SmartFarm
AI/ML
Agritech
Hackathon
Background
Farmers struggle with traditional methods of assessing readiness for harvest, often resulting in suboptimal yields, increased resource use, and financial losses. The lack of a precise and data-driven solution has made it challenging for them to synchronize harvesting activities with peak crop maturity, leading to inefficiencies in the entire agricultural process.
Product Overview
SmartFarm is an ingenious agricultural tool designed to empower farmers with accurate and data-driven insights, revolutionizing the way crops are harvested. This innovative solution integrates real-time weather data, soil quality metrics, and advanced machine learning models to predict the optimal time for harvesting crops. By providing farmers with precise recommendations, the solution aims to maximize yields, minimize resource use, and streamline the entire harvesting process.
Understanding The Problem
Secondary Research & Competitive Analysis
Online research was done to understand how this is a real life issue, how people are being affected by it and what they are currently doing to solve it. It also provided a foundation of knowledge for the primary research. In order to construct a solid foundation for SmartFarm, we had to see what our competitors were already doing while looking out for market gaps.
User Story
Scenario - Onboarding and Integration
As a user, I want to easily onboard the SmartFarm Webapp to ensure a seamless integration with how I currently manage my crops.
Acceptance Criteria
- The onboarding process should be intuitive, allowing the user to input the farm details, crop type and any existing data sources.
Scenario - Real-time Monitoring and Predictions
As a user, I want monitoring of weather conditions and predictions for optimal harvesting times..
Acceptance Criteria
- The system should display live weather forecasts tailored to the user's location. The system should also provide predictions on optimal harvesting seasons.
UserFlow
This maps out the steps each user would follow to complete a specific task when using the proposed solution. It gives an overview of users steps from entry point to final interaction.
Visual Design
Landing Page and Authentication
Harvest prediction and feedback Integration
Key Takeaways and Nextsteps
Learnings
- User Research plays a significant role while making design decisions
- Understanding the problem and defining it well is key to arriving to a solution.
Nextsteps
We are proud of the progress made so far although much is still needed to be done. Designing, developing and improving a documentation site is one of those tasks that never ends as we'd keep iterating with feedback gifted to us by the users.