Machine Learning as a Strategic Tool: A Comprehensive Literature Review for Advancing Agricultural Analysis, with Emphasis on the Cocoa Bean Quality Assessment

As precision agriculture develops prominence for sustainable cacao production in the face of global challenges the role of machine learning (ML) as an essential tool in automating and optimizing cacao bean quality assessment becomes significant. The paper provides an in-depth review and comparative analysis of ML applications in cocoa bean quality assessment, incorporating insights from 20 relevant publications. Methodologies spanning from conventional algorithms to deep learning (DL) models and hybrid approaches are thoroughly studied. The discussion contextualizes these findings within the environment of cocoa bean quality assessment, comparing them to existing literature and addressing the implications and limitations of ML in this domain. Addressing unexpected or contradictory outcomes provides deep insights into the different aspects of ML for cocoa bean studies. This study contributes to a more detailed knowledge of the strengths and limitations of various ML techniques in cocoa bean quality assessment, which will guide future research for successful and sustainable cocoa production.