Segmentation of Agricultural Operations by Machine Learning

Machine Learning has been found successful for various real-life applications including medical, finance, autonomous cars, text recognitions, and many others. Similarly, machine learning-based methods have been effective in addressing agricultural problems.

The main goal of this PhD project is to develop a machine-learning model that can segment different problems in the agricultural industry. The specific objectives of this project would be:

·      Detect and segment agricultural operations in a real agricultural environment.

·      Optimize the performance of state-of-the-art machine learning models.

·      Develop a robust machine learning model that can be validated in different scenarios.