MAPPING OF COTTON MEALYBUG (HEMIPTERA: PSEUDOCOCCIDAE) DAMAGE IN SIRSA DISTRICT, HARYANA USING GEOSPATIAL TECHNIQUEJournal: International Journal of Engineering Sciences & Research Technology (IJESRT) (Vol.5, No. 3)
Publication Date: 2016-03-30
Authors : S. K. Singh;
Page : 138-146
Keywords : Mealybug; Severity Index and;
Detection of crop stress is one of the major applications of remote sensing in agriculture. Many researche r s have confirmed the ability of remote sensing techniques for detection of pest/disease on cotton. Hence, this research was designed to investigate , (1) to study the spectral properties of noninfested and mealybug infested cotton crop (ii) damage assessment using remote sensing derived index. Mealybug - infested cotton crop showed significantly lower reflectance in the near infrared region and higher in the visible region of the spectrum compared with the non - infested cotton crop. Mealybug Pest Stress Index - 8 (MPSI - 8), a remote sensing index derived in this study shows a significant negative relationship with mealybug severity (r 2 =0.6319) an d shows the potential to assess the pest and disease damage because of its characteristic that include pigment, leaf struc ture , and water sensitive band. MPSI - 8 depicts the change in pigment concentration and water stress and shows a negative relationship with mealybug severity. The h igh negative value of the index shows the high severity of mealybug. Land Surface Temperature (LST) also shows a positive and significant relationship with mealybug severity (r 2 =0.5921). Multiple l inear regression analys i s showed a strong relationships (r 2 =0.752) between mealybug severity and remotely derive index. Model developed in this study for the mealybug damage assessment in cotton crop yielded significant relationship (r 2 =0. 752 ) and was applied on satellite data of 21 st September 2009 which reveals high severity of mealybug and it was low on 24 th September 2 010 which confirms 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 cot ton.
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