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

Sara Maleki; Bahram Maleki Zanjani; Bahram Baghban Kohnehrouz; Mariana Landin; Pedro Pablo Gallego

doi:10.3389/fpls.2021.723992

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

Front Plant Sci. 2021 Oct 28:12:723992. doi: 10.3389/fpls.2021.723992. eCollection 2021.

Authors

Sara Maleki^{1

2}, Bahram Maleki Zanjani¹, Bahram Baghban Kohnehrouz³, Mariana Landin⁴, Pedro Pablo Gallego²

Affiliations

¹ Department of Agronomy and Plant Breeding, Faculty of Agriculture, University of Zanjan, Zanjan, Iran.
² Agrobiotech for Health, Department of Plant Biology and Soil Science, Faculty of Biology, University of Vigo, Vigo, Spain.
³ Department of Plant Breeding and Biotechnology, University of Tabriz, Tabriz, Iran.
⁴ Department of Pharmacology, Pharmacy and Pharmaceutical Technology Department, Grupo I+D Farma (GI-1645), Faculty of Pharmacy, University of Santiago, Santiago de Compostela, Spain.

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 R ² between experimental and predicted values (72.67 < R ² < 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.

Keywords: Actinidia arguta; DOE; artificial intelligence; ex vitro acclimatization; healthy plants; kiwiberry; machine learning; physiological disorders.