Optimization of the number of evaluations for early blight disease in tomato accessions using artificial neural networks
Graphical abstract
Introduction
The tomato (Solanum lycopersicum L.), originally from South America, is one of the world’s most cultivated crops (Ranjan et al., 2012). It has a high yield, but is economically risky, mainly due to disease. Early Blight Disease (EBD), caused by the fungus Alternaria tomatophila Simmons, causes tomato crop losses (Brun et al., 2013).
A wide range of hosts, high variability, and a prolonged active stage all hamper the control of EBD (Singh et al., 2014). Relative humidity higher than 80% and moderate temperatures, of around 27 °C, favor the disease (Foolad et al., 2000). EBD is controlled primarily with fungicides. A few resistant tomato cultivars are also available (Ashrafi and Foolad, 2015). Chemical control is expensive and causes problems for humans and the environment (Hariprasad and Niranjana, 2009), however resistant cultivars can reduce this problem. This makes identification of sources of resistance in tomato accessions of germplasm banks highly valuable.
Data from six to eight evaluations of EBD severity processed at regular intervals, of- the area under the disease’s progress curve (AUDPC) (Mukherjee et al., 2010) can identify superior tomato genotypes for breeding programs (Grigolli et al., 2011, Foolad and Ashrafi, 2015, Rani et al., 2015). These assessments are labor-intensive and are usually carried out in the field using a large number of genotypes (Foolad et al., 2008, Kumar and Srivastava, 2013).
Artificial neural network (ANN) models were used to predict average regional wheat yields and production (Alvarez, 2009), environmental impact on strawberry production, (Khoshnevisan et al., 2013) and biological and environmental factors influencing single pea seed mass (Dacko et al., 2016). The efficiency of ANNs to model complex problems can predict AUDPC using fewer evaluations. This reduces labor and costs during selection of tomato accessions resistant to EBD. ANNs were also applied to predict genetic resource characteristics in plant breeding (Pandolfi et al., 2009, Bari et al., 2012, Emamgholizadeh et al., 2015). ANN computational models, which mimic the human brain in recognizing data patterns and regularities, represent an alternative as a universal substitute for complex functions (Gianola et al., 2011). They can have superior performance compared to conventional statistical models by being non-parametric, not requiring detailed information on the physical processes of the system studied, as well as tolerating data loss.
The objective of this study was to verify the efficiency of ANNs to predict the area under the progress disease curve (AUDPC) of Early Blight Disease (EBD) using fewer evaluations to establish the best time for its evaluation in tomato accessions.
Section snippets
Material and methods
The resistance of one hundred and thirty-five tomato accessions from the Germplasm Vegetable Bank of the Federal University of Viçosa (BGH-UFV) for Early Blight Disease (EBD) was evaluated in three experiments: the first from October 2009 to January 2010 with 33 accessions; the second from July to October 2010 with 51 accessions; and the third from September to December 2010 with 51 accessions (Laurindo et al., 2015). These accessions were randomly selected from approximately 840 accessions of
Results
The results from data with one evaluation showed less satisfactory results than that with two evaluations (Table 1). Average correlation of 0.19, −0.21, and 0.32 AUDPC between values calculated and predicted by the “Network 1”, “Network 2” and “Network 3”, respectively, was obtained. The best results were those from the fifth evaluation, with an average correlation between calculated and predicted AUDPC of 0.90, 0.91, and 0.91 for “Network 1”, “Network 2” and “Network 3”, respectively (Table 1).
Discussion
Artificial neural networks (ANN) simulate the biological neural system, mimicking the human brain’s learning processes to solve complex problems (Odabas et al., 2013). This technique has advantages of being non-parametric, tolerates data losses, and does not require detailed information on the system to be modeled (Silva et al., 2014) to solve complex problems of a linear or non-linear nature (Azevedo et al., 2015). Therefore, it may be useful to predict AUDPC with fewer evaluations.
The low
Conclusions
ANNs were effective at predicting the area under Early Blight Disease progress curve (AUDPC) in tomatoes, optimizing the assessment efficiency for disease severity and reducing from six to two the number of evaluations to be carried out on the twelfth and eighteenth days after inoculation of this disease.
Acknowledgements
To “Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)” and “Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)”. Dr. Phillip Villani revised and corrected the English language used in this manuscript.
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