Master thesis proposal: Deep learning for detection of coffee berry disease
Climate change brings along problems for farmers around the world. Assisting agriculture using AI to make farming more resilient may become an important step towards adapting to the new normal. Limiting the uncertainties and risks in food production can substantially improve the situation for many farmers, and increases the availability and robustness of food supplies. Deep learning is one of the most important tools in the AI toolbox, and has shown impressive results on many tasks on data of different modalities, not the least for computer vision.
Coffee berry disease (CBD) affects Arabica coffee plants, and is caused by the fungus Colletotrichum kahawae. CBD is one major factor hindering coffee production on the African continent. The spread of CBD is highly dependent on rainfall, temperature, and humidity, and has been affected by climate change.
In this master thesis, you will develop and train predictive models for detecting coffee berry disease using computer vision and deep learning. The work will include training models using images of infected and uninfected plants and berries. Techniques will include convolutional neural networks, transfer learning, domain adaptation, and few-shot learning. You will work in close collaboration with our deep learning research group and experts in agriculture from Mpendakazi Agribusiness in Tanzania. The work requires students with an excellent skill set within machine learning, image processing, and statistical inference. You will be expected to start out with a literature study, and then start with simpler models and eventually extend or develop upon more advanced solutions. As this is a master thesis project with a research organization, we will help you reach a high level of research excellence, and a successful project may result in writing a joint research paper in addition to the master thesis.
- J. Arun Pandian, V. Dhilip Kumar, Oana Geman, Mihaela Hnatiuc, Muhammad Arif, and K. Kanchanadevi. Plant Disease Detection Using Deep Convolutional Neural Network. https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwj3iILp75P6AhVIposKHVayB_oQFnoECAsQAQ&url=https://www.mdpi.com/2076-3417/12/14/6982/pdf&usg=AOvVaw1tQg7vl--nkLOvAMwEyGCM
- Jahnavi Kolli; Dhara Mohana Vamsi; V. M. Manikandan. Plant Disease Detection using Convolutional Neural Network. https://ieeexplore.ieee.org/document/9673493
- Experience of implementing machine learning models.
- Courses in mathematical statistics, probability theory or similar.
- Programming skills. Preferably with some experience of relevant frameworks such as Pytorch, Keras, or Tensorflow.
RISE Center for Applied AI Research connects AI research within RISE Research Institutes of Sweden. We are around 60 researchers working on machine learning related tasks within different fields including natural language processing, computer vision and network analysis.
RISE is Sweden’s research institute. Through our international collaboration programmes with industry, academia and the public sector, we ensure the competitiveness of the Swedish business community on an international level and contribute to a sustainable society. Our 2,800 employees engage in and support all types of innovation processes.
The Deep Learning Research Group is connected to RISE Center for Applied AI Research working on modern AI and machine learning. We have solid expertise in the field of deep learning, computer vision, federated learning, uncertainty quantification, and privacy-preserving machine learning.
Keywords: machine learning, deep learning, computer vision, climate change, smart agriculture
Welcome with your application!
If you have questions, please contact firstname.lastname@example.org. We will interview suitable candidates as applications are received. Please send in your application as soon as possible. Last day of application is 7th of November. Note that all applications for this position must go through our recruitment system Varbi. We do not accept applications by email.
Göteborg eller Lund
Visstidsanställning 3-6 månader
Student - examensarbete/praktik
2022-11-07Skicka in din ansökan