Potential use of hyperspectral data to monitor sugarcane nitrogen status

Abstract

Nitrogen management in crops is a key activity for agricultural production. Methods that can determine the levels of this element in plants in a quick and non-invasive way are extremely important for improving production systems. Within several fronts of study on this subject, proximal and remote sensing methods are promising techniques. In this regard, this research sought to demonstrate the relationships between variations in leaf nitrogen content (LNC) and sugarcane spectral behaviour. The work was carried out in three experimental areas in São Paulo State, Brazil, with different soils, varieties and nitrogen rates during the 2012/13 and 2013/14 seasons. A significant correlation was observed between the LNC and variations in the sugarcane spectra. The green and red-edge spectral bands were the most consistent and stable predictors of LNC among the evaluated harvests. Stepwise multiple linear regression analysis (MSLR) generated better models for LNC estimation when calibrated with experimental area, independent of the variety. The present research demonstrates that specific wavelengths are associated with the variation in LNC in sugarcane, and these are reported in the green region (near 550 nm) and in the red-edge wavelengths (680 to 720 nm). These results may help in future research on the direct in situ application of nitrogen fertilizers.

Predictive Models of Chlorophyll Content in Sugarcane Seedlings Using Spectral Images

“…The model obtained in our study performed better than those from other studies wherein hyperspectral imaging and multivariate analysis were used (Wu et al, 2016) or even with the use of more sophisticated multivariate regression methods and a broader electromagnetic spectrum region (Martins et al, 2021). Croft et al (2015) assessed sugarcane chlorophyll content using hyperspectral images with a high determination coefficient (0.97) but did not assess the infrared region, which provided less spectral information than ours.…”

Sugarcane Nitrogen and Irrigation Level Prediction Based on UAV-Captured Multispectral Images at the Elongation Stage

“…Similar studies have also been conducted in recent years for sugarcane [19]. Some researchers used satellite imagery [20,21]21 and UAV hyperspectral imagery[22,23] to predict sugarcane biomass and achieved varying degrees of success. However, no reports have been published on nitrogen and irrigation level prediction based on UAV imagery for sugarcane.…”

Estimation of leaf nitrogen levels in sugarcane using hyperspectral models

“…MIPHOKASAP & WANNASIRI (2018) evaluated three methods of generating a hyperspectral model using the Hyperion orbital sensor to determine the nitrogen levels in four sugarcane varieties and observed that the models with the best fit required two to four wavelengths. MARTINS et al (2021) reported that five to six wavelengths were required to generate area-specific models and ten wavelengths to develop a general model.…”

“…Non-destructive methods are fast and cost-effective; nonetheless, their complexity varies because obtaining spectral data with passive sensors without considering important parameters (solar azimuth, solar elevation angle, and plant biophysical parameters) limits data analysis; thus,the methods requirethe accurate calibration of sensors (MARTINS et al, 2021;MOKHELE& AHMED, 2010;ZHAO et al, 2012;MAHAJAN et al, 2014;LISBOA et al, 2018;MORIYA et al, 2018).…”

“…This issue can be resolved by multivariate techniques that identify regions of the electromagnetic spectrum associated with the attributes of interest, thus avoiding overestimations (ABDEL-RAHMAN et al, 2014;FIORIO et al, 2018;TAVARES et al, 2020, MARTINS et al, 2021.…”