Beneath the weather: uncovering the story inside crop and climate data

A UX case study about turning predictive science into everyday clarity.

Ukko Agro is a Toronto-based ag-tech company that helps farmers and retailers make better crop decisions using data. Its platform combines weather information, crop models, and data from both on-farm and virtual weather stations to predict how crops will grow and what risks they might face—up to 14 days in advance. This helps users know exactly where and when to apply important products like fungicides and insecticides, saving both time and resources.

When I joined the team, the goal was to redesign the existing mobile app so it could connect retailers and growers more smoothly and handle large amounts of data safely and efficiently. At the same time, the app needed to be easier to understand for users who often found predictive information confusing. My role was to make the experience clear, simple, and genuinely helpful—while adding new features that supported their day-to-day work in the field.

Framing the Challenge

Ukko Agro needed a mobile enterprise app that could connect global retailers with their growers and agronomists in a single, secure ecosystem. Each organization required access to specific farm and field data, while growers could only view information related to their own fields and weather stations.

Privacy and scalability were critical—the platform had to protect sensitive agricultural data while supporting everything from a single farm to thousands of users across retailers, growers, and connected weather stations.

On top of that, the product had to succeed in an industry known for its slow adoption of new technology. Building trust through clarity and ease of use was just as important as the technical architecture itself.

Learning the language of the field

During my first few weeks on the project, I focused entirely on research — immersing myself in the unfamiliar world of agriculture. The goal was to understand both the people and the science behind Ukko Agro before making any design decisions.

Those weeks were a deep dive into crop growth stages, weather stations, and disease forecasting. I studied how climate and field data interacted and how these insights informed real farming decisions.

I created personas, identified key stakeholders, and mapped the existing product to understand how information moved between retailers, producers, and agronomists. By the end of this stage, I had a general picture of who was involved and how they interacted with the system — but it was only the beginning of understanding what they truly needed.

Here are the personas developed during the early research phase to understand how users interact with predictive insights. They represent key roles across the agricultural ecosystem — including farmers, agronomists, and retailers — and were essential in shaping the redesign strategy. Each persona summarizes real behaviors, challenges, and needs uncovered during interviews and field observations, serving as a quick reference for key stakeholders throughout the design process.

Think slowly

Once I had a basic understanding of the product and its users, I decided not to rush into design. The agriculture space was complex, and every answer uncovered more layers I needed to understand.

I focused on farmers and their everyday routines — how they noticed early signs of disease, how much spraying cost them, and what actually happened once a field needed treatment. I wanted to see how they kept track of information: did they use an app, jot notes on paper, or rely on memory? What worked about that, and what didn’t?

By asking these questions and sharing early prototype versions, I was able to uncover pain points and opportunities that our team could address through design and functionality. These insights shaped how the app could better fit into real workflows rather than asking users to change them.

Through interviews with growers, retailers, agronomists, and sales teams, I began to trace how decisions were made and where information flowed between people. The existing app structure already mirrored much of how users worked in the field, but it didn’t always make those connections clear. Some information felt hidden, key actions were easy to miss, and adding new features risked creating even more complexity.

What became clear was that a few thoughtful changes — better organization, clearer visibility, and a stronger link between data and action — could make the system feel far more natural to use. Small adjustments could turn something functional into something that truly fit the rhythm of how people worked.

The team around me made this process possible. Product managers, scientists, and engineers gave me time and trust to explore, ask questions, and validate ideas. Their openness helped me understand how weather data, crop science, and real farming routines needed to connect — and what had to change in the design to make that happen.

The second brain in my design process (after sticky notes and Mural). It helped me connect retailers, farms, and weather stations — and reminded me that the messier the whiteboard, the clearer the thinking.

From notes to navigation

Once…

Here is my next phase of the process in information organisation, including how and where users would access it:

During the clustering and grouping phase, I created personas to thoroughly understand the specific needs and constraints of users. Additionally, I conducted a thorough analysis of crop modeling to gain a deep understanding of how growers currently use data and how the app could fit seamlessly into their existing processes to aid in crop growth. Furthermore, I considered various elements of farmers’ daily work, such as the practicality of using the app while operating a tractor or with limited hand mobility, to ensure the app’s usability and effectiveness in real-world scenarios.

1. As the sole developer of this project, my key performance indicator was my ability to independently build an app from the ground up with little guidance. I had to be self-motivated and able to prioritize important features.

2. Another key performance indicator for me was the adoption rate of the app within the agriculture industry. I knew that it would be a challenge because this industry is known for being slow to adopt new technology, but I was extremely proud when the app was met with enthusiasm and interest from users, and exceeded all expectations in terms of adoption.

I began the design process by conducting a thorough analysis of the agriculture industry, with a focus on the specific crops our modeling was covering. To gain a deep understanding of the farmers’ needs, I delved into their farming processes, including how they care for their crops, how they communicate with other farmers and experts, and their current methods for monitoring weather and temperatures. Additionally, I identified stakeholders and users, as well as their pain points, through numerous interviews with retail organizations, agronomists, and salespeople. This research helped me to not only gain a deeper understanding of the industry but also inform the development of a new app architecture that addresses the specific needs and concerns of those who will be using it.

Tools I used in my design thinking process:

Feasibility Chart

Kano Method

MVP

Interviews analysis, and feasibility charts:

The task of building the app from scratch and the tight deadlines presented a significant challenge. To overcome this, I collaborated closely with the front-end and back-end teams to identify and implement efficient processes and determine which portions of the existing code from libraries could be repurposed to expedite development without compromising on user experience and overall objectives.