Sustainable Efficient Solutions for Smart Agriculture

Abstract

India is a country that is strongly reliant on agriculture, with about half of the population relying on it for their living. However, the majority of agricultural techniques are not for profit and do not produce favourable results. It should upgrade with modern technology to improve seed quality, evaluate soil fertility, monitor water levels, estimate market prices, and improve sensitivity to defects and background information in agriculture. In agricultural production, decision-making, crop selection, and the establishment of a supporting system to increase crop output are all significant challenges. Weather, soil fertility, water level, water quality, seasons, commodity prices, and other natural factors influence agriculture forecasting. Growing agricultural automation tendencies have resulted in a substantial amount of software and app development that allows for the rapid capture of essential data. Mobile devices are increasingly being used by everyone, even farmers. Farmers rely heavily on information and communication technology (ICT) in their daily life, particularly when it comes to accessing and disseminating agricultural information. Research and innovation advancements are perceived as vital factors in their financial success and agricultural production expansion. These improvements are made possible by the Internet of Things (IoT) and Big Data Analytics (BDA). These technologies assist farmers in addressing concerns such as resource optimisation, agricultural land monitoring, decision-making support, and understanding of crop, land, weather, and market circumstances. The integration of IoT and BDA plays a vital role in offering energy efficient solutions in smart applications especially in farming. This chapter proposes a Smart Agricultural framework based on the integration of IoT and BDA (IoT-BDA), which also promotes the farming sector’s sustainability. Data from sensors, data from cloud platform storage, and data from databases are all used in smart agriculture, and all three concepts must be integrated. The data collected from various sensors and kept in a cloud based storage, which is then analysed using adequate analytics techniques and the necessary information is then delivered to a user interface, which naturally supported the findings. It gives farmers information on the ground water level, climatic conditions, and the typical seasonal crops that grow in the area, as well as soil and water nutrient levels, so they can decide which crop to put on their farms. It is used to increase crop output while keeping the cost of agricultural items under control. It also assists in the analysis of commodity pricing and the determination of the best price. In terms of overall performance, the suggested approaches outperform the already used tactics, according to the findings of the experiments.