The aim of this study was to determine the effects of Murashige and Skoog (MS) salts on optimal growth of two pistachio rootstocks, cv. "Ghazvini" and "UCB1" using design of experiments (DOE) and artificial intelligence (AI) tools. MS medium with 14 macro-and micro-elements was used as base point and its concentration varied from 0 to 5 × MS concentrations. Design of experiments (DOE) software was used to generate a five-dimensional design space by categorizing MS salts into five independent factors (NHNO, KNO, mesos, micros and iron), reducing the experimental design space from 3,125 to just 29 treatments. Typical plant growth parameters such as shoot quality (SQ), proliferation rate (PR), shoot length (SL), and some physiological disorders including shoot-tip necrosis (STN) and callus formation at the base of explants (BC) were evaluated for each treatment. The results were successfully modeled using neurofuzzy logic software. The model delivered new insights, by different sets of "IF-THEN" rules, pinpointing the key role of some ion interactions ( × Cl, K × × EDTA, and Fe × Cu × ) for SQ, PR, and SL, whilst physiological disorders (STN and BC) were governed mainly by independent ions as Fe and EDTA, respectively. In our opinion, the methodology and results obtained in this study is extremely useful to understand the effect of mineral nutrients on pistachio culture, through discovering new complex interactions among macro-and micro-elements which can be implemented to design new media of plant tissue culture and improve healthy plant micropropagation for any plant species.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6196285PMC
http://dx.doi.org/10.3389/fpls.2018.01474DOI Listing

Publication Analysis

Top Keywords

neurofuzzy logic
8
pistachio rootstocks
8
design experiments
8
experiments doe
8
macro-and micro-elements
8
design space
8
physiological disorders
8
design
5
combining doe
4
doe neurofuzzy
4

Similar Publications

This paper addresses the smart management and control of an independent hybrid system based on renewable energies. The suggested system comprises a photovoltaic system (PVS), a wind energy conversion system (WECS), a battery storage system (BSS), and electronic power devices that are controlled to enhance the efficiency of the generated energy. Regarding the load side, the system comprises AC loads, DC loads, and a water pump.

View Article and Find Full Text PDF

Soil temperature estimation at different depths using machine learning paradigms based on meteorological data.

Environ Monit Assess

December 2024

Department of VLSI Microelectronics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, 602105, Tamil Nadu, India.

Knowledge of soil temperature (ST) is important for analysing environmental conditions and climate change. Moreover, ST is a vital element of soil that impacts crop growth as well as the germination of the seeds. In this study, four machine-learning (ML) paradigms including random forest (RF), radial basis neural network (RBNN), multi-layer perceptron neural network (MLPNN), and co-active neuro-fuzzy inference system (CANFIS) were used for estimation of daily ST at different soil depths (i.

View Article and Find Full Text PDF

Energy-efficient data routing using neuro-fuzzy based data routing mechanism for IoT-enabled WSNs.

Sci Rep

December 2024

Department of Electronics and Communication Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603203, India.

This paper proposes a novel Neuro-fuzzy-based Data Routing (NFDR) mechanism for efficient data routing and dynamic cluster formation in Internet of Things (IoT) enabled Wireless Sensor Networks (WSNs). The NFDR mechanism incorporates optimal scalability factors computed from past and present network parameter values, acting as an additional buffer factor to sustain nodes within clusters, even with partial satisfaction of network parameter values. The neural network determines cluster formation requirements, while the objective function adjusts according to the updated fuzzy logic of identified cluster members.

View Article and Find Full Text PDF

Spatial prediction of human brucellosis susceptibility using an explainable optimized adaptive neuro fuzzy inference system.

Acta Trop

December 2024

Department of Pathology, Microbiology, and Immunology, School of Veterinary Medicine, University of California, Davis, USA. Electronic address:

Brucellosis, a zoonotic disease caused by Brucella bacteria, poses significant risks to human, livestock, and wildlife health, alongside economic losses from livestock morbidity and mortality. This study improves Human Brucellosis Susceptibility Mapping (HBSM) by integrating the Adaptive Neuro-Fuzzy Inference System (ANFIS) with meta-heuristic algorithms, including Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Boruta-XGBoost identified key covariates, while VIF and tolerance tests addressed collinearity, and Shapley additive explanation (SHAP) values enhanced model interpretability.

View Article and Find Full Text PDF

Millet protein, as a promising plant-based protein substitute source, is an excellent basis for essential amino acids compared to commonly consumed staple grains. Compared with the traditional extraction process, ultrasound has been used to enhance the extraction efficiency of various plant-based proteins. To reveal the mechanism of ultrasound-enhanced extraction of proteins, adaptive neuro-fuzzy inference system (ANFIS) algorithm and numerical simulation based on Fick's law were applied to illustrate the mass transfer behavior of millet proteins under different ultrasonic conditions including solid-liquid ratios (S/L ratios), pH and acoustic energy density levels (AED).

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!