Sugarcane yield modeling,
analysis and estimates
This research line focuses on developing data mining models that address the complexity of the relationship between the variables that affect the growth of sugarcane, in order to provide the decision maker in the sugar-energy industry with more information and also to extract knowledge from databases already available in the mills.
Projects under development seek to evaluate the efficiency of nitrogen fertilization, to estimate the sugar yield at the time of harvest, to identify potential points of borer proliferation, and to analyze the decline in productivity of the ratoon. Previous projects had an emphasis on yield estimate and on analyzing the factors related to the sugarcane yield.
Published research
02
OLIVEIRA, M.P.G. de ; RODRIGUES, L. H. A. . How good are the models available for estimating sugar content in sugarcane?. European Journal of Agronomy, v. 113, p. 125992, 2020.
02
FERRACIOLLI, M. A. ; BOCCA, F. F. ; RODRIGUES, L. H. A. . Neglecting spatial autocorrelation causes underestimation of the error of sugarcane yield models. Computers and Electronics in Agriculture. v. 161, p. 233-240, 2019.
02
PELOIA, P.R. ; BOCCA, F. F. ; RODRIGUES, L. H. A. . Identification of patterns for increasing production with decision trees in sugarcane mill data. Scientia Agricola, v. 76, p. 281-289, 2019.
02
OLIVEIRA, M.P.G. de; BOCCA, F.F. ; RODRIGUES, L. H. A. From spreadsheets to sugar content modeling: a data mining approach. Computers and Electronics in Agriculture, v. 132, p.14-20, 2017.
02
PELOIA, P.R. ; RODRIGUES, L. H. A. Identification of commercial blocks of outstanding performance of sugarcane using data mining. Engenharia Agrícola, v. 36, p. 895-901, 2016.
02
BOCCA, F.F. ; RODRIGUES, L. H. A. The effect of tuning, feature engineering, and feature selection in data mining applied
to rainfed sugarcane yield modelling. Computers and Electronics in Agriculture, v. 128, p.67-76, 2016.
to rainfed sugarcane yield modelling. Computers and Electronics in Agriculture, v. 128, p.67-76, 2016.
1
BOCCA, F.F. ; RODRIGUES, L. H. A. ; ARRAES, N.A.M. When do I want to know and why? Different demands on sugarcane yield predictions. Agricultural Systems, v. 135, p. 48-56, 2015.
Participation in events
1
OLIVEIRA, M. P. G. ; RODRIGUES, L. H. A. . How does atypical weather affect sugar content estimates obtained by machine learning?. In: XI Congresso Brasileiro de Agroinformática, 2017, Campinas, SP. Anais do XI Congresso Brasileiro de Agroinformática, 2017. (Anais do Congresso - PDF 84 MB)
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FERRACIOLLI, M. A. ; BOCCA, F. F. ; RODRIGUES, L. H. A. . Efeito da autocorrelação espacial na avaliação de modelos empíricos de produtividade na cana-de-açúcar. In: XI Congresso Brasileiro de Agroinformática, 2017, Campinas, SP. Anais do XI Congresso Brasileiro de Agroinformática, 2017. (Anais do Congresso - PDF 84 MB - In Portuguese)
1
FERRACIOLLI, M. A. ; BOCCA, F. F. ; RODRIGUES, L. H. A. . Neglecting autocorrelation in development degrades performance of sugarcane yield models. In: I International Conference on Agro BigData and Decision Support Systems in Agriculture, 2017, Montevideo. Proceedings of the First International Conference on Agro Big Data and Decision Support Systems in Agriculture, 2017. v. 125. p. 123. (Anais do Congresso - PDF 42 MB)
1
RODRIGUES, L. H. A.; BOCCA, F. F. . Sugarcane Yield Estimate Analysis by using Regression Error Characteristic Curves (REC Curves). In: I International Conference on Agro BigData and Decision Support Systems in Agriculture, 2017, Montevideo. Proceedings of the First International Conference on Agro Big Data and Decision Support Systems in Agriculture, 2017. p. 167-169. (Anais do Congresso - PDF 42 MB)
1
FERRACIOLLI, M. A.; RODRIGUES, L. H. A.; BOCCA, F. F.. Neglecting spatial autocorrelation leads to underestimation of the error in the development of sugarcane yield models. In: XXV Congresso de Iniciação Cientifica da Unicamp, 2017, 2017. v. 3.
1
RIBEIRO, N. V.; RODRIGUES, L. H. A.; OLIVEIRA, M. P. G. de ; BOCCA, F. F.. Development of predictive models using Data Mining techniques to detect borer infestation (Diatraea saccharalis) in sugarcane culture. In: XXV Congresso de Iniciação Cientifica da Unicamp, 2017, 2017. v. 3.
1
SIQUEIRA, T. da S.; RODRIGUES, L. H. A.; BOCCA, F. F.; OLIVEIRA, M. P. G. de. Decision trees for knowledge discovery on the yield decline of sugarcane ratoons. In: XXV Congresso de Iniciação Cientifica da Unicamp, 2017, 2017. v. 3.
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POLEZ, R. T.; RODRIGUES, L. H. A.; BOCCA, F. F.. Partial dependence plots for inspecting machine learning models of sugarcane yield. In: XXIV Congresso de Iniciação Científica da UNICAMP, 2016.
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SIQUEIRA, T. da S.; RODRIGUES, L. H. A.; BOCCA, F. F.. REC curves for visual evaluation of sugarcane yield machine learning models. In: XXIV Congresso de Iniciação Científica da UNICAMP 2016.
1
DE GENARO, L.F.A. ; BOCCA, F.F. ; RODRIGUES, L. H. A. Identificação visual de padrões de erros na estimativa de produtividade de cana-de-açúcar feita em campo por especialistas. In: XXII Congresso de Iniciação Científica da Unicamp, 2014, Campinas, SP. Caderno de Resumos do XXII Congresso de Iniciação Científica da UNICAMP, 2014. v. 1. p. 415-415.
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MENGARDA, C.S. ; BOCCA, F.F. ; RODRIGUES, L. H. A. Estudo da Curva REC (Regression Error Characteristic Curves) nas estimativas de produtividade de cana-de-açúcar. In: XXII Congresso de Iniciação Científica da Unicamp, 2014, Campinas, SP. Caderno de Resumos do XXII Congresso de Iniciação Científica da UNICAMP, 2014. v. 1. p. 415-415.
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RODRIGUES, L. H. A.; BOCCA, F.F. ; PELOIA, P.R. . Sugarcane Yield Prediction by Using Data Mining Techniques. In: BBEST - 2nd Brazilian BioEnergy Science and Technology Conference, 2014, Campos do Jordão. Proceedings of the 2nd BBEST, 2014. p. 1-1.