Abstract
Deforestation in coffee-growing regions has long been difficult to accurately detect at scale, hampering efforts to protect rainforests. Recent advances in satellite technology and machine learning, however, offer a solution. Our team has developed a more precise method to address these challenges, combining improved imagery with these machine learning tools to more effectively monitor deforestation related to coffee production. Our approach not only enhances precision but also provides a more consistent and transparent framework for reporting deforestation events within coffee supply chains. This innovation supports ongoing efforts to combat deforestation and reduce the environmental impact of the coffee industry, offering a new resource for both policymakers and organizations on the ground. Furthermore, this work signals the broader potential of applying machine learning to address systemic environmental challenges.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ICC2024-18032/s1, Poster file (in PDF format) presented at ICC 2024.
Author Contributions
Conceptualization, D.B.; writing—original draft preparation, D.B.; and writing—review and editing, M.F. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Not applicable.
Conflicts of Interest
The authors are employed by Enveritas Inc., which is a nonprofit organization providing sustainability assurance for the coffee and cocoa industries.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).