Research on World Agricultural Economy

Comparative Analysis of Machine Learning Models for Predicting Rice Yield: Insights from Agricultural Inputs and Practices in Rwanda

Cyprien Mugemangango

University of Rwanda

Joseph Nzabanita

University of Rwanda

Dieudonne Ndaruhuye Muhoza

University of Rwanda

Nathan Cahill

Rochester Insitute of Technology

DOI: https://doi.org/10.36956/rwae.v5i4.1247

Received: 17 August 2024 | Revised: 18 September 2024 | Accepted: 23 September 2024 | Published Online: 15 November 2024

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Abstract

Food security is a global challenge, especially in developing countries like Rwanda. With a growing population and limited agricultural land, Rwanda struggles to meet increasing food demands. Rice is a staple food crop in Rwanda, playing a crucial role in the country’s food security. However, factors like climate variability, soil nutrient management, and limited access to high-quality inputs hinder rice yield optimization. This paper investigates the most effective machine learning model for predicting rice crop yield in Rwanda, using agricultural inputs and practices. The study used secondary datasets from the National Institute of Statistics of Rwanda (NISR) for rice yield prediction. Eight supervised machine learning algorithms were used, including Linear Regression, Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network, eXtreme Gradient Boosting Tree, and AdaBoost. The models were evaluated based on their accuracy in predicting rice yields, with RMSE, MAE, and Relative Error as primary metrics. The feature importance analysis was also conducted to identify significant factors influencing yield predictions. The study’s findings revealed that the Adaptive Boosting Tree model outperformed the other machine learning models in predicting rice yield. This model achieved RMSE, MAE and Relative Error of 0.69, 0.46 and 12.4%, respectively, indicating a high level of predictive accuracy. The feature importance analysis further highlighted the key factors that contributed to rice yield predictions, with the quantity of inorganic fertilizer, degree of erosion, season, and seed type emerging as the most influential variables. The study demonstrates the effectiveness of machine learning models, particularly the Adaptive Boosting Tree, in improving rice yield predictions and highlighting the crucial role of agricultural inputs like fertilizer and seed type, in influencing crop yields. The output from this study will help the farmers and stakeholders to make data-driven decisions about resource use and crop management.

Keywords: Agricultural Inputs; Agricultural Practices; Machine Learning; Artiϐicial Intelligence; Precision Farming

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