Prediction of mineral contents in sugarcane cultivated under saline conditions based on stalk scanning by Vis/NIR spectral reflectance

Neto, Antônio José Steidle; Toledo, João Vitor; Zolnier, Sérgio; Lopes, Daniela de Carvalho; Pires, Christiano V.; Silva, Thieres George Freire da

doi:10.1016/j.biosystemseng.2017.01.003

Cited by 24 publications

(7 citation statements)

References 37 publications

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“…VIP values greater than 1.0 indicate variables with greater influence on the models, and VIP values between 0.8 and 1.0 indicate the moderately influential variables. All variables with VIP smaller than 0.8 are insignificant to the predictions [ 50 ]. Some substantial similarities could be observed for different samples and quality parameters on a first overview.…”

Section: Resultsmentioning

confidence: 99%

Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy

Corrêdo

Maldaner

Bazame

et al. 2021

Sensors

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Proximal sensing for assessing sugarcane quality information during harvest can be affected by various factors, including the type of sample preparation. The objective of this study was to determine the best sugarcane sample type and analyze the spectral response for the prediction of quality parameters of sugarcane from visible and near-infrared (vis-NIR) spectroscopy. The sampling and spectral data acquisition were performed during the analysis of samples by conventional methods in a sugar mill laboratory. Samples of billets were collected and four modes of scanning and sample preparation were evaluated: outer-surface (‘skin’) (SS), cross-sectional scanning (CSS), defibrated cane (DF), and raw juice (RJ) to analyze the parameters soluble solids content (Brix), saccharose (Pol), fibre, pol of cane and total recoverable sugars (TRS). Predictive models based on Partial Least Square Regression (PLSR) were built with the vis-NIR spectral measurements. There was no significant difference (p-value > 0.05) between the accuracy SS and CSS samples compared to DF and RJ samples for all prediction models. However, DF samples presented the best predictive performance values for the main sugarcane quality parameters, and required only minimal sample preparation. The results contribute to advancing the development of on-board quality monitoring in sugarcane, indicating better sampling strategies.

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Section: Resultsmentioning

confidence: 99%

Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy

Corrêdo

Maldaner

Bazame

et al. 2021

Sensors

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“…The absorption peak near 680 nm was caused by the absorption of chlorophyll on the surface, 13 which determined the color of the plant. The absorption peak near 970 nm 14 was caused by the absorption of water. The average spectra of sugarcane roots and dry sugarcane leaves had shapes similar to that of soil and stone.…”

Section: Resultsmentioning

confidence: 99%

Development of sugarcane and trash identification system in sugar production using hyperspectral imaging

Aparatana

Saengprachatanarug

Izumikawa

et al. 2020

Journal of Near Infrared Spectroscopy

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Classification and differentiation of clean sugarcane from trash (green sugarcane leaf, dry sugarcane leaf, stone, and soil) are important for the sugar payment system at a sugar mill. Currently, the methods used to do this are manual and subjective. Therefore, this study is aimed at accurately differentiating clean sugarcane from trash by using hyperspectral imaging with multivariate analyses. Samples containing sugarcane billets and trash mixed in a ratio of 18:38 were analyzed in this study. The reflectance data of the samples were analyzed in the wavelength range of 400–1000 nm via principal component analysis (PCA). The PCA model was capable of identifying all of the clean sugarcane and trash samples. The spectral loadings of the PCA model show that the sugarcane and trash samples are easily identifiable based on the color (visible light) of each class, water absorption (approximately 970 nm), and chlorophyll absorption (approximately 680 nm). Based on the characteristic wavelengths of the PCA loading peaks, over 90% of the sugarcane and trash samples were differentiated using a multiple linear regression model. Sugarcane and trash are classified by using partial least-squares discriminant analysis and support vector machine models. For all wavelengths, the classification rate is 92.9% and 98.2%, respectively. This shows that sugarcane and trash can be accurately classified and differentiated by using hyperspectral imaging and multivariate analyses.

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“…(5) ):

where VIP k ( a ) is the importance of wavelength or the VI in the k th variable based on a model with a factors (PLS components), W 2 ak is the weight of the wavelength or VI variable in the a th factor of the PLS, SSY a represents the sum of squares explained by Y by the PLS model with a factors, and SSY t is the total sum of squares of Y explained by all a factors of the PLS model ( Li et al., 2016a ). According to ( Steidle Neto et al., 2017 ), wavelengths or VIs with VIP values greater than 1 are highly influential in the estimation, between 0.8 and 1 moderately influential, and less than 0.8 insignificant in the estimation of Nc.…”

Section: Methodsmentioning

confidence: 99%

Estimating canopy nitrogen concentration of sugarcane crop using in situ spectroscopy

Trujillo

Torres

Galindez-Jamioy

et al. 2021

Heliyon

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Estimating nitrogen (N) concentration in situ is fundamental for managing the fertilization of the sugarcane crop. The purpose of this work was to develop estimation models that explain how N varies over time as a function of three spectral data transformations in two stages (plant cane and first ratoon) under variable rates of N application. A randomized complete-block experimental design was applied, with four levels of N fertilization: 0, 80, 160, and 240 kg N ha −1 . Six sampling events were carried out during the rapid growth stage, where the canopy reflectance spectra with a hyperspectral sensor were measured, and tissue samples for N determination in plant cane and first ratoon were taken, from 60 days after emergence (DAE) and 60 days after harvest (DAH), respectively, until days 210 DAE and 210 DAH. To build the models, partial least squares regression analysis was used and was trained by three transformations of the spectral data: (i) average reflectance spectrum (R), (ii) multiple scatter correction and Savitzky-Golay filter MSC-SG) reflectance spectrum, and (iii) calculated vegetation indices (VIs).

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Prediction of mineral contents in sugarcane cultivated under saline conditions based on stalk scanning by Vis/NIR spectral reflectance

Cited by 24 publications

References 37 publications

Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy

Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy

Development of sugarcane and trash identification system in sugar production using hyperspectral imaging

Estimating canopy nitrogen concentration of sugarcane crop using in situ spectroscopy

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