Introduction

The Greener App wasn't built in a day! This documentation page shows all of the different steps that we took throughout the course to ideate, design, test, and improve our application through various stages. This section is designed for collaborators so that they can access our ressources. From start to finish, we've broken down the process below.

Individual exploration

Here you will find our personal preliminary research conducted prior to Greener. This will give you insights as to how we came up with the idea for the App and the technologies we researched.

Amanda Anna Ariana Leo Zexu

When a picture of a sick crop is sent to Greener servers, it is processed by the AI which will analyze patterns to predict with over 90% confidence level the pest affecting the plant.

The AI is trained on a dataset and is improving as users send new pictures. The data is sent to the servers and analyzed by deep neural networks that will come up with a probability and a disease name. The AI is > 90% certain that this is the disease affecting the plant and gives the answer instantly. The AI uses classification to identify the patterns left by the pests or disease on the plant leaves. Colors, forms, shapes, regularity, … are analyzed.

You will find here more information about the technology we use for the algorithm

Project definition

Given that farmers in isolated areas have increased access to the internet and therefore to the tools it can provide, we decided to create a mobile application that targeting this user group.

During our research, we found that there is a need for farmers to gain access to precise advice that will save them significant amounts of crop loss, especially in crops diseases detecting. Meanwhile, services like ours allow for the democratization of the access to knowledge by providing low-cost video training so that farmer's learn best practices from experts.

Therefore, overall the goal of the project is to empower agriculturers to optimise their crops with accessible, high-tech solutions at the lowest cost possible.

Wireframe

We started using Figma to build layouts that outline the specific size and placement of structural elements and app features.
We have focused on our "scan" feature which allows users to take/upload a photo of their crops so as to detect and verify the diseases that may occurred. Then, we add more features such as the possibility of searching one specific disease by their name and the possibility of downloading the according treatment videos.

Mockups and tests

In order to get end-users' feedback more easily, we made up our own mockups to demonstrate our concepts. It is a medium-fidelity representation of our project which is designed through Figma website. For the interface, we decided to focus on the aspect of "user-friendly" as our potential users may not be familiar with technology or be afraid of the "advanced app" concept. So the application must be easy to use but fully functional using AI technology at the same time.
If you are curious, click here to see our users' feedback on Notion.
Below you will find our first mockup.

Below you will find our second (revised) mockup.

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