MSc thesis subject: A machine vision/learning approach for yield estimation in a wide spinach (Spinacia oleracea) field

Unmanned aerial vehicles (UAV) shipped with on-board sensors have become an effective remote sensing (RS) tool in agriculture. They have been used mainly for image surveying and there are still many helpful aerial RS application to develop. Working with UAV it’s an added valuable point for any professional who wishes to succeed in this competitive market. Herein, you will have the opportunity to work in a novel and ambitious project with UAV applied to precision agriculture.

The goal of this work is to use computer visions and machine learning approaches for crop yield estimation through high-resolution UAV imagery. This thesis will be carry out in the follow steps:

  1. Review previous works in machine vision and learning techniques applied to agriculture;
  2. Design an approach to solve this problematic;
  3. Experiments with the already available dataset.


  • Andreas Kamilaris, Francesc X. Prenafeta-Boldú, Deep learning in agriculture: A survey, Computers and Electronics in Agriculture, Vol. 147, pp. 70-90, 2018.
  • E. Hamuda, M. Glavin, and E. Jones, “A survey of image processing techniques for plant extraction and segmentation in the field,” Computers and Electronics in Agriculture, vol. 125, pp. 184 – 199, 2016.
  • N. Yu, L. Li, N. Schmitz, L. F. Tian, J. A. Greenberg, and B. W. Diers, “Development of methods to improve soybean yield estimation and predict plant maturity with an unmanned aerial vehicle based platform,” Remote Sensing of Environment, vol. 187, pp. 91 – 101, 2016.


  • UAV enthusiast
  • Willing for learning novel software and hardware tools
  • Excited to work in robotics

Theme(s): Sensing & measuring