Precision Agriculture with Single Board Computers

Precision agriculture uses technology and automation to optimize crop production, reduce waste, and promote sustainability. It enables farms to make informed decisions based on real-time data and historical trends, maximizing crop quality while minimizing resource usage and environmental impact. As technological advances, the precision agriculture scope expands. The integration of SBCs enables the management of various sensors and devices critical to precision farming, but also makes it possible to handle data processing for smart agriculture and farming automation.

This article explores how SBCs can be used to improve precision agriculture applications, integrating automation and autonomous operational processes.

Benefits and Challenges of SBCs in Precision Agriculture

Due to their flexibility and processing capabilities, SBCs can provide several aspects of precision agriculture applications:

• Increased Efficiency: SBCs optimize environmental conditions, enabling the integration of IoT applications and AI for efficient farming.

• Control and Automation: SBCs can manage various aspects of indoor farming systems, from climate control to nutrient management.

• Cost-effectiveness: SBCs are cost-effective, with compact sizes and low power consumption making them increasingly popular for battery-based applications.

However, depending on the the location and application, concerns may rise related to communication and data security:

• Real-time Challenges: Real-time farming relies on reliable networks and low latency, which can be challenging to achieve in remote rural areas [3].

• Security Concerns: Protecting agricultural data and automated systems from cyber threats is a growing concern, especially as the industry becomes more connected.

SBCs in Action

Data collection

Data play a central role in decision-making in precision agriculture. Sensors are placed strategically in fields and on equipment to monitor parameters such as temperature, humidity, soil moisture, and nutrient levels. The table below lists sensors that can be used in precision agriculture applications.

Table 1: Sensors and their application in farming.

However, acquiring the data is only part of the process. SBCs act as the central control unit for these sensors, enabling real-time data collection and analysis. Thus, there is the possibility of processing the data locally or performing the transmission to a cloud-based platform. As new SBCs provide more processing power and are ready to execute machine learning, farmers rely less on high-speed data transmission to analyze data in real-time by cloud solutions. As a result, farmers can quickly respond to changing crop conditions, making precise adjustments to farming processes, such as irrigation.

Agricultural systems automation

SBCs can also play a crucial role in automating processes in precision agriculture. By enabling autonomous vehicles and robots, SBCs allow farmers to equip them with sensors and cameras that collect data on crop growth and environmental conditions. For instance, autonomous tractors can be used to plant and harvest crops. At the same time, drones can monitor crop health, detect pests and diseases, and even spray pesticides on the affected areas. In addition, SBCs can also automate irrigation systems, fertilizer application, and other aspects of crop management, reducing the manual labor need and improving efficiency [3,4,5].

These automation applications are economically viable for medium and large farms, and even small farms can benefit from automation. Thanks to the computing power of SBCs, a single SBC can execute several tasks, making it possible for small farmers to take advantage of automation benefits. Automating fertilizer and irrigation management reduces costs and errors, resulting in timely actions and minimal crop losses, for example.

Agricultural data analysis

SBCs run on operating systems like Linux, providing access to a vast library of advanced open-source software. They can handle substantial volumes of data from multiple sensors in real time, supporting precision agriculture decision process. The data can be analyzed locally using big data analytics and machine learning algorithms, running the software analysis using the SBC's processing power. For example, machine learning algorithms can be used to analyze soil data and recommend the optimal amount of fertilizer to apply to each crop, or to predict the likelihood of a pest outbreak based on weather patterns and other environmental factors. As a result, advanced data analysis applications can run locally, providing farmers with insights.

Use case: SBCs for Irrigation

Water is a crucial component of agriculture and is normally the first aspect approached by agriculture precision. In this section, you will find a case study using SBCs in an irrigation system.

Setting up IoT sensors

First, it is necessary to set up the sensors and to cover broad crop field areas, it is required to use IoT devices with wireless transmission. One option is to use sensors like the DHT11 for temperature and humidity and FC-28 for soil moisture. They can be connected to ESP8266 boards to transfer the acquired data, which use Wi-Fi and MQTT to transfer the data.

