Welcome to AgriGuard! This project aims to revolutionize agricultural practices in India by leveraging AI/ML and modern web technologies to empower farmers with timely insights and decision-making support.
In India, agricultural productivity suffers from the absence of monitoring, knowledge gaps, and insufficient decision-making support. Integrating IoT for real-time monitoring, enhancing accessibility to modern agricultural practices, and deploying AI/ML-driven decision support systems can empower farmers with timely insights, knowledge, and tools, ultimately leading to more efficient and sustainable crop cultivation.
Our project relies on various technologies to fulfill its goals:
- Backend Operations and Machine Learning: Python is utilized for backend operations and machine learning tasks.
- Frontend Interface: ReactJS powers the frontend interface, providing a dynamic and responsive user experience.
- Web Development Backbone: Flask acts as the backbone of our web development, ensuring a robust and scalable backend infrastructure.
- Machine Learning Algorithms: We utilize machine learning algorithms for data analysis and optimization, enhancing decision-making capabilities.
- User Interface Design: Design tools such as Figma contribute to crafting a user-friendly interface, ensuring an intuitive user experience.
Description: This image depicts how crop images are captured and provides an overview of the web interface.
Description: This image illustrates the backend part of our project, showing how data processing and machine learning tasks are performed.
Description: This image showcases the revenue generation aspect of our model and includes business-related content.
Currently, companies physically engage with farmers to verify fertilizer efficacy, necessitating feedback visits. With a streamlined remote consultancy system, companies optimize crop cultivation, recommend fertilizers, and establish a recurring revenue model through consultation fees and sales, driving sustainable growth for both parties involved.
This initiative promises economic benefits by enabling companies to target consumers who have cultivated crops treated with their fertilizers. By enhancing farmers' productivity, it fosters increased revenue streams for both agricultural businesses and farmers, thereby contributing to overall economic growth and sustainability.
We are continually working on improving this project. If you have any suggestions or contributions, feel free to contribute!
- Frontend Repository: AgriGuard Frontend
- Backend Repository: AgriGuard Backend
- Machine Learning Repository: AgriGuard Machine Learning