Optimizing Agriculture Water Usage using AI Driven Sensors

The code repository for the project can be found here.

Wireless sensor networks constitute a distributed node system composed of limited capability sensors that are responsible for monitoring their immediate environment and transmit the parameters thus collected in real-time. This allows a deeper study of phenomena that remained obscure henceforth. It also allows the environment actuators to act per the microscopic information being collected to optimize the throughput of the application at hand. In recent years, a lot of novel research has been conducted in the context of this industry both because of the vast scope of improvement and the ensuing applications. Optimal WSNs can find applications in a vast multitude of industries such as healthcare, military, supply chain, etc.

One such industry where WSNs have been used to create both economic and positive environmental impact by optimization is the agriculture industry. Agriculture forms the backbone of India’s GDP employing nearly 70% of the country’s population. With the ever-increasing population, the demand for food resources increases which warrants a rise in cultivated produce levels. This is perhaps one of the primary reasons that increasing the productivity of existing land resources in the country is of utmost priority. What is more, is that the development must sustainably take place to not overwhelm the land resources keeping in mind the ongoing ecological world debate.

This is where WSNs can step in as has been seen in recent times, thanks to relentless research leading to innovative solutions in this field. In agriculture, sensor networks are used primarily to improve the quality and production quantity of the crop. Typically, thousands of tiny sensors are deployed in crop fields capable of sensing biochemical parameters such as humidity levels, carbon dioxide levels, nitric oxide levels, etc. This information when sent over to a processing unit helps gain insight into the unique biochemical disposition of the field in consideration which is otherwise not possible in traditional farming settings.

In this project, we focus on one specific parameter namely the humidity levels to optimize the field water usage to maximize irrigation water conservation. In recent times, conservation of water resources and more specifically the overuse of water in agriculture has become a hot topic of discussion with a call for innovative methodologies to eradicate this problem. In our project, we systematically study some previously proposed WSN transmission optimization protocols to select the optimal solution. We then apply the superior of the studied protocols to a field simulation to study the effectiveness in the conservation of field water resources. The results of our study are optimistic and indicate that by using smart irrigation solutions such as the one utilized in this article, the water consumption in agriculture can be cut down many notches.