Fuzzy Modeling Development for Lettuce Plants Irrigated with Magnetically Treated Water

Area of Science:

• Agricultural Engineering
• Water Management
• Agronomy

Background:

• Global water scarcity necessitates sustainable irrigation strategies.
• Magnetic water (MW) shows promise for enhancing irrigation efficacy.
• Accurate modeling is needed to correlate experimental results with MW irrigation.

Purpose of the Study:

• To estimate magnetic water replacement rates for lettuce production using fuzzy logic modeling.
• To compare the accuracy of fuzzy logic models against multiple polynomial regression.

Main Methods:

• Greenhouse study with lettuce using magnetic water (MW) and conventional water (CW).
• Five irrigation levels (25-125% of crop evapotranspiration) were applied.
• Biometric data collected over 35 days, analyzed by fuzzy logic and polynomial regression.

Main Results:

• Lettuce irrigated with MW exhibited superior biometric values across phenological stages.
• The fuzzy logic model demonstrated a more precise fit to the experimental data than polynomial regression.

Conclusions:

• Magnetic water irrigation positively impacts lettuce growth and yield.
• Fuzzy logic modeling offers a more accurate approach for optimizing magnetic water irrigation strategies.