). Host plant resistance in cotton accessions to the leaf hopper Amrasca devas-tans (Distant). Journal of Biopesticides, 3(3): 526-533
Peñuelas, J. and Filella, I. (1998). Visible and near-infrared reflectance techniques for diagnosing plant physiologi-cal status. Trends in Plant Science, 3(4): 151-156
Prabhakar, M., Prasad, Y., Thirupathi, M., Sreedevi, G., Dharajothi, B. and Venkateswarlu, B. (2011). Use of ground based hyperspectral remote sensing for detection of stress in cotton caused by leafhopper (Hemiptera: Cicadellidae). Computers and Electronics in Sgricul-ture, 79(2): 189-198
Prabhakar, M., Prasad, Y.G., Desai, S., Thirupathi, M., Gopi-ka, K. and Rao, G.R. (2013a). Hyperspectral remote sensing of yellow mosaic severity and associated pig-ment losses in Vigna mungo using multinomial logistic regression models. Crop Protection, 45: 132-140
Prabhakar, M., Prasad, Y.G., Vennila, S., Thirupathi, M., Sreedevi, G. and Rao, G.R. (2013b). Hyperspectral indices for assessing damage by the solenopsis mealy-bug (Hemiptera: Pseudococcidae) in cotton. Computers and Electronics in Agriculture, 97: 61-70
Reisig, D. and Godfrey, L. (2006). Remote sensing for detec-tion of cotton aphid–(homoptera: aphididae) and spider mite–(acari: tetranychidae) infested cotton in the San Joaquin Valley. Environmental Entomology, 35(6): 1635-1646
Richardson, A., Aikens, M., Berlyn, G. and Marshall, P. (2004). Drought stress and paper birch (Betula pa-pyrifera) seedlings: effects of an organic biostimulant on plant health and stress tolerance, and detection of stress effects with instrument-based, noninvasive meth-ods. Journal of Arboriculture, 30: 52–61
Sandholt, I., Rasmussen, K. and Andersen, J. (2002). A sim-ple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of Environment, 79(2): 213-224
Schott, J.R. and Volchok, W.J. (1985). Thematic Mapper thermal infrared calibration. Photogrammetric Engi-neering and Remote Sensing, 51: 1351-1357
Sims, D.A. and Gamon, J.A. (2002). Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment, 81(2): 337-354
Singh, S., Dutta, S. and Dharaiya, N. (2013a). Efficiency of remote sensing indices in crop biotic stress assessment. International Journal for Life Sciences and Educational Research, 1(3): 100-104
Singh, S., Dutta, S. and Dharaiya, N. (2013b). Evaluation of probable hot spots of mealybug concentration in cotton growing areas of Sirsa district using satellite data. Inter-national Journal for Life Sciences and Educational Research, 1(2): 115-119
Singh, S., Dutta, S. and Dharaiya, N. (2016). Mapping of cotton mealybug (Hemiptera: Pseudococcidae) damage in Sirsa district, Haryana using Geospatial technique. International journal of Engineering Sciences & Research Technology, 5(2): 138-146
Tanwar, R.K., Jeyakumar, P., and Monga, D. (2007). Mealy bugs and their management. New Delhi.
Yang, C.M., Cheng, C.H. and Chen, R.K. (2007). Changes in spectral characteristics of rice canopy infested with brown plant hopper and leaf folder. Crop Science, 47(1): 329-335
Yang, Z., Rao, M., Elliott, N., Kindler, S. and Popham, T. (2005). Using ground-based multispectral radiometry to detect stress in wheat caused by green bug (Homoptera: Aphididae) infestation. Computers and Electronics in Agriculture, 47(2): 121-135
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How to Cite
Singh, S. K., Dutta, S., & Dharaiya, N. (2016). A study on geospatial technology for detecting and mapping of Solenopsis mealybug (Hemiptera: Pseudococcidae) in cotton crop. Journal of Applied and Natural Science, 8(4), 2175–2181. https://doi.org/10.31018/jans.v8i4.1108
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Research Articles