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

• Published in: Agriculture (Basel) Vol. 13; no. 1; p. 225
• Main Authors: Kuradusenge, Martin, Hitimana, Eric, Hanyurwimfura, Damien, Rukundo, Placide, Mtonga, Kambombo, Mukasine, Angelique, Uwitonze, Claudette, Ngabonziza, Jackson, Uwamahoro, Angelique
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2023
• Subjects: Agricultural economics; Agricultural production; Agriculture; air temperature; Annual reports; Climate change; Corn; Crop production; Crop yield; Crops; crops yield; Data collection; Data mining; Decision trees; Drought; Economic conditions; Extreme weather; Food security; Harvest; Harvesting; Humidity; income; Internet of Things; Irish potato; Learning algorithms; Machine learning; maize; Meteorological data; Neural networks; Optimization; Polynomials; Potatoes; Precipitation; Radiation; Rain; Rainfall; random forest; regression analysis; Research methodology; Risk management; Risk reduction; Rwanda; Solanum tuberosum; Temperature; Vegetables; Weather; yield forecasting; Europe; Rwanda
• ISSN: 2077-0472; 2077-0472
• DOI: 10.3390/agriculture13010225

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 R2 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.