Artificial Intelligence-Based Precision Agriculture and Crop Yield Optimization: The Mediating Role of Data-Driven Decision Making and Farmer Digital Literacy

Abstract

Artificial Intelligence (AI) is revolutionizing modern agriculture by enabling precision farming techniques that optimize crop yield, resource utilization, and environmental sustainability. AI-based precision agriculture systems leverage machine learning algorithms, predictive analytics, and remote sensing data to provide actionable insights for irrigation, fertilization, pest management, and harvesting. However, the effectiveness of these technologies depends not only on technological adoption but also on farmers’ ability to interpret and apply data-driven insights and their level of digital literacy. This study investigates the impact of AI-based precision agriculture on crop yield optimization, with a focus on the mediating roles of data-driven decision making and farmer digital literacy. Data-driven decision making refers to farmers’ capacity to interpret AI-generated recommendations and implement evidence-based interventions effectively. Digital literacy reflects farmers’ technical skills, familiarity with digital tools, and confidence in utilizing AI platforms for agricultural management. A quantitative research design was employed, targeting farmers, agronomists, and agricultural extension specialists in regions practicing precision agriculture. Structured questionnaires were used to collect data, which were analyzed using Smart PLS structural equation modeling to assess both direct effects of AI-based precision agriculture and the mediating effects of decision making and digital literacy. Results indicate that AI-based precision agriculture positively impacts crop yield optimization. Data-driven decision making and farmer digital literacy both mediate this relationship, emphasizing the importance of human capability in translating AI insights into practical actions. These findings highlight the necessity of combining technological innovation with capacity-building programs and digital skill development to maximize the benefits of AI-based precision agriculture. The study offers critical insights for policymakers, agricultural extension services, and technology developers seeking to promote sustainable and high-yield farming practices.