‘Ceres’ Plant Health Database

Specialists at OMAFRA (Ontario Ministry of Agriculture, Food and Rural Affairs) collect crop protection data, such as pest counts, locations. temperatures, and treatments to generate insights and help growers make informative decisions. The data collected is unstandardised and scattered across lots of data sources and databases making it hard on specialists to deliver timely information to growers. Growers’ work is very time sensitive; A week delay could cause irreparable crop damage or loss. In 2017, one pest caused roughly 80 million dollars’ worth of crop loss.

The goal is to understand why the current processes take specialists too long to generate insights and to create a solution that will help specialists provide timely, and informative recommendations to growers/farmers. This way, farmers can better manage their pest issues and save their crops from any economic loss.

I was the UX specialist in the team working alongside a Product Manager, a Software Developer and our government partners.

Our users are crop protection specialists who work at OMAFRA, like Tracey Baute. Tracey is an Entomologist who specialises in studying insects. She leads a pest trap network, where she works closely with growers to monitor pests like Western Bean Cutworm.

External Benefiters are Growers, Crop Consultants and Grower Organizations. After interviewing the specialists, we decided to talk to the external benefiters to help us define the product’s vision. They are potential users in the far future, but further user research will be needed by then.

The data roadmap defined from Tracey’s interview has five phases. It starts with collecting the data and moves into each step, ending with a visual or a written output. After interviewing 11 Specialists, we learned that the data doesn’t always move in one specific order as it was illustrated in the data roadmap for Tracey’s journey. The arrows made the roadmap format limiting, so we created the data lifecycle diagram to better capture the specialists’ process.

As you can see in the diagram below, we clustered the pain points into six themes: data access, data standards, lack of resources, manual processes, and incorrect tools.

Based on our research, we believed the most significant issues occurred during the data collection phase. Mainly, because the data is collected in different formats and sometimes with inaccurate data, errors, and/or duplications. Inconsistent data standards requires manual processes, like inputting, reformatting and cleaning the data. As a result, this makes it hard on Specialists to deliver timely insights to growers to save their crop from any damage or loss.

The product vision is to create a flexible and extensible data management system called Ceres. To standardise collected data and reduce data cleaning, Ceres will allow specialists to customise their own templates, build fixed blocks and set specific data types in a centralised database. Ceres will also allow specialists to enter data directly, import bulk data, generate forms, and connect to other applications via APIs. The Alpha phase was released on Oct 21, 2020. To learn more about the released product, please watch the demo below. 

We estimate this solution to save specialists 30% of their time, which can be used on value add activities, such as problem solving and collaborative research. 

In three months, we conducted three rounds of remote moderated usability testing to identify pain points, uncover opportunities and learn about our users’ preferences and behaviours.

For Alpha, we focused on testing for Desktop since all of the specialists confirmed that they will structure their datasets using desktops or laptops. Mobile and Tablet could be used in the field for data entry. However, this might require offline app functionality in the future, since lots of rural areas have slow to no internet connection. Ceres will generate forms based on the datasets the specialists will build and can be easily connected to any application.

Overall feedback was positive. Some terms given to features and user interface elements were either difficult for users to understand or had different meanings in agriculture. For example, ‘field’ in computing speaks to a data entry field, and in agriculture, it speaks to a piece of land. Moreover, users identified new product opportunities and nice-to-haves, such as the ability to merge datasets and track changes done by collaborators.