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.

1. Data Exploration and Preprocessing:

   • Identifying and handling missing values and anomalies
   • Augmenting the dataset to increase its size and diversity

2. Model Building and Evaluation:

   • Implementing deep learning models for disease classification
   • Evaluating model performance and selecting the most accurate model

3. Data Visualization:

   • Visualizing image data and disease distribution
   • Analyzing patterns and correlations in the dataset

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• TensorFlow

1. Clone the repository

   git clone https://github.com/ThecoderPinar/Plant-Health-Monitoring-Project.git

2. 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.