Technological Innovation in Artisanal Agriculture: Comparison of Recommendation Approaches to Optimize Marketing

Abstract

Technological innovation is transforming the way artisanal agriculture is produced and marketed, particularly in rural areas where resources and access to information are often limited. In this context, recommendation systems based on machine learning have emerged as an effective alternative to improve agricultural marketing by providing personalized suggestions that help producers make more informed decisions. These tools make it possible to optimize processes, reduce reliance on intermediaries, and better adapt to the real needs of farmers.

Organizations such as the FAO have emphasized that the use of technology in agriculture can be key to bridging market access gaps and strengthening rural economies. This article compares three different approaches to recommendation systems applied to artisanal agriculture: one based on neural networks and cloud computing, and two hybrid approaches that integrate collaborative filtering, customer segmentation, and implicit data analysis. The analysis shows that hybrid models, due to their flexibility and accuracy, are better suited for contexts with limited technological infrastructure, effectively contributing to process optimization in agricultural environments.