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<p>Postharvest losses during apple storage pose significant challenges to the economic sustainability of fruit production systems worldwide. In Türkiye, particularly in the Karaman region, traditional cold storage operations typically rely on fixed temperature and humidity settings that do not adapt to dynamic fruit quality parameters or changing environmental conditions. This study develops and evaluates a&#xa0;quality-parameter-based fuzzy logic control system designed to optimize apple cold storage under real commercial conditions. A&#xa0;Mamdani-type fuzzy logic system incorporating five measurable inputs—physical quality index, chemical quality index, remaining shelf life, market value, and external climate impact—and 81&#xa0;inference rules was implemented in a&#xa0;1200‑t commercial facility. The system was tested over a&#xa0;7-month storage period (August 2023–March 2024), comparing traditional fixed control, fuzzy logic control, and operator-managed hybrid strategies. Results show that the fuzzy logic system achieved 23.2% energy savings (142 vs. 185 kWh/t and month) while significantly improving quality preservation, with the percentage of marketable fruit increasing for ‘Golden Delicious’ (84.5% to 90.2%), ‘Granny Smith’ (86.2% to 91.5%), and ‘Starking’ (82.8% to 88.5%). Economic analysis indicated an 81.8% increase in net profit (1,745,377&#xa0;TL additional profit), with a&#xa0;5.5-month payback period and 348% return on investment. These findings demonstrate the feasibility and practical benefits of quality-based adaptive control in apple cold storage, offering substantial improvements in energy efficiency, product quality, sustainability, and overall profitability.</p>

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Quality-Based Adaptive Fuzzy Logic Control System for Apple Cold Storage Optimization: A Case Study in Karaman, Türkiye

  • 期刊论文

  • 发表日期：

    2026年3月17日

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  • Mehmet Cabir Akkoyunlu

摘要

Postharvest losses during apple storage pose significant challenges to the economic sustainability of fruit production systems worldwide. In Türkiye, particularly in the Karaman region, traditional cold storage operations typically rely on fixed temperature and humidity settings that do not adapt to dynamic fruit quality parameters or changing environmental conditions. This study develops and evaluates a quality-parameter-based fuzzy logic control system designed to optimize apple cold storage under real commercial conditions. A Mamdani-type fuzzy logic system incorporating five measurable inputs—physical quality index, chemical quality index, remaining shelf life, market value, and external climate impact—and 81 inference rules was implemented in a 1200‑t commercial facility. The system was tested over a 7-month storage period (August 2023–March 2024), comparing traditional fixed control, fuzzy logic control, and operator-managed hybrid strategies. Results show that the fuzzy logic system achieved 23.2% energy savings (142 vs. 185 kWh/t and month) while significantly improving quality preservation, with the percentage of marketable fruit increasing for ‘Golden Delicious’ (84.5% to 90.2%), ‘Granny Smith’ (86.2% to 91.5%), and ‘Starking’ (82.8% to 88.5%). Economic analysis indicated an 81.8% increase in net profit (1,745,377 TL additional profit), with a 5.5-month payback period and 348% return on investment. These findings demonstrate the feasibility and practical benefits of quality-based adaptive control in apple cold storage, offering substantial improvements in energy efficiency, product quality, sustainability, and overall profitability.