Classification of Plant Diseases in Brazilian Agriculture Using Computer Vision and Machine Learning

Local Chapter São Paulo, Brazil Chapter

Coordinated by Brazil ,

Status: Completed

Project Duration: 02 Aug 2023 - 02 Oct 2023

Open Source resources available from this project

Project background.

Brazil, known for its vast agricultural landscapes, faces significant challenges in monitoring and diagnosing plant diseases that can negatively impact crop yields.

One of the key objectives is to develop accurate and efficient disease detection models that can identify specific symptoms and patterns associated with various plant diseases. By training these models on diverse datasets encompassing different crops and disease types, researchers aim to create robust and adaptable systems that can accurately classify and quantify diseases.

However, several challenges persist in this field. The high variability of environmental conditions, such as lighting, weather, and plant growth stages, poses a significant hurdle for accurate disease detection. Furthermore, the diversity of Brazilian crops and diseases requires extensive research and development efforts to ensure generalization and applicability across different regions and conditions.

Project plan.

  • Week 1

    Onboarding; Literature review; Problem statement; Brainstorming

  • Week 2

    Data collection; Data labeling; Datasets preparation/translation; Data preprocessing

  • Week 3

    Data augmentation; Transfer learning; Feature extraction

  • Week 4

    Models building

  • Week 5

    Models improvement

  • Week 6

    Models final tuning and tests

  • Week 7

    Web app development

  • Week 8

    Web app deployment; Project overview; Final presentation

Learning outcomes.

1. Computer vision

2. Machine learning

3. Deep learning

4. Web app development

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