Agrisim

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  AgriSim

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Inspiration:

AgriSim is inspired by the vision to harness technology for empowering farmers, ensuring they can achieve higher yields and sustainability in agriculture.

What it does:

Agrisim is a mobile application designed to empower farmers by enhancing the productivity and profitability of their farms through advanced data analysis. Using cutting-edge machine learning models, it provides farmers with tailored crop recommendations, helping them optimize their yields throughout the year. This personalized advice is generated by analyzing various crucial factors, such as soil pH levels, nutrient content (K, N, and P values), climate data, and even water table information.

The beauty of Agrisim is that it seamlessly gathers all this data from governmental and non-governmental sources through RESTful API integration, sparing farmers the hassle of manual data entry. This not only streamlines the process but also ensures accuracy and reliability, making it a user-friendly and flexible tool.

Moreover, Agrisim incorporates augmented reality (AR) technology, which allows farmers to physically walk the perimeters of their fields while using the app. With the AR view active, farmers can effortlessly capture the exact shape of their fields. Once they return to the starting point, the app automatically calculates the approximate area of the field based on the polygon they've created. This combination of cutting-edge technology and automation simplifies farm management and decision-making, ultimately boosting productivity and profitability for farmers.

Overall, AgriSim offers farmers a virtual farm evaluation using AR and provides precise crop-planting guidance based on real-time data on climate and soil quality.

How we built it:

The front-end of the app is designed using Dart in Flutter. The backend models are prepared using Jupyter notebooks, through which we prototyped the ML pipelines. We integrated AR technology(ARCore) with environmental data analytics, crafting an intuitive platform that offers actionable insights for efficient farm management.

Challenges we ran into:

We faced challenges in creating a user-friendly interface for technologically diverse users, ensuring data accuracy, and customizing our platform for different agricultural environments.

Accomplishments that we're proud of:

We're proud to have developed a scalable agritech solution that's received positive feedback from farmers, significantly improved crop yields, and fostered sustainable farming practices.

What we learned:

The project underscored the critical role of user-focused design in agritech and the transformative impact of combining AR with data analytics in agriculture.

What's next for AgriSim:

Next, we plan to expand our service offerings, enhance our predictive algorithms, and introduce new tools for AI-assisted pest management and yield forecasting.

Built With

• ar
• dart
• fastapi
• flutter
• machine-learning
• python