Crop Pest Management Using AI in Somalia

Local Chapter Somalia Chapter

Coordinated by ,

Status: Completed

Project Duration: 20 Sep 2023 - 20 Oct 2023

Open Source resources available from this project

Project background.

Somalia, a nation nestled in the Horn of Africa, is characterized by diverse geography, encompassing coastal plains, highlands, and arid desert regions. With a total of 8.9 million hectares of arable land, agriculture has been the cornerstone of its economy and an intrinsic part of Somali culture for generations. Traditional practices such as subsistence farming and nomadic pastoralism have sustained communities, while cash crops and livestock have played pivotal roles in generating revenue.

According to The United Nations Food and Agriculture Organization, Somalia produced approximately $56.1 million worth of Bananas, Grapefruits, Sesame seeds, and Lemons in 2021.

The periodic overflow of the Juba and Shabelle rivers has had a profound impact on Somali farmers and their produce, exacerbating the susceptibility of crops to pests and pathogens. The river’s flooding disrupts agricultural activities, leading to soil erosion, submergence of fields, and destruction of standing crops.

Such disruptions in the agricultural landscape create a favorable environment for pests and pathogens to thrive and spread rapidly. The inundation of farmlands not only damages crops but also disrupts the ecological balance, leaving crops more vulnerable to infestations and diseases. As a consequence, crop yields are significantly affected, posing a severe threat to food security and farmers’ livelihoods.

Despite adversities the agricultural sector has faced over the years, Somali farmers have exhibited resilience, utilizing age-old techniques such as rotation grazing, communal grazing, organic fertilizers, animal dung etc to cope with challenges and maintain the significance of agriculture in their lives.

Project plan.

  • Week 1

    Research the problem
    Review past approaches
    Identify possible data sources
    Review academic research papers

  • Week 2

    Data Collection

  • Week 3

    Data preprocessing

  • Week 4

    Exploratory Data Analysis

  • Week 5

    Data Visualization

  • Week 6

    Model Training

  • Week 7

    Model optimization

  • Week 8

    Application Deployment

Learning outcomes.

Remote sensed data collection, computer vision, data analysis, object detection.

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