Abstract
The agriculture sector not only contributes to the nation's economy but also
serves as an important element in foreign exchange and trade markets. With the
advancement in technology, robots, drones, satellite imagining, IoT, wireless sensor
networks, machine learning, big data analytics, and unmanned aerial vehicles (UAV)
are being deployed to manage, monitor and control agricultural chores. However, the
farmers are unable to meet the increasing urban food demand with limited cultivable
land availability. Thus, to solve this issue, hydroponic farming is opted for in several
parts of the world. It is a soil-free and nutrient-rich water medium for agriculture,
which is increasingly opted for by the urban population. Hydroponic farming has been
vastly explored in the context of urban farming, where land, water, time, and labour are
required in a limited amount, yet productivity is far better compared to traditional
agricultural methods.
It has been recently adopted in urban sections in India due to restricted movement in
COVID-19 pandemic situations to fulfil basic food requirements. However, hydroponic
farming has shortcomings such as higher initial cost, the possibility of complex nutrient
discharge problems, the energy requirement for the creation of microclimatic
conditions, fertigation and effluent treatment and pretrained skilled labour. In order to
resolve these issues, a smart hydroponic farming architecture is discussed, which
reduces human intervention and water wastage using wireless sensor networks and IoT.
In order to successfully and efficiently implement the agricultural supply chain,
machine learning algorithms and data mining techniques are utilized from the
production to inventory storage stage. The following sections deal with a brief
introduction to hydroponic farming, its architecture and components, and future
opportunities regarding the field of automated hydroponic farming.