Resumen

CULQUE TOAPANTA, Walter Vinicio; VISCAINO NARANJO, Fausto Alberto  y  LLERENA OCANA, Luis Antonio. Intelligent system for optimizing and classifying agricultural producers using machine learning algorithms. RCCI [online]. 2025, vol.19, n.2  Epub 25-Sep-2025. ISSN 2227-1899.

An intelligent system for optimizing and classifying agricultural producers was designed and implemented using machine learning algorithms on a web-based platform for the Municipal Autonomous Government of Santiago de Pillaro. The research aimed to develop a digital tool that facilitates the efficient management of producer information, improving decision-making and promoting competitive advantages. The intervention consisted of implementing the system in a case study with 35 agricultural producers in the canton over a 12-week period. The algorithmic model was validated using statistical analyses such as the ANOVA test, which allowed for the evaluation of differences in classifications before and after implementation, and performance metrics such as precision, recall, and F1-score, to determine the effectiveness of the classification algorithms. The results show that the system improved producer classification accuracy and facilitated decision-making, promoting more efficient management. The conclusions indicate that the system is an effective tool that can enhance the canton's agricultural management by optimizing resources and administrative processes. Furthermore, its integration into other areas responsible for agricultural management and local sustainability is recommended to expand its reach and benefits.

Palabras clave : machine learning; classification; agricultural management; web platform; optimization.

        · resumen en Español     · texto en Español     · Español ( pdf )