Crop Pest Management Using AI in Somalia

Challenge 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.

The Problem

The agriculture sector in Somalia faces a convergence of challenges, including recurrent droughts, civil conflicts, and limited infrastructure. The periodic flooding of the Juba and Shabelle rivers compounds these problems, leading to extensive damage to farmlands and susceptibility of crops to pests and pathogens.

In recent times, Somalia faced significant agricultural losses due to crop pests, particularly the desert locust invasion. Conventional pest management techniques have proven insufficient to combat the heightened infestation risks brought about by flooding.

As a result, there is an urgent need to implement innovative and sustainable integrated pest management solutions that can protect crops and enhance agricultural resilience in the face of such challenges.

Goal of the Project

As a solution to the stated problem, a concept is proposed aiming to address the challenges faced by the agricultural sector and revolutionize pest management practices to safeguard agricultural productivity. 

The project will try to address these specific objectives:

• Develop an AI-powered crop pest management system tailored to Somalia's specific challenges.
• To employ cutting-edge technologies like computer vision, remote sensing and deep learning techniques to improve pest detection, real-time monitoring and surveillance, and precision pest control strategies.
• To provide data-driven decision-making support to farmers and policymakers by integrating data analytics and machine learning. 

Project Timeline

What you'll learn

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