Unmanned aerial vehicles (UAV) shipped with on-board sensors have become an effective remote sensing (RS) tool in agriculture and environmental studies. However, the full potential of those platforms has not been yet achieved. 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 using small UAV for precision agriculture.
Apple orchard yield estimation is a very important predictor for fruit growers because it enables them to manage harvesting and post-harvesting recourses beforehand. This task is often achieved by experienced professionals with manual and ground instrumentation making it hard to achieve in a reasonable time a with a good accuracy.
This thesis proposes the development of a machine vision algorithm that computes the orchards flower density variability that can be afterwards used for yield estimation. This thesis will be carry out within the follow steps:
- Review previous works about yield estimation in apple orchards;
- Algorithm design;
- Experiments in the already available dataset.
- Aggelopoulou, A. D., Bochtis, D., Fountas, S., Swain, K. C., Gemtos, T. A., & Nanos, G. D. (2011). Yield prediction in apple orchards based on image processing. Precision Agriculture, 12(3), 448–456.
- Bargoti, Suchet & P. Underwood, James. (2016). Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards. Journal of Field Robotics.
- UAV enthusiast
- Willing for learning novel software and hardware tools
- Excited to work in robotics
Theme(s): Sensing & measuring