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Designing a sugarcane harvest intellegence Dashboard

Digital Harvest

Feb 21 - May 21


Digital Harvest hired me to improve the user experience of their sugarcane harvesting software. Digital Harvest was mainly focusing on Central America and Indonesia with their harvesting software. This meant conducting user research with a very diverse and partly remote user group.

Getting to know the topic

To gather the most important information from the team I conducted an introductory workshop on Miro in which we worked out the main user groups and what the team thought their main concerns were.

Competitive analysis

Before going into user research I conducted a competitive analysis for Digital Harvest. This helped position my work better within the field of harvesting software. It turned out that Digital harvest had an opportunity to become the leading sugarcane growing software, if it improved their user experience and visibility. The only direct competitor that was still more successful at the moment was Gamaya.

User interviews

However, to understand the needs, circumstances and fears of the users better I had to plan user interviews. Carlos the marketing manager assisted me in this task. We worked out one research objective and three research goals:

Research objective

Find out how sugarcane harvesting software can help mills and growers with their work?

Research goals

What does the work of sugarcane growers and mills look like?

What are the main goals of sugarcane growers and mills?

What are the main obstacles for sugarcane growers and mills to reach their goals?

Carlos recruited various mill owners in Mexico and Panama and with his help I conducted 4 interviews in the Latin American sector. I was also connected with the Indonesian branch to conduct an interview with one of the people working in a big mill there. The cultural and environmental circumstances of the different locations had to be taken into consideration.

Research analysis

After this I analysed the research findings and with the help of Carlos created three Personas.

Problem statement and hypothesis

Having these three personas and the general research findings in mind it was also possible to draft our problem statement and hypothesis.

Problem Statement

Sugarcane mills and growers are aiming to increase their TCH (tons of cane per hectar) and TSH (tons of sugar per hectar) output. However, it is hard to predict this output between planting and harvest, as external factors like for example weather or field pests are hard to control.


We believe that by offering mills and growers an easy to use software that helps them to make more accurate harvest predictions, while having more control over potential threats to their field health, will help them increase their productivity.

Ideation workshops

Having defined the problem statement and hypothesis I conducted another series of workshops with the team in which we went through a series of design thinking exercises, like how might we, worst possible ideas and user stories. In these exercises we ideated on potential solutions to the problem and looked at them from different angles.


With the input from these exercises I was then able to start drafting my first prototype for the sugarcane dashboard. This prototype was still very rough and the main aim was to get something out for testing.

User Testing

With Carlos’ help I conducted two user testings. Before going into user testing I translated the prototype into Spanish, so that the test would actually take place in the right language.When conducting the two tests, one big issue came up. The two test participants were not really understanding the main navigation system. Instead they got lost all the time. At the same time they gave us a more precise picture of what their circumstances looked like when they would use a dashboard like Visible Harvest. It was clear that the Dashboard had to be created from a different angle, with a different navigation that was more simple and that I had to concentrate on the essentials first, before adding more complexity to it.

Information architecture improvement

To speed up the process I invited the team to another workshop in which we went through various card sorting exercises. These tasks helped us understand the information architecture better. It was clear that we had to stick to very simple concepts within the top navigation bar. The users were from various cultural backgrounds, with various different languages. Whereas the first prototype had focused on categories such as monitoring, predictions and optimisations these were very general and easily misunderstood concepts. Simplicity was key.

With the team we tried out all sorts of different combinations for the information architecture. Eventually we came up with the simple solution of the 4 top navigation categories being: Overview, Weather, Field Condition, Yield. Looking at it this way made things much easier to understand.

Further prototyping

Having this information architecture in mind I created a new prototype. As the CEO was eager to get started with the development of the dashboard we didn’t go into a second round of testing, but I refined the prototype, designed a mobile version, created a style guide and handed all the assets over to the main developer. While there was still a lot of research that could have been done for the improvement of the prototype, time restraints had to be considered.

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