NDVI based classification of the IRS P6 LISS IV data of the target village at Warangal district, Andhra Pradesh revealed four distinct classes with NDVI values less than 0.17 denoting severe mealybug damaged cotton fields; 0.17 - 0.26 denoting moderately damaged and above 0.26-0.40 for healthy cotton fields. Buffer analysis with first 15 m from road showed the number of pixels with severe infestation is very high (825) compared to the next 15 m buffer (533) indicating a strong relationship between mealybug severity in cotton fields adjacent to roadsides that harbour many alternate weed hosts. Hyperspectral radiometry of differential pest severity grades yielded several indices such as Triangular Vegetation Index (TVI), Modified Chlorophyll Absorption Reflectance Index (MCARI), Renormalized Difference Vegetation Index (RDVI), Optimised Soil Adjusted Vegetation Index (OSAVI) and Pigment Specific Simple Ratio (PSSR) which were found promising (R2 =0.62 to 0.76) to distinguish the mealybug damage which is an emerging pest on Bt cotton in India.
There was an outbreak of rice leaf folder in 3 districts viz., Kaithal, Karnal and Kurukshethra of Haryana during kharif 2012. The predominant basmati types grown in the area were CSR 30 and Pusa 1121 and the damage level in farmers field was 10-80%.The ground control points (GCPs) of fields damaged were collected using DGPS. The satellite image (IRS P-6, LISS-4 dated 10 October-2012) was subjected to supervised classification using ground truth data (classification accuracy of 62%). A map was prepared depicting the severity and spatial spread of the pest damage.
Developed a methodology for depiction of Brown plant hopper (BPH) pest incidence on rabi rice crop using a series of remote sensing (RS) data sets from MODIS and IRS P6- AWiFs. Using Normalised Difference Water Index (NDWI), Temperature Vegetation Dryness Index (TVDI) developed from satellite data along with the brown plant hopper pest data recorded during ground truth collection as inputs, developed methodology in GIS for spatially depicting BPH intensity in the region with reasonably good accuracy. It is also possible to locate the susceptible areas for BPH incidence in the region using this model. It is useful for agricultural extension functionaries to assess the BPH damage and also to locate areas with high risk of pest damage. This will facilitate in planning pest control operations in advance.