Exploring new frontiers: Current status and future research directions for AIoT application in shrimp farming in the Vietnamese Mekong delta

• Published in: Aquacultural engineering Vol. 111; p. 102559
• Main Authors: Le, Tan Duy, Nguyen, Huynh Phuong Thanh, Nguyen, Minh Tu, Le, Ba Nhat Minh, Dang, Kim Khoi, Ha, Quang Phuc, Nguyen, Tan Viet Tuyen, Nguyen, Hong Quan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.10.2025
• Subjects: AIoT applications; artificial intelligence; automation; cost effectiveness; data analysis; disease detection; disease prevention; dissolved oxygen; domain; Environmental monitoring; farm management; food safety; humans; income; industry; Internet; IoT technology; labor; river deltas; shrimp; Shrimp Farming; surveys; Sustainability; temperature; traceability; Shrimp Farming; IoT technology; Environmental monitoring; AIoT applications; Sustainability
• ISSN: 0144-8609
• DOI: 10.1016/j.aquaeng.2025.102559

With the proliferation of Internet of Things (IoT) devices for data sensing, communication, collection, exchange, and, accordingly, a huge amount of data being generated, the emerging artificial intelligence (AI) stands out as an excellent tool to provide learning capabilities for those interconnected devices. Together with high-speed mobile networks and big data, the mixture of AI and IoT, namely Artificial Intelligence of Things (AIoT), enables data analytics to optimize and enhance the performance of IoT systems. AIoT can potentially transform many aspects of human activities, especially agriculture applications. Shrimp farming, an essential sector of the aquacultural industry that provides a significant source of income and food for many communities worldwide, is expected to benefit most from AIoT. It is noticed that traditional shrimp farming methods are often labor-intensive and environmentally damaging. By integrating AIoT into the shrimp farming process, significant improvements can be achieved across various domains, including monitoring, disease prevention, feeding optimization, and sustainability. This study aims to serve as a comprehensive literature survey and a fieldwork carried out in the Vietnamese Mekong Delta (VMD). We explore the promising application of AIoT, its drivers, and barriers in shrimp farming globally, and specifically in the VMD. Our findings indicated that although the adoption of AIoT in this domain is still limited, the IoT technology has been widely used for monitoring and managing shrimp farming systems. This includes tracking essential environmental parameters such as temperature, pH, dissolved oxygen, and gas emissions. Furthermore, automatic control systems have been implemented to ensure optimal shrimp growth and survival of the shrimps. Those results were verified through interviews with local authorities and shrimp farmers. Despite discrepancies in the perception and level of promising AIoT applications, efforts have been made by shrimp farmers to implement basic IoT systems for environmental monitoring and farm management towards optimizing farming time and lowering labour demand. However, the application of continuous environmental monitoring and reporting using AI technologies is still limited. Owing to the advantages of learning capability and data analytics, AI integration into IoT for shrimp farming can substantially enhance the efficiency, sustainability, and cost-effectiveness while lowering labour demand and environmental impacts. Further research is, therefore, necessary to reach the full potential of AIoT in other critical areas of shrimp farming, such as disease detection and prevention, as well as supporting traceability and food safety monitoring in the whole production chain.