Scientific research project

Abstract

Vietnam, a developing country where agriculture remains vital to the economy, faces significant challenges due to the rapid advancement of information technology, increasing demands for higher quality agricultural products, land reduction from urbanization, and climate change impacts. These factors, coupled with a rising population and growing food demand, create substantial hurdles for agricultural production. Experts suggest that high-tech agriculture is an inevitable trend and a solution for advancing the country's agricultural sector. High-tech agriculture integrates advanced technologies into production processes to enhance efficiency, achieve breakthroughs in yield and quality, meet societal needs, and ensure sustainable development. This project successfully develops an IoT-based model for aquaculture management, utilizing IoT sensors for real-time monitoring via web or mobile interfaces. The model recommends optimal environmental conditions for various aquatic species and provides timely alerts through sensors, significantly enhancing management efficiency and reducing losses. Despite these accomplishments, the system's optimization is limited by the research timeframe, capabilities, and equipment constraints.

Introduction

Vietnam is a developing country where agriculture remains a key component of the economy. However, it faces significant challenges such as rapid advancements in information technology, increased international integration demanding higher quality agricultural products, reduced land due to urbanization, and climate change, all compounded by rising food demands from a growing population. Experts suggest that adopting high-tech agriculture is essential for addressing these challenges and advancing the agricultural sector. High-tech agriculture integrates advanced technologies into production processes to enhance efficiency, improve productivity and product quality, and ensure sustainable development.

In 2020, Vietnam's aquaculture sector covered 1.3 million hectares and utilized 10 million cubic meters of cages, including 7.5 million cubic meters for brackish and saline water and 2.5 million cubic meters for freshwater. The total production was 4.56 million tons, comprising 950,000 tons of shrimp (267,700 tons of black tiger shrimp, 632,300 tons of white-leg shrimp, and 50,000 tons of other shrimp) and 1.56 million tons of catfish. The country had 2,362 brackish water shrimp hatcheries, producing 79.3 million shrimp fry. In the Mekong Delta, there were about 120 parent catfish hatcheries and nearly 4,000 hectares of catfish fry nurseries, yielding around 2 billion catfish fry. Marine aquaculture involved 260,000 hectares and 7.5 million cubic meters of cages, producing 600,000 tons, including 38,000 tons of marine fish, 375,000 tons of mollusks, 2,100 tons of lobster, and 120,000 tons of seaweed. In Vinh Long, cage farming on the Tien River and Co Chien River included 1,717 cages, with a production of 128,500 tons in 2020.In 2020, Vietnam's aquaculture sector covered 1.3 million hectares and utilized 10 million cubic meters of cages, including 7.5 million cubic meters for brackish and saline water and 2.5 million cubic meters for freshwater. The total production was 4.56 million tons, comprising 950,000 tons of shrimp (267,700 tons of black tiger shrimp, 632,300 tons of white-leg shrimp, and 50,000 tons of other shrimp) and 1.56 million tons of catfish. The country had 2,362 brackish water shrimp hatcheries, producing 79.3 million shrimp fry. In the Mekong Delta, there were about 120 parent catfish hatcheries and nearly 4,000 hectares of catfish fry nurseries, yielding around 2 billion catfish fry. Marine aquaculture involved 260,000 hectares and 7.5 million cubic meters of cages, producing 600,000 tons, including 38,000 tons of marine fish, 375,000 tons of mollusks, 2,100 tons of lobster, and 120,000 tons of seaweed. In Vinh Long, cage farming on the Tien River and Co Chien River included 1,717 cages, with a production of 128,500 tons in 2020.

Proposed Method

The system will employ specific sensors to measure various parameters in the fish pond. It will process the data received from these sensors and compare it with the initial baseline data collected to optimize the pond's conditions. If the balancing is ineffective, the system will analyze which parameters are not within the desired range and provide recommendations for adjustments to the user. The diagram includes the following components:

- Sensors: Including temperature, TDS (Total Dissolved Solids), depth, turbidity, and pH sensors.

- Arduino: Receives requests from the ESP8266, reads data from the sensors, and sends the data back to the ESP8266.

- ESP8266 Modules: Each with different functions. One ESP8266 (left side) sends requests to the Arduino, receives data directly from it, uploads the data to the server, and compares the data to send notifications about the pond's status if needed. The other ESP8266 (right side) retrieves data from the server, addresses issues arising in the pond, and provides appropriate solutions.

- Server: Stores information about the pond, including parameters, status, solutions, etc.

- Pumps and Discharge Systems: Used to address simple issues in the pond.

Figure 1. System Workflow Diagram for Pond Management

Figure 2. Circuit diagram connecting ESP8266 and Arduino

Figure 3. Website Diagram

Results

Displaying Aquaculture Information: This section shows data from the sensors about the aquaculture system. It also displays data for different fish species to facilitate comparison for users. Additionally, the system will issue an alert if data is not received within the specified time limit (60 seconds).

Figure 4: Display of Aquaculture Information

Displaying All Aquaculture Parameters: Shows all parameters from the start of system operation.

Figure 5: Display of Aquaculture Parameters

Displaying Aquaculture Status Notifications: Provides notifications related to the condition of the aquaculture system, such as high temperatures, and indicates whether the issue has been resolved.

Figure 6: Display of Aquaculture Status Notifications

Displaying Aquaculture Activities: If there are unresolved notifications, the IoT system will address the issues and add activity notifications regarding the resolution of aquaculture problems.

Figure 7: Display of Aquaculture Activities

Conclusions

In this project, a model for aquaculture management using IoT technology has been successfully assembled. The aquaculture model employs IoT sensors to provide real-time monitoring through a website or mobile device interface. The project also proposes suitable environmental conditions for different types of aquatic species and provides timely alerts via sensors. The application of advanced technology significantly aids in efficient and timely management, improving aquaculture practices and reducing losses. However, despite these achievements, the system is not yet optimized due to limited research time, constrained capabilities, and inadequate equipment.

References