Multi-objective optimization identifies cultivation strategies for balancing yield, quality, and resource efficiency in hydroponic netted melon

TL;DR

This study finds optimal hydroponic cultivation strategies for netted melon by balancing yield, quality, and resource efficiency using multi-objective optimization.

Contribution

The paper introduces a quantitative decision-support framework using Pareto frontier analysis to identify cultivation strategies tailored to specific market segments.

Findings

Three optimal strategies were identified for profit, quality, and resource efficiency in melon cultivation.

A 10 L substrate volume improved economic performance and water productivity compared to 20 L.

Higher planting density increased yield but reduced fruit quality.

Abstract

Managing the trade-offs between yield maximization and fruit quality is a critical challenge in hydroponic melon production. To identify optimal cultivation strategies, we evaluated three netted melon cultivars (‘Dalgona’, ‘Hero’, and ‘Kingstar’) under restricted (10 L) versus standard (20 L) substrate volumes and two planting densities (3 vs. 4 plants/slab). We utilized Pareto frontier analysis to visualize the balance among fruit quality, resource use efficiency, and economic performance. The analysis revealed three distinct optimal strategies targeting specific market segments: profit-maximization (‘Hero’, 10 L, 4 plants/slab, 268% return on investment [ROI]), premium-quality focus (‘Dalgona’, 10 L, 3 plants/slab, 100% premium-grade), and resource-efficiency (‘Kingstar’, 10 L, 3 plants/slab, water productivity of 5.7 kg m⁻³). While increasing planting density enhanced total yield by 20.7%, it compromised internal fruit quality. The 10 L substrate volume achieved superior economic performance (ROI 42.9%–268.0%) across all cultivars by inducing a root restriction effect that improved sugar content and water productivity compared to the 20 L standard. These findings provide a quantitative decision-support framework enabling producers to select optimal parameter combinations tailored to their specific management objectives.

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