Nitrogen and potassium deficiency identification in maize by image mining, spectral and true colour response

Abstract

Nutrients namely nitrogen (N) and potassium (K) management is an important agronomic practice to attain higher yield. The present study was conducted to determine effective spectra ranges and significant component images of RGB intensities which restrict ineffective data to be processed in identifying N and K deficiency symptoms through image mining, hyperspectral and true color responses. Maize crop was grown under field condition as per the recommended package of practice. At seed development stage, the leaf reflectance and digital images were acquired from N and K deficient leaves along with control (N and K sufficient) leaves. A portable spectroradiometer capable of measuring the wavelength range of 350–1050 nm of the electromagnetic spectrum was used to collect spectral data. The digital image was acquired using 20.1 megapixel camera. The result indicated that N and K deficiency increased the leaf reflectance at two ranges of green (centered 555 nm) and red edge (centered 715 nm). The K deficient leaf showed increased reflectance at near infrared (NIR) region of the spectrum. Differences in spectral reflectance of the leaves were highly correlated to Red, Green and Blue intensity values of N and K deficient leaves. High values for the blue and red portions suggests that chlorophyll and other associated pigments are not as plentiful in the N and K deficient plants, and higher reflectance values in the green correlates with more yellow pigment and decreased plant functions. The results indicated that identification of nutritional deficiency symptoms through image mining techniques could yield improved accuracy using spectra range and significant component images of RGB intensities that resonate with the physiological changes in crop due to the deficiency.