Transforming Agribusiness: The Role of Artificial Intelligence in Quality Control - A Systematic Review (#1122)

Authors

Escajadillo Gamboa, Yessenia

Santiago Calderón, Luis

Cuba Cornejo, Carmen

Gimenez López, Bruno

Blanco Contreras, Carlos

Ronceros Morales, Cristhian

Abstract

The objective of the present Systematic Literature Review (SLR) is to analyze the impact and applications of Artificial Intelligence (AI) in the agro-export sector. This review examines how AI has evolved in recent years, highlighting its potential to integrate multiple technologies in strategic decision-making and improve the competitiveness and sustainability of the sector. The methodology used focused on the Scopus database, where 240 articles were identified. After applying inclusion and exclusion criteria, 53 relevant studies were selected for analysis. AI-driven automation not only increases operational efficiency, but also contributes to a safer and more satisfying work environment by reducing the burden of repetitive and error-prone tasks. However, for the successful implementation of these technologies, a holistic approach that considers strategic, technical, and collaborative factors is necessary. Interdisciplinary cooperation between scientists, engineers and industry professionals is essential to develop solutions tailored to the specific needs of the agro-industrial industry. The conclusions of this research highlight that AI offers concrete solutions to address the challenges in the quality of agro-export products, guaranteeing food safety, meeting consumer demands and reducing food waste. Technologies such as deep learning and neural networks are standardizing production processes and adding value to the supply chain. In addition, automation not only improves operational efficiency, but also enhances the productivity of human capital and promotes more sustainable work environments