Ignitia App by JetStyle | Climate Intelligence for Tropical Farmers

Intro

JetStyle has created digital and design solutions for green projects for over 6 years now. Our goal is to apply our expertise to help startups, enterprises and government agencies achieve the SDGs. We believe in the power of collaboration on the way to a safer and healthier future.

Our Green projects

When technologies and thorough research meet to improve lives of a huge community – this is where we’re most excited to participate and contribute our team’s best competencies. For our latest project we’ve mobilized our development powers and created an app for a weather forecasting service ignitia.

Yellow combine harvester operating in a golden wheat field under a blue sky.

Client background

Climate change has altered the landscape of various ecosystems, and it has significantly influenced the agricultural industry. Developing countries in the tropics are among those that feel the effects of climate change the most. Farmers in countries such as Brazil or Ghana, where a large share of the population depends on rainfed agriculture, struggle to adapt to the changing environment, and existing weather prediction models just don't help.

ignitia offers climate intelligence technologies for farmers across the tropics. The company is not just another weather forecast service – ignitia has its own award-winning climate intelligence approach.

Traditional weather forecasting is often not oriented at the challenges of tropical weather conditions — where weather is driven by smaller scale convection based storms and not cold/warm weather fronts. Therefore different scales of physics need to be considered for accurate weather predictions.

Ignitia’s team includes an R & D and AI laboratory that has invested more than 30 man-years into researching weather with a specific focus on rainfall and climate variability in tropical and subtropical climates such as Brazil and Africa. It’s designed to help farmers plan their agricultural activities according to the seasonal weather specificity. As ignitia uses custom weather models and AI, its forecasts are more accurate than global weather forecasting models.

High resolution: 30 times more granularity than global models

Near-term forecasts for small scales/ individual thunderstorms (nowcasts)

Primarily satellite-based data with little need for ground stations

Designed specifically for tropical zones

Advanced Machine Learning provides continuous improvement

Apart from basic weather forecasting features, ignitia provides relevant information for making essential agribusiness decisions such as crop selection, soil preparation, fertilization, weeding, application of pesticides, etc.

Climate protection effort by ignitia:

0.04%

reduction in fuel cost by avoiding unnecessary transport and inefficient use of machines on wet ground.

0.10%

reduction in machine wear by avoiding unnecessary transport and ensuring more efficient driving.

4.5%

reduction in the cost of fertilizers by identifying the best time to spray using the ignitia forecast.

0.14%

reduction in the cost of seeds by identifying the best time to plant with the ignitia forecast.

Challenge

We started our collaboration with ignitia in 2022.

Our task revolved around creating a functional app that would highlight all ignitia’s best features on one hand, and provide a smooth and convenient experience for ignitia’s users, on the other hand. At the start of the project we worked with ignitia’s design layouts and future app’s API.

As always, the product development approach was the best idea to deliver target experience to the audience as soon as possible. Let’s see how we did this:

Solution

The product development approach is all about iterative and incremental progress. It means we aim to deliver tangible benefit from the first sprint, and then develop the product with consideration of the client’s feedback. As we develop the product, we keep in touch with the market needs, and as a result, we get a product that’s relevant to the audience.

For ignitia, we started with reinforcing the client’s backend team with our React Native one. Together we built a powerful development tandem. React Native was the most relevant tool for ignitia’s task, as this framework allows creating apps that are compatible with both Android and IOS.

Another element of the task was to develop the app’s design system from the client’s drafts.

Weather app screens comparing before and after updates: two screens show weather in Palotinam and Brazil with gray skies, temperature, humidity, and wind; a third screen shows UPL notifications about thunderstorms, heavy rain, and spraying times with daily info navigation.

Smartphone screens showing a weather and farming app with temperature, precipitation, wind, and humidity; before screen shows a cloudy day with a sun icon, after screens show rain prediction, thunderstorm warnings, and detailed daily info for fertilizing and spraying times.

Comparison of weather notification app screens before and after redesign, showing improved layout, icons, and added background visuals.

