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

Local Chapter São Paulo, Brazil Chapter

Coordinated byBrazil ,

Wallace G. Ferreira

Status: Ongoing

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.

The problem.

A machine learning challenge using computer vision can significantly contribute to solving the problem of plant disease management in Brazilian agriculture and have a positive impact on the local community. This collaborative effort can help to the creation of robust disease detection models that are tailored to the specific needs of Brazilian crops and farming practices. By accurately and efficiently identifying diseases, these models can empower local farmers with timely information for proactive decision-making, allowing them to implement targeted treatments, reduce crop losses, and optimize resource usage. This ultimately improves the livelihoods of farmers, ensures food security, and contributes to the sustainable development of Brazilian agriculture.

Project goals.

- Literature review, available code review, and problem statement - Data collection and dataset labeling, data preprocessing, and augmentation - Models for transfer learning and feature extraction - Development, training, testing, and tuning machine learning and deep learning models - Web app development and deployment

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

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