Using SBCs for data management and automation

Besides acquiring the data, it is necessary to have a server to receive and process it. SBC models like the ROCK 4 SE serve as data hubs. These SBC models are robust and versatile, featuring a high-performance Hexa-core Rockchip RK3399-T processor and 4GB 64bit LPDDR4 RAM. Furthermore, it is equipped with a Dual Arm Cortex-A72 CPU, Quad-core Cortex A53, and Arm Mali T860MP4 GPU. In addition, you can also create user-friendly interfaces for real-time monitoring using tools like NodeRed and InfluxDB.

The system can go beyond data collection and monitoring. The ROCK 4 SE's processing capabilities can automate irrigation based on the received data. For instance, if some regions of the field show soil moisture levels below a specified threshold, the system can trigger the irrigation pumps only for those zones if this possibility exists. As a result, energy and water usage would be optimized.

Future enhancements

To further increase the project capabilities, you could integrate it into the cloud and add machine learning algorithms. The cloud integration would facilitate the system management access from anywhere. While the machine learning algorithms could improve the water and energy used based on the historical data. 

Future Outlook in SBC-Powered Precision Agriculture

The future of precision agriculture supported by SBCs is promising, with many innovations on the horizon. As SBCs increase the capability of handling artificial intelligence and machine learning algorithms, more advanced and complex data analysis can be made, providing farmers with increasingly precise insights into crop management. Regarding robotics and automation, new machines are expected to replace work in repetitive farming tasks, and drones equipped with SBCs are expected to provide more accurate crop monitoring, improving pest detection and disease prevention. 

Besides the conventional crop farms, SBCs can increase process automation and optimization of vertical farming, helpint it to gain more traction, controlling indoor luminosity, for example. Moreover, combining new IoT and sensor devices with SBC management can improve livestock farming, optimizing feed consumption and reducing disease incidence by closely monitoring each animal and requiring treatment as soon as an anomaly is detected.

Conclusion

SBCs are essential supporting devices of modern precision agriculture. Their ability to process and analyze data in real-time and their role in automation make them indispensable tools for farmers striving for sustainable and efficient crop production. As the agricultural sector evolves, SBCs will gain traction, boosting new precision farming applications.

The ROCK series powering precision Agriculture

References

[1] McFadden, Jonathan, Eric Njuki, and Terry Griffin. "Precision Agriculture in the Digital Era: Recent Adoption on US Farms." (2023).

[2] Tycoonstory. “What is IoT Precision Agriculture and How is it Different from Traditional Agriculture?“

[3] AgriTech Tomorrow “5 Challenges For Precision Agriculture To Face”. 2020

[4] Abu, N. S., et al. "Internet of things applications in precision agriculture: A review." Journal of Robotics and Control (JRC) 3.3 (2022): 338-347.

[5] Raj, E.F.I., Appadurai, M., Athiappan, K. (2021). Precision Farming in Modern Agriculture. In: Choudhury, A., Biswas, A., Singh, T.P., Ghosh, S.K. (eds) Smart Agriculture Automation Using Advanced Technologies. Transactions on Computer Systems and Networks. Springer, Singapore. 

[6] Mathe, Sudha Ellison, et al. "A survey of agriculture applications utilizing raspberry pi." 2022 International Conference on Innovative Trends in Information Technology (ICITIIT). IEEE, 2022.

[7] Gsangaya, K. R., et al. "Portable, wireless, and effective internet of things-based sensors for precision agriculture." International Journal of Environmental Science and Technology 17 (2020): 3901-3916.

[8]  Sebastian. “IOT Sensor Project: ESP8266 with DHT11 Sensor sends Data to MQTT”.

[9] Jia, Weibing, and Zhengying Wei. "Raspberry Pi-Embedded Intelligent Control System for Irrigation and Fertilization Based on deep learning." Journal of Physics: Conference Series. Vol. 2504. No. 1. IOP Publishing, 2023.

[10] askSensors. “How to Connect Soil Moisture Sensor and ESP8266 to the AskSensors IoT Cloud”.