DETECTION OF BACTERIAL WILT DISEASE (PSEUDOMONAS SOLANCEARUM) IN BRINJAL USING HYPERSPECTRAL REMOTE SENSING
- Mahalanobis National Crop Forecast Centre, DAC&FW, New Delhi -110 012, India
Keywords: Hyperspectral, Bacterial wilt disease, Chlorophyll, Leaf Area Index, Spectroradiometer
Abstract. Bacterial wilt disease (pathogen: Pseudomonas solancearum) is a major problem affecting brinjal crop. Infected leaves show yellowing, loss in turgidity, drying and ultimately the entire plant collapses. The study aims to examine the potential of hyperspectral remote sensing for detection of biotic stress caused due to bacterial wilt disease and identify best spectral band widths and hyperspectral indices indicative of disease infestation. This study was conducted in a farmer’s plot at Alampur in Baruipur block, South 24 Pargana district, West Bengal. Canopy spectra (using ASD Fieldspec 2 Spectroradiometer), chlorophyll content (by Chlorophyll meter) and Leaf Area Index (LAI) (by plant canopy imager) were collected. The healthy plants had green and fully turgid leaves whereas diseased plants had lower chlorophyll content and LAI. The reduction in chlorophyll content lowered reflectance in green region and internal leaf damage in near-infrared region. A correlation analysis was carried out between reflectance at specific bandwidths and hyperspectral indices with chlorophyll content and LAI of healthy and stressed plants. Bandwidths of 528–531 nm, 550–570 nm, 710–760 nm, and single bands such as 800 nm and 920 nm and indices viz. Greenness index, Modified Chlorophyll Absorption in Reflectance Index (MCARI), Transformed Chlorophyll Absorption in Reflectance Index (TCARI), Triangular Vegetation Index (TVI), Simple Ratio Pigment Index (SRPI), Photochemical Reflectance Index (PRI 2), Lichtenthaler Indices (LIC1, LIC2), Structure Intensive Pigment Index (SIPI) etc. were found to have strong positive correlation (R2 > 0.9) with plant parameters. These specific bandwidths and indices can be helpful in biophysical parameter estimation and early detection of crop stress, crop growth and disease monitoring.