PlantPal

Challenge

Houseplant care creates an unusual UX problem because the user is managing several small tasks that occur on different schedules and depend on the needs of individual plants.

A person may need to remember when each plant was last watered or fertilised, how much sunlight it requires, what soil it prefers, when it should be repotted, and whether visible symptoms indicate a health problem. The original persona scenario centred on exactly this problem: a busy plant owner forgetting care routines and becoming frustrated by conflicting information when something starts going wrong.

The challenge was therefore broader than designing a plant database. PlantPal needed to act as a lightweight care-management system that reduced memory load while still helping users understand their plants rather than simply telling them what button to press.

The experience also needed to support two different types of users. Beginners needed guidance around watering, soil, placement, plant identification, and diagnosing problems, while more experienced users needed tools for tracking their existing collection and discovering plants with compatible care requirements.

Constraints

The product was a UX prototype rather than a production application, so the design needed to communicate complex functionality without relying on a complete backend, working AI model, plant database, or live notification infrastructure. That meant features such as AI plant recognition and AI diagnosis had to be designed as believable user flows rather than technically implemented systems.

Another major constraint was cognitive load. The concept had a large potential feature set, but overwhelming users with plant data would work against the core purpose of the product. The interface therefore needed to prioritise daily actions and progressively reveal more detailed information only when it was useful.

The project also required attention to accessibility and interaction clarity. Important actions needed sufficient contrast, familiar affordances, large enough interaction targets, and recognisable navigation patterns so users could understand the app with minimal instruction. The final design explicitly used contrasting colours, spacing, icons, and grouped information to improve attention and perception.

Research

The design process began by defining target users, scenarios, user stories, conceptual metaphors, interaction types, and essential use cases.

The main persona represented a relatively inexperienced plant owner who enjoyed having indoor plants but struggled to maintain consistent care routines. This scenario helped establish the central value proposition: PlantPal should reduce uncertainty and memory burden through reminders, clear plant information, and AI-assisted support.

The user stories were then separated into beginner and expert needs. Beginner-focused requirements included:

• Recommended soil and soil quantities
• Watering guidance
• Watering reminders
• Placement based on sunlight requirements
• Guidance on when plants should be moved
• Identifying plant-health problems
• Suggested remedies
• Plant identification

More experienced users were considered through requirements such as maintaining detailed plant profiles and receiving recommendations for plants with similar care requirements.

Conceptually, PlantPal was framed using both a garden metaphor and a library metaphor. The garden metaphor represented the user's personal collection, while the library metaphor represented the growing body of information associated with each plant and any additional plants the user identified.

The interaction model was primarily instruction-based and menu-driven, with users navigating between Home, Care, Add, Plants, and Profile. Camera-based identification introduced an exploration component, while reminder configuration and data entry introduced response-based interaction.

Essential use cases were also mapped in detail for plant identification, recommendations, and custom plant profiles. These use cases helped define system responses and ensure that each major feature had a clear success condition rather than existing only as a visual concept.

Iterations

PlantPal went through several distinct interface iterations before reaching the final prototype.

The first prototype established the core structure around Daily, Garden, Care, and Scan. It successfully represented the basic product model, but the interface was shallow, visually repetitive, and missing important functions such as richer reminder controls, onboarding, and profile functionality. The heavy use of a single green colour also made the design feel flat and unfinished.

The second prototype expanded functionality substantially. It introduced daily and overdue tasks, a central add button, plant profiles, care logs, AI-assisted and manual plant entry, and a user profile. Functionally it was much closer to the intended product, but the layout and colour treatment still felt dated and insufficiently refined.

The third prototype focused more heavily on reducing visual complexity. Text was reduced in favour of imagery, larger interaction targets were introduced, and selected navigation states became more obvious. However, the navigation remained too small and visually ambiguous, and the colour system still lacked polish.

The final prototype consolidated the strongest ideas from the earlier versions into a more cohesive interface. Onboarding was expanded with a short introductory video and personalised setup questions. A softer green gradient replaced the harsher earlier colour treatment, while contrasting controls were used to draw attention to interactive states. Unnecessary information was removed to reduce clutter.

The final design also introduced a more complete home dashboard, recognisable iconography, clear navigation, task and reminder cards, and motivational feedback when users completed their daily care routine.

Key Features

• Personalised onboarding
• Plant-care experience and preference setup
• Pet-safety preferences
• Metric and imperial measurement options
• Daily plant-care dashboard
• Watering and care reminders
• Overdue task tracking
• Individual plant profiles
• Plant-care histories and logs
• AI-assisted plant identification
• Manual plant entry
• Automatic plant-detail population
• AI-assisted plant-health diagnosis
• Suggested remedies for detected problems
• Plant growth photography
• Per-plant image galleries
• Growth notes and historical comparisons
• Reminder frequency and notification controls
• Plant collection / Garden view
• Sorting by name, date added, last watered, and upcoming watering
• Search and filtering for larger collections
• Plant recommendations based on compatible care requirements
• Wishlist functionality
• Daily task streaks
• Weekly care statistics
• Social sharing of plant photos and progress

The Care experience was specifically designed around reducing user memory load. After choosing a plant, users receive a checklist of required actions and can complete a task with a single tap rather than repeatedly entering information.

AI diagnosis was integrated directly into this care flow. Users could photograph and crop the affected area of a plant before receiving possible problems and suggested remedies. The intention was not only to solve the immediate issue but also to gradually teach the user more about plant care.

Plant growth could also be logged photographically, creating a visual history designed to give users a tangible sense of progress and encourage continued care.

Reminder settings included configurable frequency and notifications, with explicit warning states when a user's settings could cause them to miss required care actions.

Final Deliverable

The final deliverable was a detailed high-fidelity mobile prototype covering the complete PlantPal experience from onboarding through daily use.

The flow included initial personalisation, the home dashboard, daily and overdue care tasks, individual plant care screens, AI diagnosis, growth logging, reminder settings, AI and manual plant addition, plant profiles, collection management, recommendations, search, filtering, streaks, and sharing.

The final design moved substantially beyond the initial green utility-style concepts into a cleaner and more intentional mobile experience built around reducing cognitive load, supporting recognition over recall, and helping users build confidence in caring for their plants.

Takeaway

PlantPal was one of the projects that made me think more deliberately about UX as a behavioural problem rather than simply a visual one.

The core problem was not that users lacked access to plant information. Most of that information is already available online. The problem was that it is fragmented, difficult to remember, inconsistent between sources, and disconnected from the actual routines of an individual plant owner.

The strongest design decisions therefore came from reducing that burden: surfacing only today's relevant tasks, using recognition instead of recall, providing one-tap completion states, storing care history, automating repetitive data entry, and making warnings explicit rather than ambiguous.

The iterative prototype process also demonstrated how much visual polish affects perceived usability. Earlier versions technically contained many of the required functions, but unclear navigation, generic iconography, dense text, and flat colour treatment made them feel less intuitive. Simplifying the interface and establishing stronger hierarchy made the same underlying product considerably easier to understand.