Intelligent Maize Yield Prediction Model Based on Plant Attributes and Machine Learning Algorithms

Oyenike Mary Olanrewaju, Eli Adama Jiya and Faith Oluwatosin Echobu
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Oyenike Mary Olanrewaju: Faculty of Computing, Federal University Dutsin-Ma, Katsina State, Nigeria.
Eli Adama Jiya: Faculty of Computing, Federal University Dutsin-Ma, Katsina State, Nigeria.
Faith Oluwatosin Echobu: Faculty of Computing, Federal University Dutsin-Ma, Katsina State, Nigeria.

International Journal of Research and Scientific Innovation, 2024, vol. 11, issue 7, 1097-1104

Abstract: Agriculture is a vital component of the Nigerian economy. The sector is a major source of employment for a large number of Nigerians. Maize is a widely planted crop and consumed in Nigeria, especially in the northern part of the country, with many poor families relying on it as the major source of carbohydrates. Therefore, sufficient provision of the crop is very vital, and prediction of the yield is very essential for proper planning in case of crop failure. This research developed three machine learning models for predicting maize yield using Random Tree, Random Forest and Neural Networks. The work made use of maize yield data from an experimental farm of Federal University Dutsin-ma, Katsina state. From the performance evaluation of the models, the Random Tree model demonstrated better performance than other models. It achieved the lowest MAE, RMSE, RAE, and RRSE values of 0.093, 0.096, 19.7%, and 19.2% respectively. This result indicates a lower error rate and a higher accuracy of almost 80% in predicting the numerical value of the weight of the maize yield. It is recommended that the model here be used to predict future maize yield in the state for proper planning and to ensure food security for the people of the state who are major maize consumers.

Date: 2024
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