The neural network learns the patterns from the input **data** by reading the input **data** set and applying different computations to it. However, the neural network does not just do this once; it learns repeatedly using the input **data** set and also the results of previous tests. Each step in learning from the input **data** set is called an epoch. That is, an epoch refers to one cycle in the entire training **data** set [48,49]. Initially, CNN were trained with a large number of epochs or steps (iterations) to ensure that the smallest loss would be within that step range. After the first training, we determined an ideal number of steps to obtain the least loss to optimize the analyses and repetitions that would be performed; this test served fundamentally to know how many epochs would be necessary for the final model fit; this step was essential because an excessive number of epochs leads the model to overfitting, i.e., in this case, it will present results with very good statistical metrics, but with erroneous identification of species.