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

Anderson P Coelho,João A Fischer Filho,Rogério T De Faria,Luiz F Palaretti,Alexandre B Dalri,João V T Bettiol

doi:10.1590/1807-1929/agriambi.v23n1p9-15

Abstract

ABSTRACT 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.

Highlights

The world area planted with sugarcane is 22 × 106 ha, with cultivation in 120 countries (FAO, 2016)
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 artificial neural network (ANN) were used for calibration and validation of the data

Summary

Introduction

The world area planted with sugarcane is 22 × 106 ha, with cultivation in 120 countries (FAO, 2016). Brazil is the largest global producer of sugarcane with 661 million tons, representing 20.3% of the global production (USDA, 2017). For being a highly competitive activity and since the price paid for the product is a function of its quality, increased profitability is linked to greater sugar yield (Pereira et al, 2014; Bigaton et al, 2015). Among the technological analyses in sugarcane, only °Brix, juice Pol and wet cake weight (WCW) are determined, while the other technological attributes are calculated (CONSECANA, 2006). 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) they either have low accuracy or require specific high-cost equipment

Objectives

Methods

Results