PRE2019 3 Group14: Difference between revisions
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== State-of-the-art == | == 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 | 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 or 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]. | 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[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]. | ||
Revision as of 13:57, 13 February 2020
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].