Advancements in Cocoa Production: A Bibliometric Review

What is it about?

This research examines the application of computational and mathematical methods in cocoa production from 2000 to 2020. Using a two-pronged approach, it analyses 1886 documents and reviews 734 investigations focused on cocoa or its derivatives. The findings emphasize interdisciplinary contributions across Chemistry, Biology, Social Sciences, and more, highlighting advancements in genetic improvement, machinery optimization, and crop yield enhancement. Machine learning emerges as a key area, addressing challenges like bean quality, ripeness detection, and yield estimation.

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Photo by Etty Fidele on Unsplash

Photo by Etty Fidele on Unsplash

Why is it important?

The study underscores the significance of integrating advanced computational methods in cocoa farming to drive sustainability and efficiency. It highlights the critical need for digital transformation, especially for smallholder farmers, in the cocoa value chain. The research expands understanding of agri-food supply chains, noting a gap in ICT and IoT solutions in cocoa production management. It stresses the development and transfer of high-tech tools for better agricultural practices. This is essential for improving production, optimizing crop yields, and enhancing the quality and sustainability of cocoa products, vital for smallholder farmers. KEY TAKEAWAY: Interdisciplinary cocoa research uses computational methods, emphasizing machine learning's role in quality assessment and yield prediction. Adoption gaps in ICT/IoT technologies, especially among smallholders, prompt calls for digital transformation, aiming for sustainability and efficiency. Accessible high-tech tools are crucial for improved agricultural practices.

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