Strengths of artificial neural networks in modeling complex plant processes

Abstract

Commonly, simple mathematical models can not be used to describe exactly the biological processes due to their higher complexity. In fact, most biological interactions cannot be elucidated by a simple stepwise algorithm or a precise formula, particularly when the data are complex or noisy. ANNs allows an accurate description of those kind of biological processes in plant science, offering new advantages over traditional treatments as the possibility of a model, prediction and optimize results. Different kind of data can be analyzed using a unique and "easy to use" technology. Researchers with a high specialized mathematical background are not required and ANNs offer the possibility of achieving the whole view of the experimental study with a limited number of experiments and costs. Additionally, it is possible to add new inputs and outputs to the database to reach a new understanding.

Figure 1

Experimental versus predicted values by ANN s for the different parameters studied. (■) Correlation point for data used for model developing, (○) correlation point for data not included for model developing (validation data).