Determination of Canopys Average SPAD Readings Based on the Analysis of Digital Images

Abstract

Chlorophyll meter SPAD (Soil Plant Analysis Development) is one of the most popular tools used to monitor the N Status of rice (Oryza Sativa L.) but its high price limits it from being widely adopted. Digital imaging analysis used to detect nitrogen offers a promising, inexpensive substitute at canopy level. And the present study evaluated rice canopy SPAD readings by analyzing digital images captured with a domestic digital camera. A field experiment was conducted in 2011 using a completely randomized split-plot design, with main-plot treatments of six N fertilizer application rates and subplot treatments of different plant densities. To remove the image backgrounds, the images were segmented and a dark green color index (DGCI). The index, which is derived from values of hue, saturation, and brightness (HSB), was used to assess the greenness of the rice canopy. The DGCI was significantly correlated with average SPAD readings rice canopy at each growth stage, but the regression lines were different from each other. This might be caused by different lighting conditions, which lead us to conclude a calibration is indispensable when using a digital camera for the diagnosis of a plant's nitrogen status.

Monitoring the Nitrogen Nutrition Status of Rice Plants Using Spectral and Image Technologies

“…Li et al extracted the dark green color index (DGCI) of image features, and concluded that DGCI was significantly correlated with the SPAD value of rice leaves. Thus, DGCI could be used to estimate the chlorophyll value of rice leaves and indirectly evaluate the growth and nutrition status of rice [17]. Jia et al used a digital camera and a Greenseeker hand-held sensor to monitor cotton growth and N status.…”

Monitoring the nitrogen nutrition status of rice plants using spectral and image technologies

“…Li et al extracted the dark green color index (DGCI) of image features, and concluded that DGCI was significantly correlated with the SPAD value of rice leaves. Thus, DGCI could be used to estimate the chlorophyll value of rice leaves and indirectly evaluate the growth and nutrition status of rice [18]. Jia et al used a digital camera and a Greenseeker hand-held sensor to monitor cotton growth and N status [19].…”

Intelligent image analysis for retrieval of leaf chlorophyll content of rice from digital images of smartphone under natural light

“…Compared to conventional destructive spectroscopic methods of Chl estimation (Lichtenthaler and Wellburn 1983), sensor-based, modern, noninvasive approaches have received considerable attention due to their highthroughput and real-time measurement abilities (Cassol et al 2008, Yadav et al 2010, Rigon et al 2012, Stefan et al 2013, Jinwen 2014, Novichonok et al 2016. Different types of Chl meters (Pagola et al 2009, Novichonok et al 2016, multi-spectral (Reyniers et al 2006) and hyper-spectral sensors (Chen et al 2010, Li et al 2014 have been developed to measure absorbance and spectral reflectance of leaves.…”

“…Different types of Chl meters (Pagola et al 2009, Novichonok et al 2016, multi-spectral (Reyniers et al 2006) and hyper-spectral sensors (Chen et al 2010, Li et al 2014 have been developed to measure absorbance and spectral reflectance of leaves. Among Chl meters, the SPAD meter (SPAD-502, Minolta, Japan) has been widely used to measure the Chl content as well as to assess crop nitrogen status nondestructively (Uddling et al 2007, Cabangon et al 2011, Ling et al 2011, Liu et al 2012, Wang et al 2013, 2014Yuan et al 2016, Vesali et al 2017, Agarwal and Dutta Gupta 2018. The instrument measures transmission of light absorbed by Chl in the red band (650 nm), as well as transmission of infrared light (950 nm), where no absorption of Chl occurs.…”