Bioscience Evidence 2026, Vol.16, No.4, 264-276 http://bioscipublisher.com/index.php/be 273 aquaculture. A survey of 624 small-scale farms in Myanmar found that farmers using diversified production methods, such as polyculture and pond-edge planting, generally achieved higher production, economic benefits, and phosphorus utilization efficiency (Wang et al., 2022). These approaches do not require large investments in equipment but can reduce water quality fluctuations by improving ecological cycling capacity. Based on ecological regulation, digital monitoring technologies are becoming important tools for improving management efficiency in family prawn farms. Pond monitoring systems based on NB-IoT technology can remotely collect indicators such as temperature, pH, and dissolved oxygen and provide auxiliary control for aeration equipment. In practical tests, the system achieved temperature control accuracy of ±0.12 ℃, dissolved oxygen control accuracy of ±0.55 mg/L, and pH control accuracy of ±0.09, indicating that low-cost sensor technologies are already suitable for aquaculture applications (Huan et al., 2020). For M. rosenbergii nursery production, an automatic monitoring system based on ESP32, MQTT, and Node-RED can continuously record temperature, pH, TDS, and turbidity parameters and improve data stability through data processing and sensor error reduction (Ramli, 2024). Future digital upgrading of prawn farms does not necessarily require large intelligent aquaculture platforms but can gradually develop from simple functions such as mobile water quality monitoring, abnormal condition alerts, and automatic aeration control. 6.3 Intelligent, ecological, and climate-resilient aquaculture models The future development direction of M. rosenbergii aquaculture will gradually shift from traditional “scheduled management” toward continuous sensing, risk prediction, and precise control. With the development of the Internet of Things (IoT), artificial intelligence, and machine learning technologies, water quality management is changing from post-event treatment to early warning. Data analysis systems based on IoT and machine learning can continuously collect environmental data such as temperature, pH, and dissolved oxygen, predict water quality trends, and allow early actions such as aeration adjustment, feeding regulation, or environmental improvement. An intelligent M. rosenbergii farming system in Bangladesh combined sensors, cloud platforms, and machine learning models to achieve real-time water quality monitoring and production prediction. The regression model achieved a coefficient of determination (r²) of 0.94, while the random forest model achieved 97.84% accuracy in production grade classification (Ahmed et al., 2024). An intelligent management system developed in the Pak Phanang region of Thailand combined dissolved oxygen, pH, and temperature monitoring with automatic control devices, achieving a 93.3% survival rate and marketable size within a 120-day culture period (Songpayome et al., 2024). Intelligent technologies have gradually moved from experimental research into practical aquaculture applications. In addition to digital management, sustainable M. rosenbergii production in the future will require further development of ecological ponds and climate-resilient farming models. Systems such as biofloc technology (BFT), periphyton systems, and integrated multi-trophic aquaculture (IMTA) can improve system stability and reduce pollution discharge by promoting nutrient utilization among microorganisms, plants, and filter-feeding organisms. Biofloc systems are more suitable for nursery stages, while periphyton-based and ecological polyculture systems are more suitable for grow-out stages. The combination of different ecological technologies may become an important direction for low-emission aquaculture in the future (Halim et al., 2026). At the same time, climate change-related challenges, including high temperatures, extreme rainfall, and increased disease occurrence, require aquaculture systems to have stronger recovery capacity. Rising temperatures can affect growth, molting, immunity, and survival of crustaceans and increase disease risks (Daunde et al., 2025). Future M. rosenbergii industries need to integrate intelligent monitoring, ecological regulation, and climate adaptation strategies to improve the self-regulation capacity of ponds and achieve more stable and sustainable production. Author Contributions The author would like to thank the anonymous reviewers for their detailed review of the draft. Conflict of Interest Disclosure The author affirms that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.
RkJQdWJsaXNoZXIy MjQ4ODYzNA==