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Review Articles
Vol. 11 No. sp2 (2024): International conference on Multidisciplinary Approaches to SDGs: Life Sciences Perception
Implementation and adoption of smart technologies in agri-allied sectors
Department of Agricultural Extension Education, M S Swaminathan School of Agriculture, Centurion University of Technology & Management, Paralakhemundi 761 211, India
Department of Agricultural Extension Education, M S Swaminathan School of Agriculture, Centurion University of Technology & Management, Paralakhemundi 761 211, India
Department of Agricultural Extension Education, M S Swaminathan School of Agriculture, Centurion University of Technology & Management, Paralakhemundi 761 211, India
Department of Agricultural Extension Education, M S Swaminathan School of Agriculture, Centurion University of Technology & Management, Paralakhemundi 761 211, India
Abstract
Together with livestock, horticulture and fishing, India's agriculture industry has successfully met government production targets and broken records in nearly every commodity category. Unfortunately, these industries have accomplished the targets at the expense of the deterioration of natural resources and adverse impact on the environment. Besides, being a cornerstone of global sustenance, these industries face multifaceted challenges ranging from resource scarcity to climate variability. The inclusion of smart farming combined with drones, artificial intelligence, robotics, robotic agricultural bots, cloud computing, wireless sensor networks, expert systems and the Internet of Things (IoT) to bring an effective change can be a better alternative. Integrating these technologies into farming facilitates improved managerial decision-making for all the stakeholders, resulting in increased yield. The benefits of AI adoption are multifaceted, encompassing heightened efficiency, reduced environmental impact and improved crop quality. Furthermore, the burgeoning agriculture- tech sector has the potential to stimulate economic growth and job creation. Looking ahead, emerging trends in robotics, machine learning and the IoT signify a dynamic future for AI in agriculture, heralding a transformative era for the industry. The study utilizes Systematic Literature Review (SLR) method, guided by the PRISMA technique, to develop a conceptual framework. A total of 28 documents published between 2010 and 2024 are included in the analysis. This paper aims to explore the various AI trends in these sectors, while thoroughly analysing the role of these techniques, as well as their challenges and prospects.
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