Machine learning in cocoa agriculture: A systematic literature review
DOI:
https://doi.org/10.55873/8mg1dw29Keywords:
Crop Classification, Disease Detection, Precision Farming, Yield Prediction, Computer VisionAbstract
Artificial intelligence has gained increasing importance across different sectors, including agriculture, where machine learning offers new opportunities to optimize production processes. In this context, the present study analyzes the application of machine learning in cocoa agriculture through a systematic review of the scientific literature published between 2020 and 2024. The search strategy included IEEE Xplore, Springer Link, Scopus, Google Scholar, ACM Digital Library, and Semantic Scholar, initially identifying 10,320 documents. Following the selection process, 66 relevant articles were included in the analysis. The reviewed studies show that machine learning is mainly applied to the early detection of cocoa diseases, crop yield prediction, and the assessment and improvement of bean quality. The most frequently used techniques include neural networks, support vector machines (SVM), and random forest algorithms due to their ability to process large volumes of data. Furthermore, these techniques enable the analysis of factors related to climatic conditions, soil characteristics, and pest occurrence, thereby contributing to improved decision-making and the optimization of cocoa production.
Downloads
References
Araújo, M. S., Chaves, S. F. S., Guimarães, M. H. D., Dias, L. A. S., & Aguilar, M. A. G. (2024). Multi-trait selection for nutritional and physiological quality of cacao genotypes in irrigated and non-irrigated environments. Scientific Reports, 14(1), 6368. https://doi.org/10.1038/s41598-024-56556-7
Ayikpa, K. J., Gouton, P., Mamadou, D., & Ballo, A. B. (2024). Classification of cocoa beans by analyzing spectral measurements using machine learning and genetic algorithm. Journal of Imaging, 10(1). https://doi.org/10.3390/jimaging10010019
Caicedo-Vargas, C., Pérez-Neira, D., Abad-González, J., & Gallar, D. (2023). Agroecology as a means to improve energy metabolism and economic management in smallholder cocoa farmers in the Ecuadorian Amazon. Sustainable Production and Consumption, 41, 201–212. https://doi.org/10.1016/j.spc.2023.08.005
Cilas, C., & Bastide, P. (2020). Challenges to cocoa production in the face of climate change and the spread of pests and diseases. Agronomy, 10(9), 1232. https://doi.org/10.3390/agronomy10091232
Cock, J., Jiménez, D., Dorado, H., & Oberthür, T. (2023). Operations research and machine learning to manage risk and optimize production practices in agriculture: Good and bad experience. Current Opinion in Environmental Sustainability, 62, 101278. https://doi.org/10.1016/j.cosust.2023.101278
Khade, S., Malaye, V., Mhase, S., Shitole, A., & Shirsath, S. (2022). Crop yield prediction using machine learning. International Journal for Science Technology and Engineering, 10(5), 5308–5313. https://doi.org/10.22214/ijraset.2022.43191
Lamos-Díaz, H., Puentes-Garzón, D. E., & Zarate-Caicedo, D. A. (2020). Comparison between machine learning models for yield forecast in cocoa crops in Santander, Colombia. Revista Facultad de Ingeniería, 29(54), e10853. https://doi.org/10.19053/01211129.v29.n54.2020.10853
Lawal, J. O., & Omonona, B. T. (2014). The effects of rainfall and other weather parameters on cocoa production in Nigeria. 5(4), 518–523. https://doi.org/10.14295/CS.V5I4.365
Mamadou, D., Ayikpa, K. J., Ballo, A. B., & Kouassi, B. M. (2023). Cocoa pods diseases detection by MobileNet confluence and classification algorithms. International Journal of Advanced Computer Science and Applications, 14(9), 344–352. https://doi.org/10.14569/IJACSA.2023.0140937
Shaji, D., & Bharadwaj, A. (2024). Improving farm yield through agent based modeling. International Journal for Multidisciplinary Research, 6(2). https://doi.org/10.36948/ijfmr.2024.v06i02.16664
Wiah, E. N. (2017). Impact of climate change on cocoa yield in Ghana using vector autoregressive model. 1(2), 32–39.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Elias Terrazas-Huaman , Anthony Nelson Aliaga-Carbajal, Danger David Castellón-Apaza
This work is licensed under a Creative Commons Attribution 4.0 International License.
