Effectiveness of Machine Learning Models for Entrepreneurship, Innovation, and Crop Growth Prediction: A Systematic Literature Review

María De Los Angeles E.-C.; Josué Israel C.-A.; Christian Abraham D.-C. · Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology (2024) · DOI: 10.18687/leird2024.1.1.238

Resumen

– In recent years, the application of Machine Learning (ML) in agriculture has emerged as a crucial innovation for enhancing productivity. This systematic literature review (SLR) aims to evaluate the effectiveness of Machine Learning models in the quantitative analysis of crops. Utilizing the PICO methodology and adhering to PRISMA standards, research articles published between 2015 and 2024 were thoroughly analyzed. Results sourced from Scopus highlight that the most commonly employed techniques are Deep Learning, Random Forest, and Support Vector Machine (SVM) due to their high accuracy and capacity to manage large datasets. The review also addresses challenges related to data quality and model implementation, emphasizing the need for ongoing research and international collaboration to advance agricultural technology. The findings underscore the transformative potential of ML in agriculture, paving the way for enhanced crop precision and improved decision-making processes.

Instituciones: Universidad Tecnológica del Perú