The problem AGROTECH solves

Crop Cultivation Precision:
AgroTech's AI-powered crop recommendation system empowers urbanites to grow crops
efficiently by providing precise suggestions tailored to their specific location and conditions. This
ensures optimal crop yield without the need for costly agricultural experts.

Fertilizer Optimization:
Our app offers personalized fertilizer recommendations, guiding urban users in optimizing their
fertilizer usage based on the unique needs of their crops. This not only enhances crop quality
but also reduces unnecessary expenses.

Expert Guidance on Demand:
Urban dwellers facing agricultural challenges, such as plant diseases or other issues, can
connect directly with experienced farmers through our app. This instant access to expert
guidance enables quick problem resolution.

Income Generation for Farmers:
AgroTech establishes a direct link between urbanites and farmers, providing a platform for
farmers to share their expertise. This not only aids urban users but also serves as a source of
income for farmers who can monetize their agricultural knowledge.

Environmental Impact Through Tree Planting:
As users engage with the app and achieve milestones, our partnering NGOs, the Ecowarriors,
plant trees on their behalf. This not only encourages regular app use but also contributes to
environmental sustainability and reforestation efforts.

Enhanced Security and Privacy:
Utilizing a decentralized Web3 network, AgroTech prioritizes user privacy and security. This
innovative approach ensures greater control over digital identity and interactions, reducing
dependence on centralized authorities and safeguarding user data.

Challenges we ran into

Seamless ML Prediction Transition: Achieving a smooth transition in ML-based predictions,
especially in the realms of crop prediction and fertilizer recommendation, presented a challenge.
We iteratively refined our algorithms, ensuring a seamless and user-friendly experience.
Continuous user feedback played a pivotal role in enhancing prediction accuracy.

AgroConnect Integration Complexity: Integrating AgroConnect, the feature facilitating direct
communication between farmers and urban users, introduced complexities. To overcome this
hurdle, we adopted an agile development approach, prioritizing user interface improvements
and refining the backend architecture. This iterative process allowed for a gradual and
successful integration.

Multi-Tasking Implementation Challenges: Implementing multi-tasking capabilities for
simultaneous crop prediction, fertilizer recommendation, and AgroConnect functionalities proved
challenging. Our dedicated team collaborated closely, employing parallel development tracks
and conducting thorough testing phases. This collaborative effort resulted in a robust application
that seamlessly handles multiple tasks concurrently.

Bug Resolution with Agile Methodology: Throughout the development, encountering specific
bugs in the integration of AI for disease prediction demanded a meticulous approach. We
adopted an agile methodology, swiftly identifying and addressing bugs through continuous
testing cycles. Regular updates and bug fixes were released, ensuring a stable and reliable
application for users.