| Literature DB >> 35336599 |
Gabrijel Ondrasek1, Santosha Rathod2, Kallakeri Kannappa Manohara3, Channappa Gireesh2, Madhyavenkatapura Siddaiah Anantha2, Akshay Sureshrao Sakhare2, Brajendra Parmar2, Brahamdeo Kumar Yadav4, Nirmala Bandumula2, Farzana Raihan5, Anna Zielińska-Chmielewska6, Cristian Meriño-Gergichevich7, Marjorie Reyes-Díaz8, Amanullah Khan9, Olga Panfilova10, Alex Seguel Fuentealba11, Sebastián Meier Romero12, Beithou Nabil13, Chunpeng Craig Wan14, Jonti Shepherd1, Jelena Horvatinec1.
Abstract
Salinization of soils and freshwater resources by natural processes and/or human activities has become an increasing issue that affects environmental services and socioeconomic relations. In addition, salinization jeopardizes agroecosystems, inducing salt stress in most cultivated plants (nutrient deficiency, pH and oxidative stress, biomass reduction), and directly affects the quality and quantity of food production. Depending on the type of salt/stress (alkaline or pH-neutral), specific approaches and solutions should be applied to ameliorate the situation on-site. Various agro-hydrotechnical (soil and water conservation, reduced tillage, mulching, rainwater harvesting, irrigation and drainage, control of seawater intrusion), biological (agroforestry, multi-cropping, cultivation of salt-resistant species, bacterial inoculation, promotion of mycorrhiza, grafting with salt-resistant rootstocks), chemical (application of organic and mineral amendments, phytohormones), bio-ecological (breeding, desalination, application of nano-based products, seed biopriming), and/or institutional solutions (salinity monitoring, integrated national and regional strategies) are very effective against salinity/salt stress and numerous other constraints. Advances in computer science (artificial intelligence, machine learning) provide rapid predictions of salinization processes from the field to the global scale, under numerous scenarios, including climate change. Thus, these results represent a comprehensive outcome and tool for a multidisciplinary approach to protect and control salinization, minimizing damages caused by salt stress.Entities:
Keywords: artificial intelligence; neutral and alkaline salinity; plant–microbe associations; salinity and nanotechnology; salt stress; soil amendments
Year: 2022 PMID: 35336599 PMCID: PMC8950276 DOI: 10.3390/plants11060717
Source DB: PubMed Journal: Plants (Basel) ISSN: 2223-7747
Chemical speciation reactions for widely studied neutral and alkaline salt types dissolved in rhizosphere solution (after [12]).
| Neutral Salt Type | pH | Prevalent Ions (%) | Precipitated Forms |
|---|---|---|---|
| Sodium chloride | 7.94 | Na+ 98; | Magnesite |
| Potassium chloride | 7.94 | K+ 98; | |
| Magnesium chloride | 7.93 | Mg2+ 77; Cl− 98; Mg-OC 10; MgSO4 4; | |
| Calcium chloride | 7.93 | Ca2+ 78; Cl− 98; Ca-organo-complexed forms 6; | |
| Sodium sulphate | 7.94 | Na+ 98; SO42− 72; | |
|
|
|
| |
| Sodium hydrogencarbonate | 8.01 | Na+ 98; HCO3− 92; | |
| Sodium carbonate | 8.08 | Na+ 98.2; HCO3− 92; CaHCO3+ 2.2; | |
| Potassium carbonate | 8.03 | K+ 98; HCO3− 92; | |
| Magnesium carbonate | 8.07 | Mg2+ 75; Mg-organo-complexed forms 10; | |
| Calcium carbonate | 8.07 | Ca2+ 76; Ca-organo-complexed forms 6; |
Figure 1Vegetative and growth performances of lettuce (A) and strawberry (B) exposed to neutral salt stress (0–60 mM NaCl), and corn (C) exposed alkaline salt stress (0–10% wood-derived ash) with induced soil electrical conductivity (EC) and pH changes, after [12,26,27].
Figure 2Solutions for salinization management and mitigating salt stress in plants.
Performance of AI and/or ML models in selected studies.
| Area of Application | AI/ML Tools Applied | Best Performing Model | Reference |
|---|---|---|---|
| Soil resistance to penetration prediction | ANN, SVM | SVM | [ |
| Soil Survey Data, | SVM | [ | |
| Multiple linear regression (MLR), | RF | [ | |
| Disinfection protocol in seed germination | Generalized regression neural network (GRNN) | GRNN | [ |
| Extreme learning machine (ELM), RF, Ensemble empirical mode decomposition (EEMD)-ELM, EEMD-RF | EEMD-ELM | [ | |
| Soil electrical conductivity prediction | Multilayer Perceptron | Hybrid (MLP-GWO) Model | [ |
| SVM, ANN, RF | SVM | [ | |
| Multi-gene genetic programming (MGGP) | MGGP | [ | |
| RF, Naïve Bayes (NB), | LS-SVM and ANN | [ | |
| Soil organic carbon prediction | ANN, SV, RF, MLR | RF | [ |
| Chemical detection method, visible-near-infrared spectroscopy, and two-dimensional deep learning (2D-DL) | 2D-DL | [ | |
| Soil salinity prediction | Auto Encoder (AE), ANN, SVM, KNN, DT | AE-SVM | [ |
| Soil salinity prediction and mapping | MLR, RF Regression, SVR | RF Regression | [ |