Machine Learning Unmasked Nutritional Imbalances on the Medicinal Plant Bryophyllum sp. Cultured in vitro

Pascual García-Pérez; Eva Lozano-Milo; Mariana Landin; Pedro Pablo Gallego

doi:10.3389/fpls.2020.576177

Machine Learning Unmasked Nutritional Imbalances on the Medicinal Plant Bryophyllum sp. Cultured in vitro

Front Plant Sci. 2020 Dec 1:11:576177. doi: 10.3389/fpls.2020.576177. eCollection 2020.

Authors

Pascual García-Pérez^{1

2}, Eva Lozano-Milo^{1

2}, Mariana Landin^{3

4}, Pedro Pablo Gallego^{1

2}

Affiliations

¹ Applied Plant and Soil Biology, Plant Biology and Soil Science Department, Biology Faculty, University of Vigo, Vigo, Spain.
² Clúster de Investigación e Transferencia Agroalimentaria do Campus da Auga - Agri-Food Research and Transfer Cluster, University of Vigo, Ourense, Spain.
³ Grupo I+D Farma (GI-1645), AeMat, Pharmacology, Pharmacy and Pharmaceutical Technology Department, Pharmacy Faculty, University of Santiago, Santiago de Compostela, Spain.
⁴ Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain.

Abstract

Plant nutrition is a crucial factor that is usually underestimated when designing plant in vitro culture protocols of unexploited plants. As a complex multifactorial process, the study of nutritional imbalances requires the use of time-consuming experimental designs and appropriate statistical and multiple regression analysis for the determination of critical parameters, whose results may be difficult to interpret when the number of variables is large. The use of machine learning (ML) supposes a cutting-edge approach to investigate multifactorial processes, with the aim of detecting non-linear relationships and critical factors affecting a determined response and their concealed interactions. Thus, in this work we applied artificial neural networks coupled to fuzzy logic, known as neurofuzzy logic, to determine the critical factors affecting the mineral nutrition of medicinal plants belonging to Bryophyllum subgenus cultured in vitro. The application of neurofuzzy logic algorithms facilitate the interpretation of the results, as the technology is able to generate useful and understandable "IF-THEN" rules, that provide information about the factor(s) involved in a certain response. In this sense, ammonium, sulfate, molybdenum, copper and sodium were the most important nutrients that explain the variation in the in vitro culture establishment of the medicinal plants in a species-dependent manner. Thus, our results indicate that Bryophyllum spp. display a fine-tuning regulation of mineral nutrition, that was reported for the first time under in vitro conditions. Overall, neurofuzzy model was able to predict and identify masked interactions among such factors, providing a source of knowledge (helpful information) from the experimental data (non-informative per se), in order to make the exploitation and valorization of medicinal plants with high phytochemical potential easier.

Keywords: ANNs; ASMOD; Kalanchoe; artificial intelligence algorithms; media mineral nutrition; plant in vitro culture; structural risk minimization (SRM).