000			02836nab|a22003497a|4500
999			_c63318 _d63310
001			63318
003			MX-TxCIM
005			20211006075158.0
008			210211s2015||||xxk|||p|op||||00||0|eng|d
022			_a0967-0874
022			_a1366-5863 (Online)
024	8		_ahttps://doi.org/10.1080/09670874.2015.1072652
040			_aMX-TxCIM
041			_aeng
100	1		_aDas, P.K. _918395
245	1	0	_aMonitoring of bacterial leaf blight in rice using ground-based hyperspectral and LISS IV satellite data in Kurnool, Andhra Pradesh, India
260			_aLondon (United Kingdom) : _bTaylor & Francis, _c2015.
500			_aPeer review
520			_aBacterial leaf blight (BLB) is one of the most common diseases of rice in India, which may lead to partial or total crop loss based on the time of infestation. Hence, on-time identification of disease and its distribution over the affected region could provide useful information for minimizing the crop loss. In the present study, a comprehensive approach has been adopted to monitor the BLB-affected rice crop using hyperspectral data at filed scale and to upscale the observations at village-level using multispectral satellite data. The spectro-radiometer data at 350–2500 nm wavelength range was collected, along with other crop parameters, viz. chlorophyll and moisture content. The step-wise discriminate analysis (SDA) revealed that only four wavebands, i.e. 760, 990, 680 and 540 nm, could significantly discriminate diseased crop from healthy one. The selected wavebands were used to compute 12 narrowband vegetation indices, whereas according to SDA plant senescence index (PSRI), pigment-specific simple ratio (PSSRb) and red-edge position were only found to be effective. PSRI and PSSRb could be successfully re-computed using resolution simulation of hyperspectral data corresponding to linear imaging self-scanner (LISS) IV sensor. The equations were deployed on LISS IV satellite data to generate geospatial maps of PSRI and PPSRb. The geospatial maps could differentiate different degrees of stressed crop very effectively. Hence, the proposed approach can be adopted for in-season monitoring and assessment of diseased crops at regional level for better agricultural planning and management.
546			_aText in English
650		7	_aXanthomonas oryzae _2AGROVOC _913216
650		7	_aRice _gAGROVOC _2 _91243
650		7	_aRemote sensing _2AGROVOC _91986
650		7	_aImagery _2AGROVOC _910231
651		7	_2AGROVOC _93726 _aIndia
700	1		_aLaxman, B. _918396
700	1		_aKameswara Rao, S.V.C. _918397
700	1		_aSeshasai, M.V.R. _918398
700	1		_aDadhwal, V.K. _918399
773	0		_gv. 61, no. 4, p. 359-368 _dLondon (United Kingdom) : Taylor & Francis, 2015. _x0967-0874 _tInternational Journal of Pest Management _wu446136
942			_cJA _n0 _2ddc