Effects of storage on vis-NIR-SWIR reflectance spectra of Mombasa grass leaf samples

Tavares, Tiago Rodrigues; Fiorio, Peterson Ricardo; Seixas, Hugo Tameirão; Garcia, Amparo C.; Barros, Pedro Paulo

doi:10.1590/0103-8478cr20190587

Cited by 3 publications

(2 citation statements)

References 30 publications

(32 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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.…”

Section: Introductionmentioning

confidence: 99%

Estimation of leaf nitrogen levels in sugarcane using hyperspectral models

et al. 2022

Self Cite

View full text Add to dashboard Cite

Sugarcane is a good source of renewable energy and helps reduce the emission of greenhouse gases. Nitrogen has a critical role in plant growth; therefore,estimating nitrogen levels is essential, and remote sensing can improve fertilizer management. This field study selects wavelengths from hyperspectral data on a sugarcane canopy to generate models for estimating leaf nitrogen concentrations. The study was carried out in the municipalities of Piracicaba, Jaú, and Santa Maria da Serra, state of São Paulo, in the 2013/2014 growing season. The experiments were carried out using a completely randomized block design with split plots (three sugarcane varieties per plot [variety SP 81-3250 was common to all plots] and four nitrogen concentrations [0, 50, 100, and 150 kgha-1] per subplot) and four repetitions. The wavelengths that best correlated with leaf nitrogen were selected usingsparse partial least square regression. The wavelength regionswere combinedby stepwise multiple linear regression. Spectral bands in the visible (700-705 nm), red-edge (710-720 nm), near-infrared (725, 925, 955, and 980 nm), and short-wave infrared (1355, 1420, 1595, 1600, 1605, and 1610 nm) regions were identified. The R² and RMSE of the model were 0.50 and 1.67 g.kg-1, respectively. The adjusted R² and RMSE of the models for Piracicaba, Jaú, and Santa Maria were 0.31 (unreliable) and 1.30 g.kg-1, 0.53 and 1.96 g.kg-1, and 0.54 and 1.46 g.kg-1, respectively. Our results showed that canopy hyperspectral reflectance can estimate leaf nitrogen concentrations and manage nitrogen application in sugarcane.

show abstract

Section: Introductionmentioning

confidence: 99%

Estimation of leaf nitrogen levels in sugarcane using hyperspectral models

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…After removal, the leaves were stored in plastic bags and transported in thermal boxes with ice to the geoprocessing laboratory for the spectral readings, without direct contact between the leaves and the ice. This technique was adopted to preserve the turgidity and the spectral properties of the leaves [ 25 , 26 ].…”

Section: Methodsmentioning

confidence: 99%

Prediction of leaf nitrogen in sugarcane (Saccharum spp.) by Vis-NIR-SWIR spectroradiometry

Fiorio,

Silva,

Rizzo

et al. 2024

Heliyon

Self Cite

View full text Add to dashboard Cite

Predictive modelling of chlorophyll in Mombaça grass leaves by hyperspectral reflectance data and machine learning

Sánchez,

Silva,

Demattê

et al. 2024

Grass and Forage Science

View full text Add to dashboard Cite

Chlorophyll (Chl) concentration is one of the factors that affects crop productivity. This study investigated the prediction of chlorophyll concentrations in Mombaça grass' leaves using hyperspectral data and machine learning techniques. Chlorophyll variations were induced by different levels of nitrogen fertilization (104, 208, 312, and 416 kg ha−1). Spectral signatures (400–2500 nm) and chlorophyll contents of the leaves were obtained in October, November, and December 2017, and January 2018. Models were generated using Partial Least Square Regression (PLSR), Random Forest (RF), and Support Vector Regression (SVR). Two validation techniques were employed: holdout, dividing the data into training (75%) and testing (25%) sets; and leave‐one‐date‐out cross‐validation (LOOCV), in which one date was omitted during model training and used to predict the omitted date's value. Chlorophyll concentrations varied according to N doses, with the highest concentrations observed in October and December. In these months, there were greater variations in spectral reflectance in the green and red bands (530–680 nm). December was identified as the ideal period for chlorophyll quantification, for both holdout and LOOCV validation techniques. The SVR technique performed best (R2 = 0.71, RMSE = 0.23 mg g−1, dr = 0.72) compared to RF (R2 = 0.63, RMSE = 0.27 mg g−1, dr = 0.66) and PLSR (R2 = 0.60, RMSE = 0.27 mg g−1, dr = 0.67). Therefore, the prediction of chlorophyll in Mombaça grass using spectroradiometry is promising and applicable across different cultivation periods.

show abstract

Effects of storage on vis-NIR-SWIR reflectance spectra of Mombasa grass leaf samples

Cited by 3 publications

References 30 publications

Estimation of leaf nitrogen levels in sugarcane using hyperspectral models

Estimation of leaf nitrogen levels in sugarcane using hyperspectral models

Prediction of leaf nitrogen in sugarcane (Saccharum spp.) by Vis-NIR-SWIR spectroradiometry

Predictive modelling of chlorophyll in Mombaça grass leaves by hyperspectral reflectance data and machine learning

Contact Info

Product

Resources

About