Plant Growing App Development

Team composition:
Fullstack engineer
QA

System structure:
iOS application
Android application
Cloud server

Timeline:
4 month

Industry:
Agriculture

Challenge

The client decided to diversify his business and use his agricultural knowledge to help people better develop agriculture with the app.

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

The main challenge was that the client had a lot of ideas, but the budget and terms were limited. Therefore, the first and main problem was the prioritization of tasks and deadlines, and the work of the MVP. After that, we made an adequate roadmap for further phone farming app, which we follow.

Technology Stack for Agriculture App Development

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

Solution

The idea behind the concept to build a farm 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 agriculture mobile app development.

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.