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 by taking pictures of its surroundings. The robot will process the data and show the amount of ripe and sunburned apples via an app or desktop site to the user, who is the keeper of the orchard as well as the farmers that work there. The farmers can use this information quite well since the harvesting of fruit can be done based on the data. Furthermore, the farmers 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 farmers can optimize the sprinkling by looking for the location with the most apples that have suffered from the sun, and thus where they can most efficiently place their sprinkler.

Also, Smart Orchard can be used to find trees that have too many unripe apples. Trees that have too many apples must get pruned so that all of the energy of the tree can be used to produce bigger, tastier apples. Since the robot will notify the farmers how many ripe and unripe apples there are at a given location, the farmers can go to that location directly without having to look themselves. This also increases the efficiency with which they can do their job.

Objectives

The objectives of this project can be split into the main-objectives and secondary-objectives.

Main

- The system should be able to recognize apples via a camera.
- The system should utilize a neural network for classifying apples.
- The system should make a model of the complete farm.
- The system should enable the user to interact with the system.
- The system should enable the user to give feedback on the decisions which the system makes.
- The system should be able to work autonomously.
- The system should create a list of instructions for workers.

Secondary

- The interface of the system should be clear and intuitive.
- The system should be an improvement in terms of efficiency and production of the apple orchard.
- The system should be coupled to a 'picking robot' of some sort relatively easy.

Users

The main users that will benefit from this project are keepers of apple orchards, who will be able to divide tasks 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 or 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

Regarding the technical aspect of the project, the group can be divided into 3 subgroups. A subgroup that is responsible for the image processing/machine learning, a subgroup that is responsible for the graphical user interface and a subgroup that is responsible for configuring the hardware. In the meetings the subgroups will explain the progress they have made, discuss the difficulties they have encountered and specify the requirements for the other subgroups.

The USE aspect of the project is about gathering information about the user base that might be interested in using this technology. This means that we will need to set up meetings with owners of apple orchards to get a clear picture of what tasks the technology should fulfill. Furthermore, we shall need to inspect if the project is feasible from a business perspective. For this we will need to make an accurate production-cost approximation of the project and an approximation for the sale price of the project.

Planning:

Week 2

Create datasets

Preliminary design of the app

First version of the 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 the possible robot

Setup of Raspberry Pi

Week 4

Finish map of user interface

Go to an orchard for interview

Implement user wishes into the rest of the design

Week 5

Implement the 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 that will mark a significant point in the 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

Logbook

Week	All	Sven	Willem	Rick	Rik	Beau
1 (3-2 / 9-2)	Wiki page	Neural network training database[8], more in depth defining/researching on subject[4], wiki[2]		Designing neural network[10], wiki[6]	Neural network training database[10], References regarding neural network and fruit recognition[4], wiki[1]	Specifying objectives [2], sketching ideas of desktop application [8]
2 (10-2 / 16-2)	Meeting/Working with the whole group [8]	search and ordering necessary raspberry pi components[1], interview with farmer/user[], wiki[]		Object detection with Tensorflow [8]	References regarding neural network and fruit counting [4], wiki[4],	Following a tutorial for web application [12]
3 (17-2 / 23-2)					Animation[8], wiki[2]
4 (2-2 / 8-3)
5 (9-3 / 15-3)
6 (16-3 / 22-3)
7 (23-3 / 29-3)
8 (30-3 / 2-4)

[ ] = number of hours spent on task