Aquaculture, which is the breeding of fishes in artificial ponds, seems to be gaining popularity among urban and suburban dwellers in Sub-Saharan Africa and Asia. Tenant aquaculture enables individuals, irrespective of their profession, to grow fish locally in a little space. However, there are challenges facing aquaculture, such as the availability of water, how to monitor and manage water quality, and, more seriously, the problem of the absence of a dataset with which the farmer can use as a guide for fish breeding.

Aquaponics uses these two technologies in a symbiotic combination in which the plant uses the waste from the fish as food while at the same time filtering the water for immediate re-use by the fish. This helps to solve the problem of frequent water changes. An Internet of Things (IoT) system consisting of an ESP-32 microcontroller that controls water quality sensors in aquaponics fish ponds was designed and developed for automatic data collection. The sensors include temperature, pH, dissolved oxygen, turbidity, ammonia, and nitrate sensors.

The IoT system reads water quality data and uploads the same to the cloud in real time. The dataset is visualized in the cloud and downloaded for the purposes of data analytics and decision-making. The research team presents the dataset in this paper. The dataset will be very useful to the agriculture, aquaculture, data science, and machine learning communities. The insights such dataset will provide when subjected to machine learning and data analytics will be very useful to fish farmers, informing them when to change the pond water, what stocking density to apply, provide knowledge about feed conversion ratios, and predict the growth rate and patterns of their fishes.

Read the complete research at

Udanor, Collins & Ossai, N.I. & Nweke, Onyinye & Ogbuokiri, Blessing & Eneh, H. & Ugwuishiwu, Chikodili & Aneke, Stephen & Ezuwgu, A.O. & Ugwoke, P.O. & Christiana, Arua. (2022). An internet of things labeled dataset for aquaponics fish pond water quality monitoring system. Data in Brief. 108400. 10.1016/j.dib.2022.108400.