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

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Academic year: 2019

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Table 1 − Area under the disease-progress curve (AUDPC) averages for one hundred and ninety tomato accessions, assessed for late-blight  caused by Phytophythora infestans in four independent experiments.
Table 2 − Average correlation for training and validation using different severity evaluation combinations
Figure 1 − Area under the disease-progress curve (AUDPC) estimates - transformed data - of the genotypes considered during training (A) and  validation (B) for scenario 1
Table 4 − Accession grouping according to Scott-Knott clustering, comparing area under the disease-progress curve (AUDPC) averages  conventionally calculated, with six evaluations, and by artificial neural networks (ANN), with three evaluations (A) - 3 rd
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