PRE2019 3 Group14

Team Members

Sven de Clippelaar 1233466

Willem Menu 1260537

Rick van Bellen 1244767

Rik Koenen 1326384

Beau Verspagen 1361198

Subject:

Due to climate change, the temperatures in the Netherlands keep rising. The summers are getting hotter and drier. Due to this extreme weather fruit farmers are facing more and more problems harvesting their fruits. In this project we will develop a robot that will help apple farmers, since apple farmers especially have problems with these hotter summers. Apples get sunburned, meaning that they get too hot inside, so that they start to rot. Fruit farmers can prevent this by sprinkling their apples with water, and they have prune their apple trees in such a way that the leaves of the tree can protect the apples against the sun.

This is where our robot, the Smart Orchard, comes in. Smart Orchard focuses on the imaging of apples. The robot will be used for two tasks, recognizing ripe apples and looking for sunburned apples. First, Smart Orchard will be trained with images of ripe/unripe/sunburned apples in order for it to understand the distinct attributes between them. It will then use this knowledge to scan the apples in the orchard. The robot will process the data and can show via an app or desktop site the amount of ripe and sunburned apples to the user, the farmer. The apple farmer can divide its orchard/terrain into a grid. The robot will then show into the app/desktop site the amount of ripe apples and the sunburned apples to the farmer. The farmer can use this information quite well, the harvesting of fruit can be done on the basis of the data. Furthermore, the farmer cannot sprinkle their whole terrain at once, because most of the time they have just a few sprinkler installations. With the data collected by the robot the farmer can optimize the sprinkling by checking on the app/desktop site, which grid has the most apples, which suffered from the sun and thus where he can place its sprinkler the best. Also he can see where he pruned the apples trees too far or where he can place protecting screens to protect their apples.

Objectives

Beau

Users

The main users that will benefit from this project are keepers of apple orchards, who will be able to divide task more efficiently. The farmers that work at the orchard also benefit from the application, since it will tell them where to go, such that they do not have to look for bad apples themselves.

State-of-the-art

Artificial neural networks and deep learning have been incorporated in the agriculture sector many times due to its advantages over traditional systems. The main benefit of neural networks is they can predict and forecast on the base of parallel reasoning. Instead of thoroughly programming, neural networks can be trained[1]. For example: to differentiate weeds from the crops[2], for forecasting water resources variables[3] and to predict the nutrition level in the crops[4].

It is difficult for humans to identify the exact type of fruit disease which occurs on the fruit of plant. Thus, in order to identify the fruit diseases accurately, the use of image processing and machine learning techniques can be helpful[5]. Deep learning image recognition has been used to track the growth of mango fruit. A dataset containing pictures of diseased mangos has been created and was fed to a neural network. Transfer learning technique is used to train a profound Convolutionary Neural Network (CNN) to recognize diseases and abnormalities during the growth of the mango fruit[6].

Approach

Willem

Planning:

Week 2

Create datasets

Preliminary design of app

First version of neural network

Research everything necessary for the wiki

Update the wiki

Week 3

Find more data

Coding of app

Improve accuracy of neural network

Edit wiki to stay up to date

Get information about possible robot

Setup of Raspberry Pi

Week 4

Finish map of user interface

Go to orchard for interview

Implement user wishes into the rest of the design

Week 5

Implement highest priority functions of the user interface

Week 6

Reflect on application by doing user tests

Reflect on whether the application conforms to the USE aspects

Week 7

Implement user feedback in the application

Week 8

Finish things that took longer than expected

Milestones

There are five clear milestones which will mark a significant point in a progress.

• After week 2, the first version of the neural network will be finished. After this it can be improved by altering the layers and importing more data to learn from.
• After week 4, we have taken an interview with a possible end user. This feedback will be invaluable in defining the features and priorities of the app.
• After week 5, the highest priority functions of the user interface will be present. This means that we can let other people test the app and give more feedback.
• After week 7, all of the feedback will be implemented, so we will have a complete end product to show at the presentations.
• After week 8, this wiki will be finished, which will show a complete overview of this project and its results.

Deliverables

• A system prototype that is able to recognize ripe apples out of a dataset containing unripe apples and apples with diseases for orchard farmers.
• The wiki page containing all information regarding the project.
• A presentation about the product

Who will do what?

Image recognition with CNN: Rick and Rik

User Interface (app or desktop application): Beau and Willem

Hardware: Sven and Willem