Characterizing and estimating rice brown spot disease severity using stepwise regression, principal component regression and partial least-square regression

Abstract

Abstract:Detecting plant health conditions plays a key role in farm pest management and crop protection. In this study, measurement of hyperspectral leaf reflectance in rice crop (Oryzasativa L.) was conducted on groups of healthy and infected leaves by the fungus Bipolaris oryzae (Helminthosporium oryzae Breda. de Hann) through the wavelength range from 350 to 2 500 nm. The percentage of leaf surface lesions was estimated and defined as the disease severity. Statistical methods like multiple stepwise regression, principal component analysis and partial least-square regression were utilized to calculate and estimate the disease severity of rice brown spot at the leaf level. Our results revealed that multiple stepwise linear regressions could efficiently estimate disease severity with three wavebands in seven steps. The root mean square errors (RMSEs) for training (n=210) and testing (n=53) dataset were 6.5% and 5.8%, respectively. Principal component analysis showed that the first principal component could explain approximately 80% of the variance of the original hyperspectral reflectance. The regression model with the first two principal components predicted a disease severity with RMSEs of 16.3% and 13.9% for the training and testing dataset, respectively. Partial least-square regression with seven extracted factors could most effectively predict disease severity compared with other statistical methods with RMSEs of 4.1% and 2.0% for the training and testing dataset, respectively. Our research demonstrates that it is feasible to estimate the disease severity of rice brown spot using hyperspectral reflectance data at the leaf level.

A comparative screening of hybrid, modern varieties and local rice cultivar for brown leaf spot disease susceptibility and yield performance

“…Surveys show that brown spot causes a 5% yield loss across all lowland rice production situations in South and Southeast Asia (Savary et al 2000). Yet, the fact that brown spot is the "poor rice farmers' disease which occurs anywhere the crop encounters drought, macro-nutrient deficiency or both (Ou 1985;Zadoks 2002;Liu et al 2007). Modern rice varieties are particularly susceptible to the pathogen, as these use more inputs which is difficult to afford by resource-poor farmers.…”

Discrimination of rice panicles by hyperspectral reflectance data based on principal component analysis and support vector classification

“…Although at the leaf and canopy level there has been some progress on remote sensing studies about insect and disease outbreaks in rice including leaf folder (Shi et al, 2009), panicle blast (Kobayashi et al, 2001), leaf blast (Wu et al, 2002), leaf brown spot (Liu et al, 2007;, and sheath blight (Qin et al, 2003;Qin and Zhang, 2005), hardly any study was found to differentiate stressed panicles of rice crop due to insect and pathogen using remote sensing techniques. Rice brown planthopper (Nilaparvata lugens Stål), which is one of the most destructive insects in rice throughout the world, primarily feeds on the leaf sheaths and stems at the basal portion of the rice plant and sucks the phloem sap.…”

Estimation of rice neck blasts severity using spectral reflectance based on BP-neural network

“…2a). Perhaps, because the fungus spore balls of U. virens enclosing the rice panicle glume appeared in dark green and changed the structural arrangement of rice grains, the raw spectral reflectance in the visible and NIR regions was sharply decreased (Zhuang et al 2002;Liu et al 2007Liu et al , 2009aLiu et al , b, c, 2010. Liu et al (2010) showed that rice panicles with moderate fungal infection exhibited a high reflectance in the visible (400-708 nm) and SWIR (1,135-2,400 nm) spectral regions and a low reflectance in the NIR (709-1,134 nm) spectral region, while the rice panicles with serious fungal infection had a low reflectance at wavelengths from 400 to 1,297 nm and a high reflectance at wavelengths from 1,298 to 2,400 nm.…”

“…Spinelli et al (2006) assessed the near infrared (NIR)-based technique for detecting fire blight disease in the asymptomatic pear plants under greenhouse conditions. Liu et al (2007) also determined the percentage of leaf diseased with rice brown spot disease using leaf reflectance measurements and stepwise multiple linear regression (SMLR), principal component regression (PCR), and partial least squares method (PLS). Purcell et al (2009) investigated the application of NIR spectroscopy for the determination and rating of sugarcane resistance against Australian sugarcane disease, Fiji leaf gall.…”