Inspiration
Farmers often face crop losses due to delayed pest detection, unpredictable weather, and lack of real-time farm monitoring. Small and medium-scale farmers especially lack access to affordable smart farming tools. We wanted to create a solution that combines AI and IoT to help farmers make timely decisions and reduce crop loss while supporting sustainable agriculture.
What it does
Crop AI is a smart farming assistant that helps farmers monitor and protect their crops using technology.
The platform:
Monitors soil and environmental conditions using IoT sensors.
Detects pests from crop images using AI.
Provides weather insights for better irrigation and harvesting decisions.
Sends alerts and farming recommendations.
Plans future integration of market price insights for better profit decisions.
The goal is to increase productivity and reduce crop damage.
How we built it
We developed Crop AI using AI models for pest detection and integrated farm monitoring concepts.
The application interface and workflows were prototyped using Momen’s no-code/low-code platform, allowing us to quickly build dashboards, workflows, and user interaction without heavy backend coding.
Technologies used:
AI image classification for pest detection
IoT-based farm monitoring concept
Weather data integration
Momen platform for app structure and workflow
Web-based dashboard interface
Challenges we ran into
Collecting reliable pest image data for model training.
Designing a simple interface suitable for farmers.
Integrating multiple components like sensors, AI detection, and advisory outputs into one workflow.
Limited hackathon time to fully deploy real hardware sensors.
Accomplishments that we're proud of
Building an integrated smart farming solution within hackathon time.
Successfully implementing AI-based pest detection concept.
Creating an easy-to-understand farmer-friendly dashboard.
Combining sustainability and technology into one solution.
What we learned
How AI and IoT can significantly impact agriculture.
Importance of designing technology accessible to non-technical users.
Rapid prototyping using low-code platforms.
Team collaboration and quick problem solving under time constraints.
What's next for Crop AI
Next, we plan to:
Deploy real IoT sensors in farms.
Improve pest detection accuracy with larger datasets.
Add multilingual farmer support.
Integrate live market price prediction.
Develop a mobile app for rural accessibility.
Partner with agricultural organizations for real-world deployment.
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