Crop Yield Prediction Using Machine Learning Models: Case of Irish Potato and Maize

Martin Kuradusenge (), Eric Hitimana, Damien Hanyurwimfura, Placide Rukundo, Kambombo Mtonga, Angelique Mukasine, Claudette Uwitonze, Jackson Ngabonziza and Angelique Uwamahoro
Additional contact information
Martin Kuradusenge: School of ICT, College of Science and Technology, University of Rwanda, KN 67, Kigali P.O. Box 3900, Rwanda
Eric Hitimana: School of ICT, College of Science and Technology, University of Rwanda, KN 67, Kigali P.O. Box 3900, Rwanda
Damien Hanyurwimfura: School of ICT, College of Science and Technology, University of Rwanda, KN 67, Kigali P.O. Box 3900, Rwanda
Placide Rukundo: Rwanda Agriculture and Animal Resources Development Board (RAB), Butare P.O. Box 138, Rwanda
Kambombo Mtonga: Training & Education Development Consulting, Lilongwe P.O. Box 164, Malawi
Angelique Mukasine: School of ICT, College of Science and Technology, University of Rwanda, KN 67, Kigali P.O. Box 3900, Rwanda
Claudette Uwitonze: School of ICT, College of Science and Technology, University of Rwanda, KN 67, Kigali P.O. Box 3900, Rwanda
Jackson Ngabonziza: School of ICT, College of Science and Technology, University of Rwanda, KN 67, Kigali P.O. Box 3900, Rwanda
Angelique Uwamahoro: School of ICT, College of Science and Technology, University of Rwanda, KN 67, Kigali P.O. Box 3900, Rwanda

Agriculture, 2023, vol. 13, issue 1, 1-19

Abstract: Although agriculture remains the dominant economic activity in many countries around the world, in recent years this sector has continued to be negatively impacted by climate change leading to food insecurities. This is so because extreme weather conditions induced by climate change are detrimental to most crops and affect the expected quantity of agricultural production. Although there is no way to fully mitigate these natural phenomena, it could be much better if there is information known earlier about the future so that farmers can plan accordingly. Early information sharing about expected crop production may support food insecurity risk reduction. In this regard, this work employs data mining techniques to predict future crop (i.e., Irish potatoes and Maize) harvests using weather and yields historical data for Musanze, a district in Rwanda. The study applies machine learning techniques to predict crop harvests based on weather data and communicate the information about production trends. Weather data and crop yields for Irish potatoes and maize were gathered from various sources. The collected data were analyzed through Random Forest, Polynomial Regression, and Support Vector Regressor. Rainfall and temperature were used as predictors. The models were trained and tested. The results indicate that Random Forest is the best model with root mean square error of 510.8 and 129.9 for potato and maize, respectively, whereas R 2 was 0.875 and 0.817 for the same crops datasets. The optimum weather conditions for the optimal crop yield were identified for each crop. The results suggests that Random Forest is recommended model for early crop yield prediction. The findings of this study will go a long way to enhance reliance on data for agriculture and climate change related decisions, especially in low-to-middle income countries such as Rwanda.

Keywords: Irish potato; maize; air temperature; rainfall; crops yield; random forest; prediction; support vector machine; polynomial regression (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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