A review of machine learning applications in Cacao Post-harvest management

Zhaira Marianne Ferrancol Ferran, Luisa Ann Tamayo Ongquit * and Edwin Romeroso Arboleda

Department of Computer, Electronics and Electrical Engineering, College of Engineering and Information Technology, Cavite State University, Philippines.
 
Review
International Journal of Science and Research Archive, 2024, 11(01), 1540–1550.
Article DOI: 10.30574/ijsra.2024.11.1.0186
Publication history: 
Received on 23 December 2023; revised on 06 February 2024; accepted on 08 February 2024
 
Abstract: 
Cacao beans play a crucial role in chocolate production, making them a highly relevant crop. The post-harvesting stage of cacao beans, encompassing classification, quality assessment, and fermentation, holds significant importance. With the increasing demand for marketable cacao beans, the need for reliable, accurate, and fast technologies has emerged. This study provides a comprehensive review of machine learning techniques in the post-harvesting stage of cacao beans from 2016 to the present. Analyzing 36 studies, it focuses on classification, quality assessment, and fermentation. The proposed framework includes the application domain, learning algorithms, performance metrics, and reported impacts. Notably, it explores various machine learning applications like classification, quality assessment, and fermentation, highlighting commonly used algorithms like ANN, CNN, and SVM. In terms of performance metrics, GLCM achieved the highest accuracy (99.61%) in cacao classification, ANFIS excelled in quality assessment (99.715%), and k-NN emerged as the most accurate for fermentation. This review serves as a valuable resource for researchers in the cacao bean sector, offering insights into machine learning advancements.
 
Keywords: 
Cacao beans; Classification; Fermentation; Machine learning; Quality assessment; Post-harvesting stage
 
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0