Sugarcane Stalk Content Prediction in the Presence of a Solid Impurity Using an Artificial Intelligence Method Focused on Sugar Manufacturing
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For the first time in literature, an analytical method was developed using artificial neural networks (ANNs) combined with color information from digital images to predict the content of sugarcane stalks in the presence of a solid impurity. The data were generated using a laboratory-made simple imaging system and free-access computational routine for the conversion of the images into 10 colors. The ANN model was implemented using 10 neurons in the input layer, 8 neurons in the hidden layer and 1 neuron in the output layer related to the content of sugarcane stalks. The ANN model provided relative errors of 3% and achieved correlation coefficients of 0.98, 0.93, and 0.91 for the training, validation and test sets, respectively. A partial least squares (PLS) model showed the nonlinear nature of the data that implies the application of ANN model. The developed method has the potential to be applied in sugarcane mills as an improvement for the production of high-quality sugar.
KeywordsSugar Food quality Digital images Artificial neural networks
The authors are grateful to the São Paulo Research Foundation (FAPESP) for grant Nos. 2018/18212-8, 2019/01102-8, and 2018/03690-1, as well as the Coordination for the Improvement of Higher Education Personnel (CAPES)–Finance Code 001 under the W.N.G. grant fellowship.
Compliance with Ethical Standards
This is an original research article that has neither been published previously nor considered presently for publication elsewhere. All authors named in the manuscript are entitled to the authorship and have approved the final version of the submitted manuscript.
Conflict of Interest
Wesley Nascimento Guedes declares that he has no conflict of interest. Lucas Janoni dos Santos declares that he has no conflict of interest. Érica Regina Filletti declares that she has no conflict of interest. Fabíola Manhas Verbi Pereira declares that she has no conflict of interest.
This article does not contain any studies involving human participants or animals performed by any of the authors.
Informed consent was obtained from all individual participants included in the study.
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