用物联网+机器学习，实时监测鱼塘水质并判断是否适合养鱼。
Prediction Model of Aqua Fisheries Using IoT Devices
- 部署传感器与Arduino采集水温、pH等参数，上传至Thingspeak云平台。
- 仅3个鱼塘符合标准（pH 6.5-8.5，温度16-24℃，浊度<10 NTU等），其余不达标。
- 随机森林模型表现最佳，准确率达94.42%，适合渔业智能管理决策。
水产养殖需实时监控水质参数。本研究提出基于物联网的框架，利用传感器（pH、温度、浊度）与Arduino Uno微控制器采集鱼塘数据，并通过Thingspeak云平台存储为CSV文件。实验采集5个不同环境鱼塘的数据，与标准参考值对比后判断养殖适宜性。数据被标记为11类鱼类（包括Katla、prawn、tilapia等）。采用10种机器学习算法（J48、Random Forest、K-NN等）进行分析，结果显示仅3个鱼塘满足标准：pH（6.5–8.5）、温度（16–24）°C、浊度（<10）NTU、电导率（970–1825）μS/cm、水深（1–4）米。其中，随机森林模型性能最优，准确率94.42%，卡帕统计93.5%，平均真阳性率94.4%。此外，对一场景计算了BOD、COD、DO值。论文还详述了原型硬件设计。
原文摘要 · Abstract (English)
Aquaculture involves cultivating marine and freshwater organisms, with real-time monitoring of aquatic parameters being crucial in fish farming. This thesis proposes an IoT-based framework using sensors and Arduino for efficient monitoring and control of water quality. Different sensors including pH, temperature, and turbidity are placed in cultivating pond water and each of them is connected to a common microcontroller board built on an Arduino Uno. The sensors read the data from the water and store it as a CSV file in an IoT cloud named Thingspeak through the Arduino Microcontroller. In the experimental part, we collected data from 5 ponds with various sizes and environments. After getting the real-time data, we compared these with the standard reference values. As a result, we can make the decision about which ponds are satisfactory for cultivating fish and what is not. After that, we labeled the data with 11 fish categories including Katla, sing, prawn, rui, koi, pangas, tilapia, silvercarp, karpio, magur, and shrimp. In addition, the data were analyzed using 10 machine learning (ML) algorithms containing J48, Random Forest, K-NN, K*, LMT, REPTree, JRIP, PART, Decision Table, and Logit boost. After experimental evaluation, it was observed among 5 ponds, only three ponds were perfect for fish farming, where these 3 ponds only satisfied the standard reference values of pH (6.5-8.5), Temperature (16-24)oC, Turbidity (below 10)ntu, Conductivity (970-1825)μS/cm, and Depth (1-4) meter. Among the state-of-the-art machine learning algorithms, Random Forest achieved the highest score of performance metrics as accuracy 94.42%, kappa statistics 93.5%, and Avg. TP Rate 94.4%. In addition, we calculated the BOD, COD, and DO for one scenario. This study includes details of the proposed IoT system's prototype hardware.
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