Comparison of mobile app notification screens before and after redesign; before shows basic weather alerts in text blocks, after shows updated, visually enhanced weather alerts with icons and backgrounds for Fazenda São Paulo.

There was a lot to consider from the point of view of the client’s feedback. For instance, we tested various versions of how favorable spraying and fertilization periods would look in the app.

These content blocks are incredibly important for farmers. They are exactly what sets ignitia apart from urban-oriented weather forecasting services. That’s why they had to have the perfect UX and information display.

Series of mobile app screens showing the evolution from prototype to final version for a weather and spraying time forecast tool with temperature, humidity, warnings, and hourly advice.

Mobile app prototypes showing weather forecast and optimal pesticide spraying times, progressing from initial design with warnings to a final version with detailed notifications and suggested spraying schedules.

Together with ignitia’s team, we discussed various options: from using emojis or color schemes to adding brief text notes to weather indicators. App navigation was a crucial discussion point as well.

All iterations were tested and evaluated by our target audience – farmers from Brazil and Africa. Thanks to their feedback, we improved the app design so that it fits perfectly with our audience’s needs.

The app features two user flows with different subscription plans; B2C is perfect for separate farmers. When the app is used by corporate customers, the subscription includes advanced statistics.

Comparison of weather and farming apps for citizens and farmers showing humidity, storms, spraying times, and weather forecasts in Portuguese and English.

Four smartphone screens showing weather and farming app interfaces with weather forecasts, humidity alerts, thunderstorm warnings, and spraying schedules for Brazil and Fazenda São Paulo regions.

One of the app’s core features is well-working offline mode: we paid special attention to this feature as it was crucial for the needs of the product’s target audience. Farmers appreciated it a lot as offline mode allowed checking in with the weather forecast in a location where connection could be bad.

Weather app screens for Brazil showing rain and temperature info with no internet connection alert in Portuguese.

Just take a look at the graphics used to view best fertilization periods:

Weather forecast chart showing temperature, rain probability, and humidity per hour from 10h to 24h, with temperature peaking at 25°C around 15h, rain probability highest at 95% at 12h, and humidity peaking at 65% at 12h; includes rainfall outlook bar charts for next 4 weeks and 6 months, and a 4-week temperature outlook with expected and average values.

Hourly weather forecast showing temperature, rain probability, and humidity with corresponding icons from 10h to 24h, plus 4-week and 6-month rainfall and temperature outlook bar charts for September to early October.

We also made sure all the results of development work would be available for the ignitia team in the future, so we dedicated time to transfer and integrate all deliverables into the company’s development flow. As we were working on the development and design tasks, we created detailed guidelines so that the results of work are easily accessible for any ignitia’s team member. Also, ignitia had our full support when it came to publishing the apps into the stores.

Result

Close collaboration from stage 1 of the process, fast value delivery and business traction – this is what we managed to achieve together with ignitia’s team. After the start of work, we got proof that the product was relevant to the market. ignitia’s subscription plans turned out to be affordable and useful for both agricultural corporations and separate farmers across the targeted locations.

Grid showing logos and names of partners: MIT Solve, Resonance Science Foundation, Earth 300, Bartlett School of Sustainable Construction University College London, The Arctic Institute, World Economic Forum, and Decoded with With Love project.

ignitia has won several awards for its unique weather prediction approach, and now the team is working on applying the technology to new markets. This process requires upgrading the app’s infrastructure according to the needs of the new markets.

I’m so happy to be a part of this inspiring growing project. It’s a pleasure to work with the guys – and hopefully we will achieve even more success together in the future.

Julia Bozhko

Developer, JetStyle

Recently, ignitia took part in a project dedicated to enhancing maize production in the Eastern Region of Ghana, Africa. Two methods of the production process were evaluated. One part of the field was irrigated and fertilized according to suggestions by Ignitia; the other one was manufactured according to traditional agricultural practices. The results farmers received with ignitia spoke for themselves; the crops were much healthier than the test sample that used the traditional approach to growing.