Challenges We Faced:

During the development of Agroscan, we encountered several key challenges:

Selecting the right technology: Choosing the appropriate tech stack that balanced performance, scalability, and ease of development was a critical and time-consuming decision.

Frontend development: Designing a user-friendly and responsive mobile interface that could be easily used by farmers with varying levels of digital literacy posed several UI/UX challenges.

Backend development: Building a robust and secure backend to manage user data, disease reports, and model predictions required careful planning and implementation.

Integration of frontend and backend: Ensuring smooth and real-time communication between the frontend and backend systems was a complex task, especially for handling image uploads and model responses.

Building the ML model: Developing an accurate machine learning model capable of identifying various crop diseases involved extensive data collection, preprocessing, and algorithm selection.

Training the ML model: Model training demanded significant computational resources and time, particularly in achieving high accuracy and minimizing false predictions.

Meeting the deadline: Coordinating all components and completing the project within the given timeframe was challenging, requiring effective teamwork and time management.