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Local Chapter project

Global Wheat Head Detection (GWHD) for Tanzania Using AI and Machine Learning

Machine LearningData ScienceData Visualization
Start date
May 31, 2023
Finish date
July 23, 2023
Project status
completed
Global Wheat Head Detection (GWHD) for Tanzania Using AI and Machine Learning

Challenge background

Located in North Africa, Algeria has sought to support agriculture because of its potential in this sector. Indeed, it has put in place several agricultural policies and the objective was to achieve food security by substituting local production for imported products. One of the most important goals is the development of modern and sustainable protected greenhouse cultivation in Algeria. Managing greenhouses by means of AI technologies allows growers to be more focused on their crops and provides control at their fingertips. Proposed by the University of Ain Temouchent, Algeria, this project aims to develop ML models for the management of intelligent greenhouses

The problem

The management of all equipment under one control system, including heating, venting, and irrigation, is a hard task in terms of systems management and data collection. As a seasonal grower with product cycles of up to two years in duration, patience is necessary. It takes time to collect the data for these systems to work and learn.

Greenhouse environments are also challenging for technology implementation due to broad temperature and humidity ranges, which influence both the electronic and mechanical components that contribute to their ongoing development. This can be a frustration for staff trying to complete their weekly plans.

AI solutions for greenhouse growers are still in their initial phases of development. The integration of intelligent control systems requires changes to processes, which can be disruptive to production, so flexibility and managing expectations are important to manage the greenhouses effectively.

Goal of the project

  • Build a strong community for sharing knowledge of AI and ML models in agriculture.
  • Decide the best values for managing the levels of temperature, humidity, the use of water, light, and other parameters.

Project timeline

  1. 1

    Week 1

    Understanding the problem conducting research on best datasets and best AI approaches

  2. 2

    Week 2

    Data collection and gathering

  3. 3

    Week 3

    Data cleaning / preprocessingn/ labelling

  4. 4

    Week 4

    Model building/ choosing the best

  5. 5

    Week 5

    Application building / real deployment

  6. 6

    Week 6

    Visualisation and publication

What you'll learn

Problem solving and Hands-on real-world AI experience

Get involved

What to expect from a Local Chapter project

First project

  • Welcomes beginners and experienced practitioners.
  • Focuses on education and collaborative delivery.
  • Produces open-source project work.

Benefits

  • Address a significant real-world problem with your skills.
  • Build your project portfolio.
  • Demonstrate your work to organizations and project partners.

Requirements

  • Working English communication.
  • A learning mindset.
  • Commitment to collaborative project work.