Oduntan, O.E. and Hammed, Mudasiru (2018) A Predictive Model For Improving Cereals Crop Productivity Using Supervised Machine Learning Algorithm. In: CAPA 40th Anniversary International Conference, August 26 - 31, 2018, Nicon Luxury Hotel, Abuja.
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Abstract
In a developing country, the gross domestic productivity of the nation is a function of the level of its production. Agriculture is regarded as the bedrock of the nation; hence farmers are faced with the challenges of determining the quantity of its product each year, leading insufficient produce. This study focuses on the developing of a predictive model that provides a cultivation plan for farmers to be able to produce cereals crops that are measurable with the population and the rate of consumption using a decision tree algorithm. The methodology employed involves the use of two variables: Independent and dependent variables. The dependent variables are the consumption rate and production rate while the independent variable is the population rate. The Decision Tree Algorithm makes the prediction of rice and beans produced based on the population. The outcome of the prediction aids the farmer to plan adequately in cultivation of cereals. The final rules extracted from this study, are useful for farmers and the government to make proactive decisions.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Subjects: | Q Science > Q Science (General) |
| Divisions: | Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science |
| Depositing User: | Mr Taiwo Egbeyemi |
| Date Deposited: | 02 Sep 2020 09:38 |
| Last Modified: | 02 Sep 2020 09:38 |
| URI: | http://eprints.federalpolyilaro.edu.ng/id/eprint/1148 |
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