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

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.