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.