Artificial neural network for prediction of the area under the disease progress curve of tomato late blight

Authors

  • Daniel Pedrosa Alves Santa Catarina State Agricultural Research and Rural Extension Agency; Experimental station of Ituporanga
  • Rafael Simões Tomaz São Paulo State University; College of Technology and Agricultural Sciences
  • Bruno Soares Laurindo Federal University of Viçosa; Dept. of Phytotechny
  • Renata Dias Freitas Laurindo Federal University of Viçosa; Dept. of Phytotechny
  • Fabyano Fonseca e Silva Federal University of Viçosa; Dept. of Animal Science
  • Cosme Damião Cruz Federal University of Viçosa; Dept. of General Biology
  • Carlos Nick Federal University of Viçosa; Dept. of Phytotechny
  • Derly José Henriques da Silva Federal University of Viçosa; Dept. of Phytotechny

DOI:

https://doi.org/10.1590/1678-992x-2015-0309

Keywords:

Phytophthora infestans, ANN, AUDPC, artificial intelligence, plant breeding

Abstract

Artificial neural networks (ANN) are computational models inspired by the neural systems of living beings capable of learning from examples and using them to solve problems such as non-linear prediction, and pattern recognition, in addition to several other applications. In this study, ANN were used to predict the value of the area under the disease progress curve (AUDPC) for the tomato late blight pathosystem. The AUDPC is widely used by epidemiologic studies of polycyclic diseases, especially those regarding quantitative resistance of genotypes. However, a series of six evaluations over time is necessary to obtain the final area value for this pathosystem. This study aimed to investigate the utilization of ANN to construct an AUDPC in the tomato late blight pathosystem, using a reduced number of severity evaluations. For this, four independent experiments were performed giving a total of 1836 plants infected with Phytophthora infestans pathogen. They were assessed every three days, comprised six opportunities and AUDPC calculations were performed by the conventional method. After the ANN were created it was possible to predict the AUDPC with correlations of 0.97 and 0.84 when compared to conventional methods, using 50 % and 67 % of the genotype evaluations, respectively. When using the ANN created in an experiment to predict the AUDPC of the other experiments the average correlation was 0.94, with two evaluations, 0.96, with three evaluations, between the predicted values of the ANN and they were observed in six evaluations. We present in this study a new paradigm for the use of AUDPC information in tomato experiments faced with P. infestans. This new proposed paradigm might be adapted to different pathosystems.

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Published

2017-02-01

How to Cite

Alves, D. P., Tomaz, R. S., Laurindo, B. S., Laurindo, R. D. F., Silva, F. F. e, Cruz, C. D., Nick, C., & Silva, D. J. H. da. (2017). Artificial neural network for prediction of the area under the disease progress curve of tomato late blight. Scientia Agricola, 74(1), 51-59. https://doi.org/10.1590/1678-992x-2015-0309

Issue

Section

Plant Pathology