Version 1
: Received: 18 June 2024 / Approved: 20 June 2024 / Online: 21 June 2024 (05:08:40 CEST)
How to cite:
de C. Motta, I. V.; Vuillerme, N.; Pham, H.-H.; Pereira de Figueiredo, F. A. Machine Learning Techniques for Coffee Classification: A Comprehensive Review of Scientific Research. Preprints2024, 2024061462. https://doi.org/10.20944/preprints202406.1462.v1
de C. Motta, I. V.; Vuillerme, N.; Pham, H.-H.; Pereira de Figueiredo, F. A. Machine Learning Techniques for Coffee Classification: A Comprehensive Review of Scientific Research. Preprints 2024, 2024061462. https://doi.org/10.20944/preprints202406.1462.v1
de C. Motta, I. V.; Vuillerme, N.; Pham, H.-H.; Pereira de Figueiredo, F. A. Machine Learning Techniques for Coffee Classification: A Comprehensive Review of Scientific Research. Preprints2024, 2024061462. https://doi.org/10.20944/preprints202406.1462.v1
APA Style
de C. Motta, I. V., Vuillerme, N., Pham, H. H., & Pereira de Figueiredo, F. A. (2024). Machine Learning Techniques for Coffee Classification: A Comprehensive Review of Scientific Research. Preprints. https://doi.org/10.20944/preprints202406.1462.v1
Chicago/Turabian Style
de C. Motta, I. V., Huy-Hieu Pham and Felipe Augusto Pereira de Figueiredo. 2024 "Machine Learning Techniques for Coffee Classification: A Comprehensive Review of Scientific Research" Preprints. https://doi.org/10.20944/preprints202406.1462.v1
Abstract
In the realm of agribusiness, transformative shifts are underway, propelled by the growing demands and expanding scales of grain production. This evolution calls for a critical reevaluation of the existing paradigms in coffee production and marketing paradigms, with a specific focus on integrating Artificial Intelligence (AI). This work aims to review, synthesize, and summarize the available data regarding how Machine Learning (ML) has been used to detect and classify characteristics in coffee beans and leaves. For this purpose, a comprehensive literature review of the most significant research contributions describing the application of AI for advanced classification techniques in coffee agriculture has been carried out. Our analysis suggests that implementing AI technologies allows the classification of coffee, encompassing various attributes such as maturity, roast intensity, disease identification, flavor profiles, and overall quality. More largely, this technological advancement holds the potential to revolutionize coffee farming by providing producers and agricultural specialists with sophisticated tools to enhance production efficiency, minimize costs, and improve the accuracy and confidence of their decision-making processes.
Keywords
artificial intelligence; coffee bean and leaves classification; computer vision; machine-learning
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
