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

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 establishment of intricate and time-consuming experimental designs, whose results may be difficult to interpret. The use of artificial intelligence (AI) systems, based on machine learning (ML) algorithms, supposes a cutting-edge approach to investigate multifactorial processes, with the aim of detecting critical factors affecting a determined response and their concealed interactions. Thus, in this work we applied artificial neural networks (ANNs) 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. In this sense, ammonium, sulfate and calcium, as macronutrients, and copper, manganese and molybdenum, as micronutrients, were the most significant nutrients affecting the in vitro culture establishment in a species-dependent manner, depending in most cases on the number of subcultures. 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.