Abstract
This study presents a method for detecting rice crop damage due to bacterial leaf blight (BLB) infestation. Rice crop samples are first analyzed using a handheld spectroradiometer. Then, multi-temporal satellite image analysis is used to determine the most suitable vegetation indices for detecting BLB. The results showed that healthy plants have the highest first derivative value of spectral reflectance of the different categories of diseased plants. Significant difference can be found at approximately 690-770 nm (red edge region) which peak or maximum of the first derivative occurs in healthy crop whereas the highest percentage of BLB showed the lowest in that region. Moreover, visible bands such as blue, green, red, and red edge 1 band show variation of correlation in the early (vegetative) to generative stage then getting high especially in early of harvesting stage than the other bands; the NIR band exhibits a low correlation from the early stage of the growing season whereas the red and red edge bands reveal the highest correlations in the later stage of harvesting. Similarly, the satellite image analysis also reveals that disease incidence gradually increases with increasing age of the plant. The vegetation indices whose formulas consist of blue, green, red, and red edge bands (NGRDI, NPCI, and PSRI) exhibit the highest correlation with BLB infestation. NPCI and PSRI indices indicate that crop stress due to BLB is detected from ripening stage of NPCI then the senescence condition is then detected 12 days later. The coefficients of determination between these indices and BLB are 0.44, 0.63, and 0.67, respectively
Highlights
Rice is one of the most important crops for the global population
Significant difference can be found at approximately 690-770 nm which peak or maximum of the first derivative occurs in healthy crop whereas the highest percentage of bacterial leaf blight (BLB) showed the lowest in that region
It is common knowledge that the function of the red and NIR bands are to analyze vegetation conditions; this study reveals that BLB infestation is more closely related to the red and red edge bands
Summary
Rice is one of the most important crops for the global population. sustainable rice farming is extremely reliant on effective pest and disease management (Zhang et al, 2002). Remote sensing can be used to detect, monitor, and assess crop diseases at different spatio-temporal scales (Franke & Menz, 2007). Many previous studies have applied remote sensing to crop monitoring and disease assessment. The leaf area index (LAI) is the most common measure for monitoring and detecting crop diseases in a range of crops; for example, rice (Xiao et al, 2002; Qin & Zhang, 2005; Ghobadifar et al, 2016), tomato (Zhang et al, 2002), wheat (Huang & Apan, 2006), and sugar beet (Mahlein et al, 2013)
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