Real-time dataset of pond water for fish farming using IoT devices

Md Monirul Islam

doi:10.1016/j.dib.2023.109761

Real-time dataset of pond water for fish farming using IoT devices

Data Brief. 2023 Nov 4:51:109761. doi: 10.1016/j.dib.2023.109761. eCollection 2023 Dec.

Author

Md Monirul Islam¹

Affiliation

¹ Department of Software Engineering, Daffodil International University, Daffodil Smart City (DSC), Birulia, Savar, Dhaka 1216, Bangladesh.

Abstract

This paper introduces a real-time water quality dataset of five ponds for fish farming obtained through an IoT framework for monitoring the aquatic environmental conditions. It utilizes sensors and an Arduino microcontroller to collect data on pH, temperature, and turbidity in pond water in Jamalpur District, Bangladesh. The data is stored in an IoT cloud platform named ThingSpeak and analyzed using 10 machine learning algorithms. The dataset consists of 4 columns and 40,280 rows, where pH, temperature, turbidity, and fish are recorded. Fish represents the target variable, while the others serve as independent variables. Within the dataset, there are 11 distinct fish categories including sing, silver carp, Katla, prawn, karpio, shrimp, rui, pangas, tilapia, magur, and koi. Results showed that only three ponds are suitable for fish farming among five ponds and the Random Forest algorithm performs the best. The study also includes details of the IoT system's hardware. This dataset will be useful for researchers and fish farmers to predict fish survival.

Keywords: Aquatic biology; IoT sensor; Smart fish farming.