PRE2019 3 Group14: Difference between revisions
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== State-of-the-art == | == State-of-the-art == | ||
In this project, we focus on a deep learning system. | |||
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[https://www.sciencedirect.com/science/article/pii/S2589721719300182]. For example: to differentiate weeds from the crops[https://elibrary.asabe.org/abstract.asp?aid=7425], for forecasting water resources variables[https://www.sciencedirect.com/science/article/pii/S1364815299000079] and to predict the nutrition level in the crops[https://ieeexplore.ieee.org/abstract/document/1488826]. | |||
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[http://www.ijirset.com/upload/2019/january/61_Surveying_NEW.pdf]. 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[https://www.ijrte.org/wp-content/uploads/papers/v8i3s3/C10301183S319.pdf]. | |||
== Approach == | == Approach == |
Revision as of 18:31, 6 February 2020
Team Members
Sven de Clippelaar 1233466
Willem Menu 1260537
Rick van Bellen 1244767
Rik Koenen 1326384
Beau Verspagen 1361198
Subject:
Sven
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
In this project, we focus on a deep learning system.
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
Reseach everything necessary for the wiki
Update the wiki
Week 3
Find more data if necessary
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
Highest priority functions of the user interface are present
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
Rick
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