This project aims to create an automated system for monitoring plant health using computer vision techniques. It leverages deep learning models for disease detection and classification in plants.
- Data Preprocessing: Handling missing values and outliers
- Feature Scaling: Applying normalization and standardization for improved performance
- Model Selection: Testing various algorithms to identify the best-performing model
- Data Visualization: Creating insightful visualizations for better understanding
- Model Evaluation: Assessing model performance using various metrics
The dataset comprises various features related to plant health, including images, environmental factors, and other relevant attributes. It encompasses a diverse range of plant diseases and related information.
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Data Exploration and Preprocessing:
- Identifying and handling missing values and anomalies
- Augmenting the dataset to increase its size and diversity
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Model Building and Evaluation:
- Implementing deep learning models for disease classification
- Evaluating model performance and selecting the most accurate model
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Data Visualization:
- Visualizing image data and disease distribution
- Analyzing patterns and correlations in the dataset
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- TensorFlow
- Clone the repository
git clone https://github.com/ThecoderPinar/Plant-Health-Monitoring-Project.git
- Install required packages
pip install -r requirements.txt
Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
Fork the Project
- Create your Feature Branch (git checkout -b feature/Plant-Health-Monitoring-Project)
- Commit your Changes (git commit -m 'Add some Plant-Health-Monitoring-Project')
- Push to the Branch (git push origin feature/Plant-Health-Monitoring-Project)
- Open a Pull Request
Distributed under the MIT License. See LICENSE for more information.