Computer-Based Tools Unmask Critical Mineral Nutrient Interactions in Hoagland Solution for Healthy Kiwiberry Plant Acclimatization

Abstract

The aim of this study was to better understand the response of ex vitro acclimatized plants grown to a set of mineral nutrient combinations based on Hoagland solution. To reach that, two computer-based tools were used: the design of experiments (DOE) and a hybrid artificial intelligence technology that combines artificial neural networks with fuzzy logic. DOE was employed to create a five-dimensional IV-design space by categorizing all macroelements and one microelement (copper) of Hoagland mineral solution, reducing the experimental design space from 243 (35) to 19 treatments. Typical growth parameters included hardening efficiency (Hard), newly formed shoot length (SL), total leaf number (TLN), leaf chlorophyll content (LCC), and leaf area (LA). Moreover, three physiological disorders, namely, leaf necrosis (LN), leaf spot (LS), and curled leaf (CL), were evaluated for each treatment (mineral formulation). All the growth parameters plus LN were successfully modeled using neuro-fuzzy logic with a high train set R2 between experimental and predicted values (72.67 < R2 < 98.79). The model deciphered new insights using different sets of “IF–THEN” rules, pinpointing the positive role of Mg2+ and Ca2+ to improve Hard, SL, TLN, and LA and alleviate LN but with opposite influences on LCC. On the contrary, TLN and LCC were negatively affected by the addition of NO3– into the media, while NH4+ in complex interaction with Cu2+ or Mg2+ positively enhanced SL, TLN, LCC, and LA. In our opinion, the approach and results achieved in this work are extremely fruitful to understand the effect of Hoagland mineral nutrients on the healthy growth of ex vitro acclimatized plants, through identifying key factors, which favor growth and limit physiological abnormalities.

History, Phylogeny, Biodiversity, and New Computer-Based Tools for Efficient Micropropagation and Conservation of Pistachio (Pistacia spp.) Germplasm

“…Among different tools of AI, Artificial Neural Networks (ANNs) and ML, Fuzzy Logic (FL),algorithms are considered the most important scientific advances of recent years with a range of important applications in understanding plant complex bioprocesses [ 210 , 213 , 214 ], showing many advantages over traditional statistics [ 215 , 216 ]. Different combination of ANNs plus FL and Genetic Algorithms have been used to model complex in vitro processes such as shoot multiplication [ 217 ], rhizogenesis and acclimatization [ 218 ], seed germination [ 219 ], designing plant culture media [ 178 , 220 ] and elicitation of bioactive compounds accumulation [ 221 ].…”

Survey of Machine Learning and Optimization Algorithms in Plant Tissue Culture

“…The model successfully identified optimum levels and combinations of inputs to achieve maximum growth and development during in vitro rooting and acclimatization. In [32], a design of experiments (DOE) approach was used to create a five-dimensional IV-design space, coupled with a hybrid of artificial neural networks (ANN) and fuzzy logic to assess the effects of varying mineral concentrations in a Hoagland mineral solution on growth parameters (newly formed shoot length, total leaf number, leaf area, leaf chlorophyll content, and hardening efficiency) and three physiological disorders during the ex vitro acclimatization of Actinidia arguta. The neuro-fuzzy logic effectively modeled all growth metrics and the occurrence of a physiological disorder known as leaf necrosis, showing a strong correlation between observed and predicted values.…”

Neurofuzzy logic predicts a fine-tuning metabolic reprogramming on elicited Bryophyllum PCSCs guided by salicylic acid

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