Thesis Code: 20011

Thesis Type: M.Sc. thesis in Machine Learning, Data Science, Computer Science, Mathematics, or equivalent

Research Area: Data Science for Industrial and Societal Application


  • Knowledge of Python
  • Software development skills
  • Basic concepts on data science, concerning data analysis, processing and machine learning
  • Basic concepts on image processing


Monitoring phenology of agricultural plants is a critical understanding in precision agriculture. Vital improvements can be achieved with precise detection of phenological change of plants which would henceforth improve the timing for the harvest, pest control, yield prediction, farm monitoring, disaster warning etc. Many countries across the world have been developing initiatives to build national agriculture monitoring network systems, since inferring the phenological information contributes to a better understanding of relationships between productivity, vegetation health and environmental conditions.

The objective of this thesis consists in the study and implementation of machine learning algorithms useful for plants phenology monitoring. The proposed algorithms will be trained using open datasets and proprietary datasets. The candidate will have both the task of creating the dataset and studying and evaluating the best algorithms to apply for the case of study.

The candidate is required to implement machine learning algorithms, with the exploratory possibility of deep learning algorithms using popular framework (TensorFlow, PyTorch, Keras, etc..).

Contact: send a resume with attached the list of exams to specifying the thesis code and title.