An IoT model for Fish breeding analysis with water quality data of pond using Modified Multilayer Perceptron model

Abstract

The fishing and aquaculture industries are significant food production areas, providing nutritional security, livelihood assistance, and meaningful employment to more than 14 million people. They also contribute to agricultural export by generating income. Aquatic creatures have a defined tolerance range for various environmental conditions in their habitat. When cultivating marine organisms, the quality of the water is essential. One of the critical characteristics of aquaculture is breeding. Aquaculture farmers will get better breeding results if they maintain good water quality. Dissolved oxygen (DO) and temperature in the water plays a vital role in optimizing fish breeding. Proper maintenance of DO and temperature levels in water is a crucial task Previously, the DO and temperature, where collected manually and tested in the laboratory. But this mode of testing is not effective and non-scalable. In this paper, we use the data collected through sensors and analyze these data using the Modified Multi-layer Perceptron (MMLP) model to maintain proper DO and Temperature levels in the pond for better breeding conditions. The proposed model measures the DO and temperature for fish breeding better than existing models; the experimental results show that the proposed model is better than state-of-art models.

Topics & Concepts