Crop Yield Prediction in Nigeria Using Machine Learning Techniques: A Case Study of Southern Part Nigeria
DOI:
https://doi.org/10.56919/usci.2324.004Keywords:
Decision tree classifier, Machine Learning, random forest, root mean square error, support vector machineAbstract
A key tool for digitalizing the agriculture sector and other industries is using big data and machine learning to predict farm produce. The inability of farmers to accurately predict yield is a great problem using previous farming experience. This study adopted three (3) machine learning approaches, including a decision tree classifier, random forest, and support vector machine, to model data from different zones and make predictions. The techniques adopted were tested using root mean square error to ensure the right prediction algorithm is adopted and the right values are obtained. Results show prediction from the South East is the best in terms of yields and accuracy when tested and evaluated, with 138.9 %.
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