Automation and AI in US Agriculture: Revolutionising Farming through Smart Technologies and Farm Management Software

Automation and AI in US Agriculture: Revolutionising Farming through Smart Technologies and Farm Management Software

Muhammed, Usman1 and Adebayo, Adedeji2
1Department of Agricultural Engineering, Federal University of Technology Akure
2Mechanical Engineering Department, University of Ibadan

Abstract

The US agricultural sector is undergoing a significant transformation driven by automation, artificial intelligence (AI), and smart technologies. These advancements fundamentally reshape traditional farming practices, enhance efficiency, and promote sustainability across various agricultural domains. This paper explores the integration of AI-driven technologies such as drones, sensors, farm management software, and robotics, which have revolutionised crop and livestock management by enabling real-time data acquisition, precision farming, and automated processes. Despite the numerous opportunities offered by these technologies, the agricultural industry faces significant challenges related to high implementation costs, technological accessibility, and the need for tailored training programs. Furthermore, the economic implications of adopting these technologies, including potential labour displacement and the necessity for new skill sets, are addressed. Through an analysis of current practices, this paper highlights the potential of AI and automation to improve productivity and environmental conservation while considering the social and economic hurdles that must be overcome to achieve widespread adoption.

Keywords Artificial Intelligence; Precision Farming; Smart Farming; Farm Management Software; Revolutionising Farming; Livestock Monitoring; US Agriculture; Smart Technologies

Citation Muhammed, U. & Adebayo, A. (2024). Automation and AI in US Agriculture: Revolutionising Farming through Smart Technologies and Farm Management Software. American Journal of Applied Sciences and Engineering, 5(3) 45-59.  https://doi.org/10.5281/zenodo.13995208  
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