Estimating chlorophyll content at leaf scale in viroid-inoculated oil palm seedlings (Elaeis guineensis Jacq.) using reflectance spectra (400-1050 nm)

• Main Authors: Golhani, Kamlesh, Balasundram, Siva Kumar, Vadamalai, Ganesan, Pradhan, Biswajeet
• Format: Article
• Published: Taylor & Francis 2019
• Online Access: http://psasir.upm.edu.my/id/eprint/79726/; https://www.tandfonline.com/doi/abs/10.1080/01431161.2019.1584930

Orange Spotting (OS) is an emerging disease in oil palm (Elaeis guineensis Jacq) that has been associated with Coconut cadang-cadang viroid (CCCVd; Cocadviroid, Pospiviroidae). This study was aimed at selecting efficient wavebands in the Visible/near infrared (VNIR) spectrum (400–1050 nm) for estimation of chlorophyll content in CCCVd-inoculated oil palm seedlings. The VNIR spectrum is most sensitive to chlorophyll stress and able to differentiate between healthy and diseased plants. Therefore, a greenhouse experiment was conducted to remotely sense the chlorophyll content using a hyperspectral hand-held spectroradiometer (Analytical Spectral Device (ASD) FieldSpec–II) and a portable chlorophyll meter (Minolta Soil-Plant Analysis Development (SPAD)−502). FieldSpec–II provides reflectance spectra with narrow and contiguous wavebands while SPAD-502 churns out SPAD readings corresponding to chlorophyll content. Interval Partial Least Squares (iPLS) model was used to assess the relationship between independent variable (spectra) and dependent variable (SPAD readings). Five different spectral datasets (i.e. raw spectra, first-order derivatives, Savitzky-Golay smoothing, Multiplicative Scatter Correction (MSC), and standard normal transformation) were investigated. Results showed that the MSC dataset yielded the most accurate estimation with a root mean square error of prediction of 3.70% and a correlation coefficient for prediction of 0.72. A total of thirty wavebands within a range between 601 nm and 630 nm was selected. This work showed that VNIR spectra has the potential to diagnose OS disease in oil palm using the iPLS regression method. This is completely a new application of hyperspectral remote sensing for the oil palm industry, particularly in the domain of plant protection.