IMPROVING AGRICULTURAL YIELDS IN THE DEMOCRATIC REPUBLIC OF CONGO USING MACHINE LEARNING ALGORITHMS

Authors

  • Rodolphe Nsimba Malumba Institute National du Bâtiment et des Travaux Publics, Kinshasa, Democratic Republic of Congo
  • Mardochee Longo Kayembe Department of Mathematics, Statistics and Computer Science, Faculty of Science and Technology, University of Kinshasa, D.R.Congon
  • Fiston Chrisnovic Balanganayi Kabutakapua Department of Mathematics, Statistics and Computer Science, Faculty of Science and Technology, University of Kinshasa, D.R.Congon
  • Bopatriciat Boluma Mangata Section Informatique de Gestion, Haute Ecole de Commerce de Kinshasa, Kinshasa, Democratic Republic of Congo
  • Trésor MAZAMBI KILONGO University of Bunia, Bunia, Democratic Republic of Congo
  • Rufin Tabiaki Tandele University of Bunia, Bunia, Democratic Republic of Congo
  • Emmanuel Ntanyungu Ndizieye Institut Supérieur de Commerce de Bunia, Bunia, Democratic Republic of Congo
  • Parfum Bukanga Christian Haute Ecole de Commerce de Kinshasa, Kinshasa, Democratic Republic of Congo

DOI:

https://doi.org/10.33480/techno.v22i1.6380

Keywords:

agricultural yields, climatic data, machine learning, predictive analysis, regression models

Abstract

This article presents an analysis of agricultural yields in the Democratic Republic of Congo (DRC) using machine learning algorithms. The study is based on around 30,000 records covering several years of agricultural production. Each record includes variables such as seed type, climatic conditions (temperature, rainfall and humidity), soil characteristics (pH, nutrients), farming practices (fertilizer use, irrigation) and yields obtained. The data comes from a variety of sources, including METTELSAT, the World Meteorological Organization (WMO) and WorldClim for climate data, and the DRC Ministry of Agriculture and the FAO for soil and agricultural data. The algorithms evaluated include linear regression, random forest regression, Gradient Boosting Machines (GBM), Support Vector Machines (SVM), and Artificial Neural Networks (ANN). The performance of the algorithms is measured using metrics such as MSE, MAE, RMSE, R² Score and MAPE on three separate case studies (Farm A, Farm B and Farm C). The results show that artificial neural networks (ANNs) perform best, with MSE ranging from 600 to 850, MAE from 12 to 17, RMSE from 24.49 to 29.15, R² Score from 0.92 to 0.95, and MAPE from 8.5% to 10.7%. Next came GBM, random forest regression, SVM and finally linear regression. These results highlight the potential of machine learning algorithms to improve agricultural yield forecasts in the DRC.

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Published

2025-03-20

How to Cite

Nsimba Malumba, R., Longo Kayembe, M., Balanganayi Kabutakapua, F. C., Boluma Mangata, B., MAZAMBI KILONGO, T., Tabiaki Tandele, R., Ntanyungu Ndizieye, E., & Bukanga Christian, P. (2025). IMPROVING AGRICULTURAL YIELDS IN THE DEMOCRATIC REPUBLIC OF CONGO USING MACHINE LEARNING ALGORITHMS. Jurnal Techno Nusa Mandiri, 22(1), 81–89. https://doi.org/10.33480/techno.v22i1.6380