Learning Model Based on Artificial Intelligence to Determine Wood Quality: A Systematic Review

Quinteros-Navarro D.; Quinteros A.; Muñoz V.; Ramirez H.; Ramirez W. · Ingenierie des Systemes d'Information (2025) · DOI: 10.18280/isi.300214

Resumen

Prediction models that are aimed at identifying patterns, attributes and conditions allow identifying quality aspects to generate results with better forecasts for the decision of a strategic framework. A review procedure was considered based on different bibliographic resources such as contribution articles and systematic literature reviews to know the state of the art on different studies carried out with prediction models that managed to obtain more precise and reliable results for the determination of wood quality. Likewise, the analysis of various contributions was carried out through a systematic process where models such as automatic, deep and alternative learning were considered. Three (39) research questions and essential activities for the treatment were considered: model identification, calculation of metrics and determination of factors. The metrics were considered with the development of two (2) formulas: the average precision (APREC) and the average accuracy (AAUC), where the results obtained from the various algorithms of the models under study were used. The review consists of: Introduction, method, related works, evaluation and analysis, discussion of results, conclusions and recommendations.

Instituciones: Universidad Nacional Mayor de San Marcos; Universidad Nacional de San Martín