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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
Published on: December 9, 2012
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A robust decision framework for vertical farming using integrated objective weighting and distance driven ranking
Navaneethakrishnan Suganthi Keerthana1, Samayan Narayanamoorthy2, Kannusamy Aarthi1
1Department of Mathematics, Bharathiar University, Coimbatore, 641046, India.
Scientific Reports
|November 19, 2025
Summary
Vertical farming, including hydroponics and aquaponics, offers high yields with minimal resources. Aquaponics is identified as the optimal vertical farming method, followed by aeroponics, fogponics, and hydroponics.
Area of Science:
- Agricultural Science
- Environmental Science
- Engineering
Background:
- Vertical farming techniques cultivate crops without soil, maximizing productivity and profit.
- These methods require fewer resources and yield high-quality produce.
- Analysis includes hydroponics, aquaponics, aeroponics, and fogponics.
Purpose of the Study:
- To comprehensively analyze various vertical farming techniques.
- To introduce a novel decision-making framework for technology selection.
- To quantify uncertainty using q-rung picture fuzzy sets.
Main Methods:
- A hybrid Logarithmic percentage change-driven objective weighting-Multi-attributive border approximation area comparison framework was developed.
- Distance measures were employed for objective analysis.
- Q-rung picture fuzzy sets were used for uncertainty quantification.
Main Results:
- Production yield is a critical factor in selecting optimal vertical farming technology.
- The study ranked aquaponics as the ideal method, followed by aeroponics, fogponics, and hydroponics.
- Framework validation was performed through comparative analysis and Spearman's rank correlation.
Conclusions:
- Aquaponics emerges as the most effective vertical farming technique based on the proposed framework.
- The developed methodology provides a reliable and accurate approach to evaluating vertical farming systems.
- Sensitivity analysis confirmed the model's accuracy and robustness.
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