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.

Highlights

  • Tomato late blight, caused by Phytophthora infestans (Mont.) de Bary, can cause complete loss if it is not properly controlled, and has been considered one of the most devastating tomato diseases worldwide (Irzhansky and Cohen, 2006). Nowicki et al (2012) reported yield losses of up to 100 % caused by the pathogen

  • Artificial neural networks (ANN) were used to predict the value of the area under the disease progress curve (AUDPC) for the tomato late blight 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

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Summary

Introduction

Tomato late blight, caused by Phytophthora infestans (Mont.) de Bary, can cause complete loss if it is not properly controlled, and has been considered one of the most devastating tomato diseases worldwide (Irzhansky and Cohen, 2006). Nowicki et al (2012) reported yield losses of up to 100 % caused by the pathogen. Genetic resistance is considered the most efficient method for controling plant pathogens, since it reduces production costs, facilitates disease management, and does not have the impacts produced by fungicides. The area under the disease progress curve (AUDPC) is a valuable tool for measuring harvest losses due to pathogen attack (Ferrandino and Elmer, 1992) and in epidemiological studies of polycyclic diseases, especially those regarding quantitative resistance studies (Jeger and Viljanen-Rollinson, 2001). The conventional estimator of AUDPC is the equation developed by Shaner and Finney (1977), which considers the information of multiple severity evaluations, and yields a single value. Jeger and Viljanen-Rollinson (2001) proposed a method for calculating the AUDPC with only two evaluations for the wheat pathosystem – Puccinia striiformis f.

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