Titulo Estágio
Deep Learning model for plant diseases detection
Áreas de especialidade
Sistemas Inteligentes
Local do Estágio
DEI-FCTUC
Enquadramento
ne of the greatest technological advances was the domestication of plants during the agricultural revolution some 8 to 12,000 years ago in various locations around the world which allowed the evolution of the human population to the present remarkable 7.7 billion people. This development has put colossal pressure on agricultural technology, which is now under immense challenges due to infectious diseases and pests spread by globalization and aggravated by climate change.
Thus, great benefits can arise from the more accurate and rapid detection of plant diseases, which is increasingly being provided by sensors that obtain real-time information from crop planting sites.
In this project we intend to develop learning models, based on deep learning, that allow the identification of species and diseases in plants using images.
Objetivo
In this internship the student should conceive and develop learning models capable of handling the plant images and data. This activity includes the following actions:
- Dataset definition and pre-processing
- Specification of the deep learning model and of the algorithm
- Deployment and test of the conceived architecture
- Improvement of the model using real tests
Plano de Trabalhos - Semestre 1
- Analyse data and define/construct dataset (1 month);
- Analyse possible learning architectures (1 month);
- Define the deep learning architecture of the system (1 months);
- Start implementing the learning system (1 month)
- Write intermediate report (1 month);
Plano de Trabalhos - Semestre 2
- Implement the learning system (2 months);
- Test and evaluate performance (2 months);
- Write final report (1 month);
Condições
This work will be carried out in the Laboratory of Neural Networks (LARN) of CISUC, where there will be a regular supervision and feedback on the behalf of the supervisor and co-supervisor.
Familiarity with machine learning and data mining algorithms and software tools are essential. Participating students will acquire valuable knowledge and experience with model building and data science by mining massive datasets, which skills are currently in high demand for various technology employers due to the relevance to various applications.
Observações
Logistics @Laboratory of Neural Networks (LARN)
DEI-FCTUC
Orientador
Bernardete Ribeiro, Catarina Silva, Joana Costa, N.Phong
bribeiro@dei.uc.pt 📩