Thesis code: 20007

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



Misdiagnosis of the many diseases impacting agricultural crops can lead to misuse of chemicals leading to the emergence of resistant pathogen strains, increased input costs, and more outbreaks with significant economic loss and environmental impacts. Current disease diagnosis based on human scouting is time-consuming and expensive, and although computer-vision based models have the promise to increase efficiency, the great variance in symptoms due to age of infected tissues, genetic variations, and light conditions within trees decreases the accuracy of detection.

The objective of this thesis consists in the study and implementation of machine learning algorithms useful for plants pathology detection. The proposed algorithms will be trained using open 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.