Overview
IFT (Intelligent Farming Tutor) is a machine learning-powered platform designed to support poultry farmers by providing real-time chicken image detection and health monitoring. Using advanced AI models, IFT helps farmers identify diseases, track growth, and optimize farming practices.
As the UI/UX designer for IFT, my role was to create a user-centric interface that simplified access to critical insights while accommodating the technological familiarity of diverse user groups, from small-scale farmers to large agricultural businesses.
Objectives
Design an intuitive interface for uploading and analyzing chicken images.
Visualize complex AI-generated insights in a user-friendly manner.
Ensure seamless integration with mobile devices for field use.
Build trust by making AI outcomes transparent and actionable.
Process
Research & Discovery
Conducted field visits to understand farmers’ challenges and workflows.
Interviewed poultry farmers to identify pain points, such as lack of timely disease diagnosis and monitoring tools.
Defined user personas, focusing on small-scale farmers with minimal tech exposure and large-scale farm managers.
Wireframing & Prototyping
Designed user flows emphasize simplicity, such as direct image uploads and quick feedback.
Created wireframes for core features, including disease detection results, growth tracking, and health recommendations.
Developed high-fidelity prototypes to validate designs through usability testing.
UI/UX Design
Used a clean, professional aesthetic with clear visual hierarchies to guide user focus.
Clear Design System
Designed dashboards with key metrics such as health status, disease probabilities, and recommended actions.
Integrated offline functionality for areas with limited internet connectivity in South Africa.
Testing & Iteration
Conducted usability tests with farmers from different regions to gather feedback on the interface and functionality.
Improved image upload features to ensure compatibility with low-resolution images.
Enhanced the clarity of disease probability visualizations and action recommendations based on user feedback.
Collaboration & Development
Collaborated with AI engineers to ensure accurate display of model predictions.
Worked closely with developers to optimize the app’s performance on low-spec devices.
Key Features
Image-Based Detection: Upload chicken images to identify health issues and receive disease probability scores.
Health Insights Dashboard: Visualized metrics such as weight trends, feeding patterns, and health status.
Actionable Recommendations: AI-driven suggestions for treatment and preventive measures.
Multilingual Support: Localized the app for various languages to cater to diverse users.
Offline Access: Enable farmers to use key features without internet connectivity.
Results
Adoption Rate: Reached over 10,000 active users within the first 6 months.
Improved Efficiency: Farmers reported a 50% reduction in the time taken to identify and address health issues.
Positive Feedback: 95% of users rated the app as easy to use and valuable for daily operations.
Reflection
Designing IFT was a fulfilling experience as it combined cutting-edge technology with real-world agricultural needs. The project emphasized the value of empathy-driven design in creating tools that are not only functional but also accessible to users from diverse backgrounds.
