Detecting rice blast disease using model inverted biochemical variables from close-range reflectance imagery of fresh leaves

Rice blast is one of the most devastating crop diseases around the world. Although previous remote sensing studies have examined the spectral variation at leaf and canopy levels in response to disease severity levels, the nonimaging nature of their data makes it difficult to examine the spectral variation related to the disease within a leaf. This study proposes to monitor the spatial and temporal pattern of rice leaf blast on individual leaves with close-range imaging spectroscopy data. Hyperspectral images were acquired from diseased leaves at different infection stages. The image data were converted to reflectance cubes and then processed with a model inversion algorithm PROCWT to retrieve leaf biochemical variables. The biochemical maps were examined to investigate the within-leaf spatial variation and leaf-level temporal variation. Preliminary results demonstrated that the PROCWT algorithm could perform on reflectance image cubes. The retrieved chlorophyll maps exhibited a decline with infection stage and significant within-leaf spatial patterns in response to the disease.