ALG-SDE: Developing an Arduino-Based System for Automated Algae Detection and Algaecide Treatment

Harmful algal blooms (HABs) present ecological and public health threats in freshwater systems in the Philippines. Despite attempts of traditional mitigation techniques, they are limited by manual monitoring and treatment delays. Which is why there is a need for automated systems that provide real-time intervention. This study developed and evaluated ALG-SDE, an Arduino-based prototype using pH, temperature, and turbidity sensors to detect and respond to algae conditions via algaecide dispensing. Results showed statistically significant reductions in pH (t = 39.28, p = 0.00065) and turbidity (t = 9.45, p= 0.011), with a strong inverse correlation (r = -0.87) between the two. Chi-square tests confirmed threshold-based activation inconsistencies (p = 0.038), suggesting sensor recalibration is needed. In conclusion, the system effectively alters algae-conducive conditions but requires improved reliability in real-time activation as well as mobility. Further iterations should focus on enhanced sensor accuracy, mobility features, and broader aquatic applicability.有害藻华（Harmful Algal Blooms, HABs）对菲律宾的淡水生态系统构成生态威胁与公共卫生风险。尽管已尝试采用传统防控技术，但此类手段受限于人工监测与处理滞后的弊端，因此亟需能够实现实时干预的自动化系统。本研究开发并评估了ALG-SDE系统：这是一款基于Arduino的原型装置，搭载pH、温度与浊度传感器，可通过投放除藻剂对藻情进行检测与响应。实验结果显示，pH值（t=39.28，p=0.00065）与浊度（t=9.45，p=0.011）均出现具有统计学意义的显著下降，且二者间存在较强的负相关关系（r=-0.87）。卡方检验结果证实了基于阈值的触发机制存在不一致性（p=0.038），表明需要对传感器进行重新校准。综上，该系统可有效改善利于藻类滋生的水环境，但仍需提升实时触发的可靠性与装置的移动性。后续迭代优化应聚焦于提升传感器精度、完善移动功能以及拓展该系统在更多水生场景中的适用性。