Soil attributes and leaf nitrogen estimating sugar cane quality parameters: Brix, pol and fibre

Rodrigues, F. A.; Magalhães, Paulo Sérgio Graziano; Franco, Henrique Coutinho Junqueira

doi:10.1007/s11119-012-9294-1

Cited by 16 publications

(8 citation statements)

References 24 publications

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“…The maps of the spatial standard errors are visually similar, with most maps being characterized by lower values, except for the fiber content maps. A study of the spatial structure variation could have better guided the sampling conducted to evaluate quality mainly to improve fiber content characterization similar to Rodrigues Júnior et al (2013) observations. Therefore, it is recommended future works with more study years including a temporal variability evaluation and a refined grid sampling for the technological attributes.…”

Section: Resultsmentioning

confidence: 98%

“…The spatial variability of crops qualitative attributes, although of the same variety, may be due to several environmental factors such climate (Mahmood & Murdoch, 2017), variability in spatial edaphic attributes (Rodrigues Júnior et al, 2013;Baluja et al, 2013), and the plants' phenological characteristics. The effect of these associated factors, reflected under the quality and yield crop, justify the importance to the understanding the variability among crop attributes that have higher values.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, to evaluate the spatial qualitative attributes of sugarcane, it is worth considering the distributions of all the qualitative attributes, except the fiber content. Based on the spatial variability of the qualitative attributes of sugarcane, the most efficient method for sampling, and the heterogeneity in plants of the same variety within the field, it is important to develop models capable of describing these attributes' behaviors in the field to better understand the cause and effect relation between soil and plant (Rodrigues Júnior et al, 2013), which would support decision making in site specific management.…”

Section: Resultsmentioning

confidence: 99%

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Spatial Variability Mapping of Sugarcane Qualitative Attributes

Ferraz¹,

Corrêdo

Wei

et al. 2019

Eng. Agríc.

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Spatial variability evaluation of qualitative attributes can be used as an excellent strategy to design forms of intervention that result in better crop profitability for some agricultural crops, for example, sugarcane. Based on the assumption that qualitative attributes of sugarcane present spatial variability and their distributions along the stems are uniform in different varieties, this study aimed to evaluate the distributions of the qualitative parameters of different sugarcane varieties and the spatial variability of these attributes in a commercial field. Samples of nine varieties were collected for laboratory quality analysis, and the Brix parameter was analyzed by a digital refractometer. The analysis of variance, the Tukey test, and geoestatistcs were the statistical analyses applied to the dataset. The maps were generated using 91 sample results from the laboratory analysis of the 16.6 ha field. It was found that, in the harvest period, there was no significant difference in Brix content along the sugarcane stems. Therefore, we can conclude that the Brix content along the sugarcane stems does not change in the harvesting period, and the ideal sampling size to better represent the factors affecting sugarcane qualitative attributes is six points per hectare.

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Section: Resultsmentioning

confidence: 98%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Spatial Variability Mapping of Sugarcane Qualitative Attributes

Ferraz¹,

Corrêdo

Wei

et al. 2019

Eng. Agríc.

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“…Juice Pol is the most laborious for determination because it requires clarifiers and specific equipment for reading. Alternative methodologies have already been tested to determine Pol (Valderrama et al, 2007;Rodrigues Júnior et al, 2013) however, they either have low accuracy or require specific high-cost equipment.…”

Section: Introductionmentioning

confidence: 99%

Application of artificial neural networks in the prediction of sugarcane juice Pol

Coelho

Bettiol

Dalri

et al. 2019

Rev. bras. eng. agríc. ambient.

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Innovative techniques that seek to minimize the costs of production and the laboriousness of certain operations are one of the great challenges in the sugar-energy sector nowadays. Thus, the objective of the present study was to estimate the Pol values of sugarcane juice as a function of °Brix and wet cake weight (WCW) using artificial neural network (ANN) modeling. A database was organized consisting of 204 technological analyses from a field experiment with 15 treatments and 2 years of evaluation. 75% of the data were used for the calibration of the model and 25% for its validation. Multilayer Perceptron ANNs were used for calibration and validation of the data. Before calibration, the variables were normalized. The training algorithm used was backpropagation and the activation function was the sigmoid. The ANNs were established with two hidden layers and the number of neurons ranging from 4 to 20 in each. The 15 ANNs with the lowest root mean square errors were randomly presented by the software, among which 6 were chosen to verify the accuracy. The ANNs had a high accuracy in the estimation of sugarcane juice Pol, both in the calibration phase (R2 = 0.948, RMSE = 0.36%) and in the validation (R2 = 0.878, RMSE = 0.41%), and can replace the standard method of analysis. Simpler networks can be trained to have the same accuracy as more complex networks.

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“…In this context, efforts have been primarily dedicated to experiments aiming at establishing the scientific grounds and demonstrating the advantages of PA techniques applied to sugarcane [9][10][11][12][13][14]. Because of these goals, characterization of soil and plant attributes in experiments is much more comprehensive than the expected for large-scale PA practice.…”

Section: Introductionmentioning

confidence: 99%

Data Analysis Workflow for Experiments in Sugarcane Precision Agriculture

Driemeier

Liu

Pontes

et al. 2014

2014 IEEE 10th International Conference on E-Science

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Precision Agriculture (PA) comprises a set of tools to understand and manage inherent spatial variability within crop fields. PA relies on a variety of techniques to collect, analyze, process, and synthesize voluminous georeferenced data. However, prior to large-scale practice, PA requires a successful experimentation stage, which is the present stage of PA for the sugarcane system. This paper presents a data analysis workflow for PA experiments, including workflow application to a case study in a sugarcane area where an appreciable diversity of soil and plant attributes has been measured. Our data analysis workflow has basis on: i) removal of outliers; ii) representation of different data acquisition techniques on a common spatial grid; iii) estimation of typical "noise" level in each measured attribute; iv) spatial autocorrelation analysis for each attribute; v) correlation analysis to identify related attributes; and vi) principal component analysis to reduce the dimensionality of the attribute space. By treating the diversity of measured attributes on a common ground, the proposed analysis workflow guides further experimentation as well as selection of data acquisition technologies suitable for large-scale sugarcane PA.

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Soil attributes and leaf nitrogen estimating sugar cane quality parameters: Brix, pol and fibre

Cited by 16 publications

References 24 publications

Spatial Variability Mapping of Sugarcane Qualitative Attributes

Spatial Variability Mapping of Sugarcane Qualitative Attributes

Application of artificial neural networks in the prediction of sugarcane juice Pol

Data Analysis Workflow for Experiments in Sugarcane Precision Agriculture

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