Seasonal Crop Yield Prediction in Nigeria Using Machine Learning Technique


  • Abdulbasit Ahmed Division of Agricultural Colleges, Ahamdu Bello University, Zaria
  • Sunday Eric Adewumi
  • Victoria Yemi-peters


Machine Learning, Support Vector Machine, Random Forest, Decision Tree Classifier and Algorithm.


The old methods adopted in the past by were very slow, undependable and sizable quantity of crops are damaged in fields because bacterial attacks and lack of adequate information. automating agriculture processes may likely be the solution to feed the nation in the future. Though there is still debate on its application to agriculture. The importance of food security in any society cannot be over emphasized, therefore balancing the inputs and outputs on a farm is fundamental to its success and profitability. With the increase in population index food production need to meet population growth, creating a wide gap between demand and supply of food. Data Mining is emerging research field in crop yield analysis. In the past farmers make use past yield to predict what they may likely have when farming in the current season The yield prediction is a major issue that remains to be solved based on available data. Data mining are the better choice for this purpose. Three (3) Different Data Mining techniques will be used for predicting crop yields during rainy and dry season. This research proposes and implements a system to predict crop yield from previous data. This can be done by using association rule mining on agriculture data. This research focuses on creation of a prediction model which may be used to future prediction of crop yield. It also shows that South East has the best in terms of accuracy for rainy season farming with model performance evaluation 138.9 using Decision Tree Classifier.


Download data is not yet available.

Author Biographies

Sunday Eric Adewumi



Victoria Yemi-peters





How to Cite

Ahmed, A., Adewumi, S. E., & Yemi-peters, V. (2023). Seasonal Crop Yield Prediction in Nigeria Using Machine Learning Technique . Journal of Applied Artificial Intelligence, 4(1), 9–20.




Most read articles by the same author(s)