An integrated framework of sensing, machine learning, and augmented reality for aquaculture prawn farm management

Abstract

The rapid growth of prawn farming on an international scale will play an important role in meeting the protein requirements of an expanding global population. Efficient management of the commercial ponds for healthy production of prawns is the key mantra of success in this industry. It is a necessity to maintain the water quality parameters in these ponds within specific ranges to create an ideal environment of optimal growth of healthy prawns. The current practice of water quality data collection and their usage for decision making on most farms is not efficient and does not take full advantage of the latest technologies. The research presented in this paper aimed at addressing this problem by systematic investigation and development of an integrated framework where (i) modern sensors were investigated for their suitability and deployed for continuous monitoring of the water quality variables in prawn ponds; (ii) novel machine learning models were investigated based on collected data and deployed to accurately forecast pond status over next 24 h. This provides farmers insight into upcoming situations and take necessary measures to avoid catastrophic situations; and (iii) augmented reality-based visualisation methods were investigated for improved data capture process and efficient decision making through real-time interactive interfaces. The paper presents the integrated framework as well as the details of sensing, machine learning, and augmented reality components. We found that (i) YSI EXO2 Multi-Sonde is the best sensor for continuous monitoring of prawn ponds; (ii) ForecastNet (our developed machine learning model) provides best forecasting results with symmetric mean absolute percentage error of 6.1 %, 9.6 %, and 8.5 % for dissolved oxygen, pH, and temperature; and (iii) augmented reality-based interactive interface achieves accuracy as high as 89.2 % for management decisions with at least 41 % less time. The experience of the project as presented in this paper can act as a guide for researchers as well as prawn farmers to take advantage of latest sensors, machine learning algorithms and augmented reality tools.