Using Prescriptive Analytics for the Determination of Optimal Crop Yield

Abstract

The application of data mining has been utilized in different fields ranging from agriculture, finance, education, security, medicine, research etc. Data mining derives useful information from careful examination of data. In Nigeria, Agriculture plays a critical role in the economy as it provides high level of employment for many people. It is typical of farmers in Nigeria to plant crops without paying considerate attention to the soil and crop requirements as most farmers inherit the lands used for farming from their fathers and just continue in the pattern of farming they had always known. This is not favorable in the level of productivity they can actually attain as the effect can be seen in same level of crop yield year after year if not even worse. Modern farming techniques make use of data mining from previous data considering soil types, and other factors like weather and climatic conditions. This study built a model that enables possible prediction of crop yield from the historic data collected and offers suggestions to farmers on the right soil nutrients requirements that would enhance crop yield. This will enable early prediction of crop yield and prior idea to improve on the soil to increase productivity. The research used XGBoost algorithm for the crop yield prediction and the Support Vector Machine algorithm for the recommendation of appropriate improvement of soil nutrient requirements. The accuracy obtained for the prediction with XGBoost was 95.28%, while that obtained for the recommendation of fertilizer using SVM was 97.86%.

Optimizing Fertilizer Recommendations for Banana Plant Using Feature Extraction Method and Machine Learning Classification

“…Random Forest is effective for Soil Classification and achieves 86.35% accuracy compared to SVM. In the case of crop prediction, SVM outperforms Random Forest with 99.47% accuracy.The accuracy of this method using Kmean's minimum evaluation and SVM with GA algorithm is 86.54%, 93.63% and 95.71%, respectively [6].…”