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<p>The rising demand for spirulina (<i>Limnospira</i> spp.) highlights the need for affordable cultivation methods and practical biomass monitoring solutions. This study introduces a novel, low-cost, Raspberry Pi-based system for real-time monitoring and automated biomass recovery in microalgal cultivation. The system integrates turbidity, light, pH, and temperature sensors with an automated module for harvesting and medium replenishment. Cultures of the filamentous, spiral-shaped microalga <i>Limnospira fusiformis</i> were used to evaluate system performance. The turbidity sensor showed strong correlation with optical density (<i>R</i><sup>2</sup> = 0.943–0.986, <i>p</i> &lt; 0.05) and dry weight (<i>R</i><sup>2</sup> = 0.954–0.975, <i>p</i> &lt; 0.05). Light, pH, and temperature sensors demonstrated average percentage errors of 0.50%, 0.58%, and 2.52%, respectively (<i>p</i> &lt; 0.05). The auto-recovery system successfully maintained biomass concentration within a narrow range (OD<sub>750</sub> = 0.67–0.74) using adjustable set points tailored to cultivation needs. Real-time data were auto-logged to Google spreadsheets for remote access. With an estimated cost of $340, the system offers a practical, time-saving, and cost-effective solution for real-time biomass monitoring and control in microalgae cultivation.</p>

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Development of a low-cost real-time monitoring system for biomass concentration and environmental factors in microalgae Limnospira fusiformis cultivation

  • 期刊论文

  • 发表日期：

    2025年10月13日

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  • Desalegn Tadesse Workie,
  • Anupreet Kaur Chowdhary,
  • Mutsumi Sekine,
  • Washburn Larry,
  • Ayirkm Adugna Woldie,
  • Masatoshi Kishi,
  • Tatsuki Toda

摘要

The rising demand for spirulina (Limnospira spp.) highlights the need for affordable cultivation methods and practical biomass monitoring solutions. This study introduces a novel, low-cost, Raspberry Pi-based system for real-time monitoring and automated biomass recovery in microalgal cultivation. The system integrates turbidity, light, pH, and temperature sensors with an automated module for harvesting and medium replenishment. Cultures of the filamentous, spiral-shaped microalga Limnospira fusiformis were used to evaluate system performance. The turbidity sensor showed strong correlation with optical density (R2 = 0.943–0.986, p < 0.05) and dry weight (R2 = 0.954–0.975, p < 0.05). Light, pH, and temperature sensors demonstrated average percentage errors of 0.50%, 0.58%, and 2.52%, respectively (p < 0.05). The auto-recovery system successfully maintained biomass concentration within a narrow range (OD750 = 0.67–0.74) using adjustable set points tailored to cultivation needs. Real-time data were auto-logged to Google spreadsheets for remote access. With an estimated cost of $340, the system offers a practical, time-saving, and cost-effective solution for real-time biomass monitoring and control in microalgae cultivation.