Artificial neural networks as an alternative to the traditional statistical methodology in plant research

Abstract

In this work, we compared the unique artificial neural networks (ANNs) technology with the usual statistical analysis to establish its utility as an alternative methodology in plant research. For this purpose, we selected a simple in vitro proliferation experiment with the aim of evaluating the effects of light intensity and sucrose concentration on the success of the explant proliferation and finally, of optimizing the process taking into account any influencing factors. After data analysis, the traditional statistical procedure and ANNs technology both indicated that low light treatments and high sucrose concentrations are required for the highest kiwifruit microshoot proliferation under experimental conditions. However, this particular ANNs software is able to model and optimize the process to estimate the best conditions and does not need an extremely specialized background. The potential of the ANNs approach for analyzing plant biology processes, in this case, plant tissue culture data, is discussed.