Using Prescriptive Analytics for the Determination of Optimal Crop Yield

Terungwa Simon Yange,Charity Ojochogwu Egbunu,Oluoha Onyekwere,Idris Abdulkadri,Alao Abiodun Abdulrahman,Malik Adeiza Rufai

doi:10.11648/j.ijdsa.20200603.11

Terungwa Simon Yange, Charity Ojochogwu Egbunu + Show 4 more

Open Access

https://doi.org/10.11648/j.ijdsa.20200603.11

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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%.

Highlights

The Nigeria agriculture is highly differentiated in terms of its climate, soil, water, crops, horticultural crops, plantation crops, medicinal crops, livestock, etc
The data used in this research was collected from Agricultural Development Programme (ADP), Ministry of Agriculture and the Nigeria Meteorological data station (NiMet), all in Lokoja, Kogi State, Nigeria
The algorithms used in the benchmarking include: linear regression, random forest, KNN and decision tree

Summary

Introduction

The Nigeria agriculture is highly differentiated in terms of its climate, soil, water, crops, horticultural crops, plantation crops, medicinal crops, livestock, etc. The agricultural sector is a major employer of labor but due attention has not been to it as Nigeria had relied on oil in the past decades to generate revenue and provide foreign exchange. Development in Nigeria, food (crop) production has not kept to pace with population growth, resulting in rising food imports and declining levels of national food self-sufficiency [2]. With the recent ban of food importation in Nigeria, all hands need to be on deck to increase food production level as a nation; because with the high birth rate recorded annually, it will be challenging for local food supply to meet with the demand. Agriculture is facing the problem of changes in the resources that are directly affecting crop yield, so the agricultural productivities in Nigeria are unpredictable.

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