A influência da inteligência artificial na melhoria dos processos e tomada de decisões na cadeia produtiva agrícola brasileira

Abstract

This study investigates the influence of Artificial Intelligence (AI) on process improvement and decision-making across Brazil’s agricultural supply chain. Recognizing agribusiness as a major contributor to the national GDP and noting the sector’s rapid digitalization, the research examines how AI solutions, such as computer vision, machine learning, and prescriptive analytics, affect operational efficiency, productivity, and sustainability from planning through post-harvest logistics. A mixed-method approach combines a literature review and sector reports with multiple case studies of farms and cooperatives already deploying these technologies, alongside semi-structured interviews with managers and data-science specialists. Performance indicators, cost per hectare, predictive accuracy, input reduction, and return on investment, are analyzed to quantify impacts and uncover adoption barriers related to infrastructure, data governance, and workforce skills. By delivering actionable recommendations for producers, policymakers, and agtech firms, this applied research seeks to accelerate the digital transformation of Brazilian agriculture and enhance its sustainable competitiveness