Predictive Models of Chlorophyll Content in Sugarcane Seedlings Using Spectral Images

Abstract

Chlorophyll content is a widely used parameter for nutritional status diagnosis in sugarcane. This study aimed to develop a predictive model of chlorophyll content in sugarcane seedlings using spectral imagery analysis within the electromagnetic spectrum visible range. The experiment was carried out in a split-plot design, with two fertilization rates and three sugarcane cultivars. For chlorophyll analysis, 144 leaves were collected from seedlings. Chlorophyll contents were extracted and measured by SPAD-502 meter. Spectral images within the range of 480 to 710 nm were analyzed using reflectance, absorbance (white source), and fluorescence (source at 405 and 470 nm) responses. Predictive models were developed using multivariate regression methods such as Principal Component Regression and Partial Least Squares Regression. We chose the best model through absorbance response using variable selection and the PLSR method (R2P = 0.718 and RMSEP = 7.665). The wavelengths of 480, 490, 500, 600, 630, and 640 nm were identified as the best for total chlorophyll content determination. The spectral image processing-based method can provide a chlorophyll measurement equivalent to SPAD, with the advantage of having a higher spatial coverage over the entire leaf area. Besides, it can also support automation of the chlorophyll measurement in greenhouses.

Simultaneous retrieval of sugarcane variables from Sentinel-2 data using Bayesian regularized neural network

“…Chlorophyll contributes to verifying vegetation health, and physiological and nutritional status ( Delegido et al, 2010 ). Like in all plants, chlorophyll in sugarcane is correlated to vegetation stress, photosynthetic capacity, and productivity ( Oliveros et al, 2021 ). Nitrogen is a macro-nutrient that once absorbed by plants becomes a biochemical variable participating in constitution of organics such as protein, chlorophyll, and nucleic acid ( Féret et al, 2021 ).…”

“…Leaf sheath moisture (LSM) affects sugarcane growth and reflects the balance between the plant nutrients ( Keshavaiah et al, 2013 ). Several studies were conducted to investigate or retrieve sugarcane LAI ( Abebe et al, 2022 ; Lin et al, 2009 ; Yang et al, 2017 ), chlorophyll content ( Oliveros et al, 2021 ), nitrogen ( Abdel-Rahman et al, 2010 ; Abdel-Rahman et al, 2013 ; Miphokasap et al, 2012 ; Miphokasap and Wannasiri, 2018 ; Shendryk et al, 2020 ), LSM ( Keshavaiah et al, 2013 ), and salinity stress ( Hamzeh et al, 2013 ; Hamzeh et al, 2016 ). Som-Ard et al (2021) reviewed remote sensing applications in sugarcane cultivation.…”

Determinação de amido e carboidrato em folhas de mangueira com o uso espectroscopia Vis-NIR.

“…Uma tecnologia que veem ganhando notória divulgação é o uso de Espectroscopia Vis-NIR, uma técnica não destrutiva que pode ser utilizada para descrever inúmeros atributos de uma planta como por exemplo: Determinação de carboidrato solúvel, amido e sacarose (Souza, 2022;Frey et al, 2020;Nkajima et al, 2019;Das et al, 2018;De Bei et al, 2017), pigmentos fotossintéticos e teor de clorofila (Oliveira et al, 2021;Yendrek et al, 2017), teor de matéria orgânica no solo (González-Aguiar et al, 2020) e também analise qualidade de diversos produtos, como por exemplo, analise de vinho (Barreto, 2020;Genisheva et al, 2018) maturação e qualidade de frutos (Souza et al, 2022).…”