Estimating SPAD, Nitrogen Concentration, and Chlorophyll Content in Rice Leaves using Calibrated Smartphone Digital Image

Abstract

Laboratory analysis is commonly used to determine nitrogen and chlorophyll content. However, smartphones can serve as rapid, mobile, and non-destructive tools for this purpose. An equation can be created to calculate nitrogen and chlorophyll content by analyzing color parameters from digital images of rice leaves. An examination was performed on 86 rice leaf samples from the maximum tillering and mature stages. Rice leaf photos were taken with a smartphone in natural outdoor lighting. Color calibration with Spydercheckr was needed to adjust for lighting conditions. Uncalibrated and calibrated image data were analyzed to determine RGB values converted into CIELAB color space. The L*, a*, and b* values had a significant correlation with SPAD parameters, nitrogen concentration, chlorophyll a, b, and total chlorophyll content. This connection was higher after image calibration. The study found that smartphone images could predict SPAD values with 87.9% to 92.3% precision, depending on color space. Using a smartphone digital picture of L* and a* values, N content could be estimated with 84.7% and 81.9% accuracy. Average accuracy for chlorophyll a, b, and total chlorophyll content was 65% to 76%. This study shows smartphone images can estimate rice leaf SPAD and nitrogen content.