Improving Food Security and Crop Yield in Nigeria through Machine Learning

Local Chapter Osun, Nigeria Chapter

Coordinated byNigeria ,

Status: Completed

Project Duration: 13 Sep 2021 - 08 Nov 2021

Open Source resources available from this project

Project background.

According to the Food and Agriculture Organisation of the United Nations, 2018, approximately 88 % of the farmers in Nigeria engage in agricultural production at a subsistence level, and they lack sustainable farming knowledge and practices. Also, Nigeria is endowed with different climatic conditions and soil quality which leads to lackluster crop production. This project is aimed at helping farmers to boost their farm produce and plan their farming system.

The problem.

Nigeria is among those hardest hit by climate change. According to research, the largest population of Nigeria depends largely on agriculture with climatic change, floods, and droughts being the most serious environmental threat causing hunger, disease, malnutrition, and poverty. At a time in Nigeria where the prices and availability of food commodities are scarce and highly costly, we need to need to produce more with the little available resources; AI could help to transform agriculture in Nigeria hence, the world at large. Hence, in this challenge, we will work on building agricultural models to help actors better think, predict, and advise farmers via a variety of AI applications that presents Nigeria with the potential to achieve food security in the country.  
With the help of technology, farmers should be able to predict when, where, and what is the climatic condition will be in Nigeria for the next farming season while promoting a data-driven agricultural system. Data such as soil PH, temperature, and moisture levels, combined with other data sources from World Bank’s data portal and Nigerian Metrological Agency could be processed to show exactly when and where farmers should add water or fertilizer, and to know the best crop type to be planted on the soil. The data will also help to decide where to invest, and help strengthen the understanding of crop losses while maximizing revenues and minimizing losses.

Project goals.

1. To help farmers to know the best crop to be planted based on the soil type. 2. To help farmers to know where and when to add water or fertilizer based on soil type. 3. To also help farmers to know the best crop mixing system to adopt. 4. To help minimize loses while maximizing crop yields and profit.

Project plan.

  • Week 1

    – Data collection
    – Data Analysis (EDA)

  • Week 2

    – Data Analysis(EDA)
    – Model building
    – Application Development( Deployment)

  • Week 3

    – Model Deployment
    – Report Planning

  • Week 4

    – Documenting Final Analysis Report

Learning outcomes.

By the end of this challenge, we should have achieved the following:

1. Extract data and perform some EDA on it.

2. Predict and detect drought.

3. Predict the occurrence of flood.

4. Predict crop yield.

5. Predict the best crop to plant

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