Factors Impacting Intentions in Adopting Artificial Intelligence Based Solutions in Agriculture An Indian Context

Abstract

Earth is now a habitat of eight billion human beings who depend on the limited resources available on the planet to survive. The increasing population is constantly exerting pressure on present agricultural production systems and demands for increased production to ensure food security across the globe. Digital technologies including Artificial Intelligence (AI) enable the farmers and facilitators for making better decisions during crop lifecycle management which in turn leads to lesser damages and increased productivity. Through the use of machine learning algorithms, AI systems can analyze vast amounts of data, including weather patterns, soil conditions, and crop characteristics, to provide valuable insights for farmers. This enables optimized resource allocation, precise irrigation and fertilization techniques, and timely pest detection and control, ultimately increasing crop yields while reducing costs and environmental impact. Despite the large number of perceived benefits and government plans, the adoption level of AI based solutions in agriculture is quite low. As a step towards bridging the gap between the present situation of agricultural production and a target of zero hunger identified as sustainable development goal (SDG) by the United Nations, this study empirically evaluates the determinants that influence adoption of AI-based solutions in agriculture. To understand and evaluate the perspectives of farmers (end-users of the solution) and facilitators (enablers in the agricultural system) involved in the diffusion of new agricultural technologies, this study uses an integrated framework built on three eminent theories from Information Systems, namely Unified Theory of Acceptance and Use of Technology (UTAUT), Diffusion of Innovation (DoI) and Technology-Organization-Environment Framework (TOE Framework). Using survey data of farmers and facilitators from two states in Northern India, this study examines the interaction of independent variables and validates the proposed framework using Structural