Grow App

  • Team composition:
  • Fullstack engineer
  • QA
  • System structure:
  • iOS application

  • Android application
  • Cloud server
  • Timeline:
  • 4 month
  • Industry:
  • Agriculture
Grow app
Challenge

The client decided to diversify his business and use his agricultural knowledge to help people better manage their growing spaces.

There are virtual assistants in many industries, but in agriculture, this application is one of the few. The client initially started to develop the project in-house but soon realized that he could not cope on his own and asked us for help.

 

Agriculture mobile app
Grow App is a virtual agronomist in your mobile phone
Features:
  • Creating virtual tailored growing spaces

  • Choosing a featured plant or creating a new one
  • Diagnosing issues with the plants using Machine Learning
  • Advanced daily push notifications
  • Online seeds and other farming items store
  • A step-by-step guide to growing plants
  • Access to experienced agronomists
Premium
Solution

The idea behind the app is a virtual agronomist assistant at your fingertips. Anybody can grow plants with the help of experienced agronomists. So communication with the specialists in the agriculture field from the customer side was one of the most essential for the project.

The development started with a tight budget and deadlines to launch an MVP, but new features are constantly being added. It was very important to put up a proper architecture in both UX/UI and technical sides to support further extensions of the functional. We started with Firebase services to cover backend tasks, and React Native for mobile applications - we often use this approach to start projects fast and get to MVP launch as soon as possible. Currently, the backend of the system emerged as a microservices' architecture system. Such services include machine learning algorithms, video calls, e-commerce, and so on.

Computer Vision solution is a killer-feature to diagnose plant disease using a mobile phone camera. The Machine Learning model has been initially built with open source plants’ disease database and constantly improving by the clients’ team of agronomists.

 

Vegetation technology redefined

 

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