Water Related

Sustainability and Productivity Enhancement IoT system for Agriculture

AP064

Minh-An DAO (The University of Melbourne, Victoria 3010 Australia)

Oct 23, 2021 623 views

Sustainability and Productivity Enhancement IoT system for Agriculture

The project vision is to create a modulo IoT network that could be easily modified and applied onto various types of farms and greenhouses to help optimize water use, give insights, and impact the health of crops and the surrounding environment. Hence, increase the quality of the product going to the market and reduce the wastes that will contribute to long-term sustainability.
An IoT network will include a master node made of the FPGA Cloud Connectivity Kit, which will be responsible for collecting data from the sensor nodes, communicating with the cloud along with controlling the actuators that will affect a specific crop on the field.
Each Sensor Node will have its own MCU connected with a Soil Moisture and pH Measurement sensor (CN0398), a Volatile Organic Compound (VOC) Detector (CN0395), the Light Recognition System (CN0397), and potentially any other sensors that the customer requests. The data collected from these sensors will be transmitted to the Master node through wired RS485, wireless LoRa, LoRaWAN or other local transmitting methods (depends on specific setup). The moisture data collected will be used to control a watering pump through an isolated Contactor. pH or other soil qualities data can be statistically displayed to give the user insight into what should be adjusted to the fertilizer. Moreover, the VOC and Light data can be used to conditioning airflow in the greenhouse and changing lighting components to provide the best environment for the crops to thrive.
The cloud-based service will be in contact with the master node to connect the user from anywhere around the world to monitoring and controlling their farm. Besides, the cloud is also responsible for updating the system's firmware Over-The-Air (OTA) and store the processed data from the FPGA for further analysis and future improvement.
If time permits, edge computing Machine Learning will also be applied to the master node to automatically control the watering, lighting and air ventilating based on the crop that is being monitored, hence reduce the network bandwidth, contribute to the long-term energy efficiency compared to applications that do most calculating on the cloud. This network can be scaled up with many Master Nodes, each responsible for specific crops in a greenhouse or a farm, connected all together and monitored by the cloud or by a local Grant Master Node. We are expected to test the system on a greenhouse facility in Vietnam after finishing the prototype, which will give us a better insight into how the system operates.
Through the project, besides expecting to learn a heap of new useful knowledge, our team hopes to create a foundation for improving the efficiency of watering and energy usage in agriculture, especially in developing countries such as our hometown Vietnam.

Project Proposal


1. High-level project introduction and performance expectation

The project vision is to create a modulo IoT network that could be easily modified and applied onto various types of farms and greenhouses to help optimize water use, give insights, and impact the health of crops and the surrounding environment. Hence, increase the quality of the product going to the market and reduce the wastes that will contribute to long-term sustainability.

An IoT network will include a master node made of the FPGA Cloud Connectivity Kit, which will be responsible for collecting data from the sensor nodes, communicating with the cloud along with controlling the actuators that will affect a specific crop on the field.

Each Sensor Node will have its own MCU connected with a temperature sensor, a soil moisture sensor, a Volatile Organic Compound (VOC) Detector (CN0395) and potentially any other sensors that the customer requests. The data collected from these sensors will be transmitted to the Master node through wired RS485, wireless LoRa, LoRaWAN or other local transmitting methods (depends on specific setup). All the sensors data can be statistically displayed on the webserver to give the user insight into what should be adjusted. The moisture data collected will be used to control a watering pump through an isolated relay (stimulating a contactor for high current pump). Moreover, the VOC data can be used to conditioning airflow in the greenhouse to provide the best environment for the crops to thrive.

The cloud-based service will be in contact with the master node to connect the user from anywhere around the world to monitoring and controlling their farm. Besides, the cloud could also responsible for updating the system's firmware Over-The-Air (OTA) and store the processed data from the FPGA for further analysis and future improvement.

If time permits, edge computing Machine Learning will also be applied to the master node to automatically control the watering, lighting and air ventilating based on the crop that is being monitored, hence reduce the network bandwidth, contribute to the long-term energy efficiency compared to applications that do most calculating on the cloud. This network can be scaled up with many Master Nodes, each responsible for specific crops in a greenhouse or a farm, connected all together and monitored by the cloud or by a local Grant Master Node. We are expected to test the system on a greenhouse facility in Vietnam after finishing the prototype, which will give us a better insight into how the system operates.

Through the project, besides expecting to learn a heap of new useful knowledge, our team hopes to create a foundation for improving the efficiency of watering and energy usage in agriculture, especially in developing countries such as our hometown Vietnam.

2. Block Diagram

3. Expected sustainability results, projected resource savings

Expected sustainability

- Improving the efficiency of watering and energy usage. Hence saving water and energy.

- Remote monitoring the greenhouse condition, lead to sufficient managing the resources.

 

Expected outcome:

- Learn a heap of new useful knowledge.

- All the sensor data can be monitored remotely.

- Actuators such as pump, fan,... can be controlled remotely.

- (If time permitted) Automatic watering and actuators controlling based on edge computing.

4. Design Introduction

5. Functional description and implementation

6. Performance metrics, performance to expectation

7. Sustainability results, resource savings achieved

8. Conclusion

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