Indetifying Corn Diseases from a Picture of Corn using Computer Vision

Local Chapter Ethiopia Local Chapter

Coordinated by Ethiopia ,

Status: Completed

Project Duration: 14 Mar 2023 - 14 Apr 2023

Open Source resources available from this project

Project background.

Corn is one of the most essential crops in the world, and is used for a wide range of purposes. Despite its importance, corn production is often threatened by various diseases that can reduce yield and increase production costs. Traditionally, diagnosing corn diseases has relied on manual inspection by trained experts, which can be time-consuming, labor-intensive and not always accurate. Advances in machine learning have made it possible to identify diseases in corn using algorithms that can quickly and accurately analyze data. These algorithms can be used to diagnose diseases in real-time, which could revolutionize corn production, by allowing farmers to address issues quickly for improved yields, reducing the environmental impact and ultimately contribute to the food security of our world.

Project plan.

  • Week 1

    Data collection

  • Week 2

    Data preparation

  • Week 3


  • Week 4

    Testing and deploying

Learning outcomes.

– Pytorch
– Computer vision
– Team management

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