{"id":27453657,"url":"https://github.com/ved-coder-king/wheat_ai_project","last_synced_at":"2025-04-15T13:56:30.934Z","repository":{"id":287979291,"uuid":"966405851","full_name":"VED-CODER-KING/wheat_AI_Project","owner":"VED-CODER-KING","description":"This project, Smart Wheat Farming AI System, was developed as part of the coursework for the Artificial Intelligence program at Esprit School of Engineering.","archived":false,"fork":false,"pushed_at":"2025-04-15T13:10:03.000Z","size":7,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-15T13:56:25.022Z","etag":null,"topics":["agriculture","data-analysis","data-visualization","deep-learning","image-classification","machine-learning","object-detection","python","wheat"],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":false,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/VED-CODER-KING.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-04-14T21:52:25.000Z","updated_at":"2025-04-15T13:10:07.000Z","dependencies_parsed_at":"2025-04-15T00:23:24.199Z","dependency_job_id":"7d44aa24-87ba-4783-a59c-74786e37add2","html_url":"https://github.com/VED-CODER-KING/wheat_AI_Project","commit_stats":null,"previous_names":["ved-coder-king/wheat_ai_project"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VED-CODER-KING%2Fwheat_AI_Project","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VED-CODER-KING%2Fwheat_AI_Project/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VED-CODER-KING%2Fwheat_AI_Project/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VED-CODER-KING%2Fwheat_AI_Project/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/VED-CODER-KING","download_url":"https://codeload.github.com/VED-CODER-KING/wheat_AI_Project/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":249085486,"owners_count":21210267,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["agriculture","data-analysis","data-visualization","deep-learning","image-classification","machine-learning","object-detection","python","wheat"],"created_at":"2025-04-15T13:56:30.243Z","updated_at":"2025-04-15T13:56:30.929Z","avatar_url":"https://github.com/VED-CODER-KING.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🌾 Smart Wheat Farming AI System 🌾\n\n![Wheat AI Project](https://img.shields.io/badge/Smart%20Wheat%20Farming%20AI%20System-Project-blue)\n\nWelcome to the **Smart Wheat Farming AI System** repository! This project was developed as part of the coursework for the Artificial Intelligence program at Esprit School of Engineering. The aim is to leverage artificial intelligence to improve wheat farming practices, making them more efficient and sustainable.\n\n## Table of Contents\n\n1. [Introduction](#introduction)\n2. [Features](#features)\n3. [Technologies Used](#technologies-used)\n4. [Getting Started](#getting-started)\n5. [How to Use](#how-to-use)\n6. [Contributing](#contributing)\n7. [License](#license)\n8. [Contact](#contact)\n9. [Releases](#releases)\n\n## Introduction\n\nWheat is one of the most important crops globally, serving as a staple food for millions. However, farmers face numerous challenges, including pests, diseases, and climate variability. This project utilizes AI to address these issues, providing tools for data analysis, image classification, and object detection.\n\n## Features\n\n- **Data Analysis**: Analyze various data points to understand trends and patterns in wheat farming.\n- **Data Visualization**: Visualize data for better decision-making.\n- **Deep Learning**: Implement deep learning models to enhance image classification.\n- **Object Detection**: Detect and classify objects in images, such as pests and diseases.\n- **User-Friendly Interface**: Easy-to-use interface for farmers and researchers.\n\n## Technologies Used\n\nThis project incorporates several technologies:\n\n- **Python**: The primary programming language.\n- **Machine Learning Libraries**: Libraries such as TensorFlow and Keras for model building.\n- **Data Visualization Tools**: Matplotlib and Seaborn for creating visualizations.\n- **Image Processing**: OpenCV for image analysis.\n- **Web Frameworks**: Flask for building the web application.\n\n## Getting Started\n\nTo get started with the Smart Wheat Farming AI System, follow these steps:\n\n1. **Clone the Repository**:\n   ```bash\n   git clone https://github.com/VED-CODER-KING/wheat_AI_Project.git\n   cd wheat_AI_Project\n   ```\n\n2. **Install Dependencies**:\n   Ensure you have Python installed. Then, install the required libraries:\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n3. **Run the Application**:\n   Start the application using:\n   ```bash\n   python app.py\n   ```\n\n## How to Use\n\n1. **Upload Images**: Use the interface to upload images of wheat crops.\n2. **Analyze Data**: Access the data analysis section to view trends and patterns.\n3. **Visualize Results**: Use the visualization tools to generate charts and graphs.\n4. **Get Predictions**: The AI model will provide predictions based on the uploaded images.\n\n## Contributing\n\nWe welcome contributions to enhance this project. If you want to contribute, please follow these steps:\n\n1. Fork the repository.\n2. Create a new branch:\n   ```bash\n   git checkout -b feature/YourFeature\n   ```\n3. Make your changes and commit them:\n   ```bash\n   git commit -m \"Add your message here\"\n   ```\n4. Push to the branch:\n   ```bash\n   git push origin feature/YourFeature\n   ```\n5. Create a pull request.\n\n## License\n\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.\n\n## Contact\n\nFor any inquiries or feedback, please reach out to the project maintainers:\n\n- **Your Name**: [Your Email]\n- **GitHub**: [Your GitHub Profile]\n\n## Releases\n\nTo download the latest version of the Smart Wheat Farming AI System, visit our [Releases section](https://github.com/VED-CODER-KING/wheat_AI_Project/releases). Make sure to download and execute the necessary files to get started with the project.\n\n![Wheat Fields](https://images.unsplash.com/photo-1565299624940-9d79e4e5e3b3)\n\nThank you for your interest in the Smart Wheat Farming AI System! Together, we can make a difference in agriculture. 🌱","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fved-coder-king%2Fwheat_ai_project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fved-coder-king%2Fwheat_ai_project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fved-coder-king%2Fwheat_ai_project/lists"}