Efectividad de modelos de Machine Learning para el Emprendimiento, la Innovación y Predicción del Crecimiento de Cultivos. Una Revision Sistemática de Literatura (#238)

Authors

Clemente Avila, Josue Israel

Espejo Castillo, María de los Angeles

Dios-Castillo, Christian Abraham

Abstract

In recent years, the application of Machine Learning (ML) in agriculture has emerged as a crucial innovation to improve productivity. This systematic literature review (SLR) aims to determine the effectiveness of Machine Learning models in quantitative crop analysis. Employing PICO methodology and adhering to PRISMA standards, research articles published from 2015 to 2024 were meticulously analyzed. The findings rescued from the Scopus source, highlight that the most commonly used techniques are Deep Learning, Random Forest and Support Vector Machine (SVM). Due to their high accuracy and ability to handle large datasets. The review also discusses challenges in data quality and model implementation, emphasizing the need for continued research and international collaboration to advance agricultural technology. The results underscore the transformative potential of ML in agriculture, paving the way for improved crop precision and decision-making processes.