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.

国际范围内对虾养殖的快速增长将在满足不断增长的全球人口对蛋白质的需求方面发挥重要作用。有效管理商业池塘以健康生产对虾是该行业成功的关键。必须将这些池塘中的水质参数保持在特定范围内，以创造健康对虾最佳生长的理想环境。目前大多数农场的水质数据收集及其用于决策的做法效率不高，也没有充分利用最新技术。本文中提出的研究旨在通过系统调查和开发一个集成框架来解决这个问题，其中 (i) 现代传感器对其适用性进行了调查，并部署用于对虾塘中水质变量的连续监测；(ii)根据收集到的数据研究新的机器学习模型，并部署以准确预测未来 24 小时内的池塘状况。这使农民能够洞察即将发生的情况并采取必要措施避免灾难性情况；(iii)增强现实——研究了基于可视化方法的可视化方法，以通过实时交互界面改进数据捕获过程和有效决策。本文介绍了集成框架以及传感、机器学习和增强现实组件的细节。我们发现 (i) YSI EXO2 Multi-Sonde 是对虾池连续监测的最佳传感器；(ii) ForecastNet（我们开发的机器学习模型）提供最佳预测结果，溶解氧、pH 值和温度的对称平均绝对百分比误差分别为 6.1%、9.6% 和 8.5%；(iii) 基于增强现实的交互界面实现了高达 89.2% 的管理决策准确率，而时间至少减少了 41%。