PhD defence: Anders Krogh Mortensen

Anders’ PhD defence presentation can be seen in the video below.

PhD defense of Anders Krogh Mortensen for the thesis “Estimation of Above-Ground Biomass and Nitrogen-Content of Agricultural Field Crops using Computer Vision”.

The thesis investigates yield estimation derived from RGB images and coloured 3D point clouds. Image segmentation methods based on image processing, handcrafted feature extraction, and deep learning is investigated. Furthermore, a novel method for segmenting lettuce in 3D coloured point clouds is proposed. Several yield models based on the segmented crops are investigated.

The research findings have shown that recent advances in deep learning can be transferred to segmentation of (mixed) crops. It was further shown, that (simple) growth models can be improved using crop coverage to explain the local variations in the crop.

PhD student:
Anders Krogh Mortensen: http://pure.au.dk/portal/en/anmo@agro…

PhD Supervisors:
Associate Professor: René Gislum: http://pure.au.dk/portal/en/rg@agro.a…
Professor Henrik Karstoft: http://pure.au.dk/portal/en/hka@eng.a…

This PhD project is part of the VIRKN project supported by a grant from the Green Development and Demonstration Program (GUDP) granted by the Danish Ministry of Environment and Food and the Future Cropping project supported by a grant from Innovation Fund Denmark.
– VIRKN: http://mst.dk/erhverv/groen-virksomhe…
– Future Cropping: https://futurecropping.dk/
– GUDP: http://mst.dk/erhverv/groen-virksomhe…
– Innovation Fund: https://innovationsfonden.dk

PhD defence: Mikkel Fly Kragh

Today, Friday, April 20th 2018, Mikkel Fly Kragh successfully defended his PhD thesis titled Lidar-Based Obstacle Detection and Recognition for Autonomous Agricultural Vehicles.
Mikkels PhD study was part of the now completed SAFE project, which sought “to develop autonomous agricultural machinery that will be able to harvest green biomass and cultivate row crops – without animals or humans being exposed to any type of safety risk”. Mikkels work focused on detecting and recognizing obstacles using lidar-sensing and sensor fusion with cameras. He achieved this by both incorporating state-of-the-art methods from computer vision to his specific domain as well as developing his own novel methods, such as a conditional random field for lidar-camera fusion.

Mikkel will continue to work part time in the department, as he is starting an industrial postdoc position at the company Vitrolife A/S.

Mikkels PhD defence presentation can be seen in the video below.

PhD defence: Peter Christiansen

Today, Friday, November 3rd 2017, Peter Christiansen successfully defended his PhD thesis titled TractorEYE: Vision-based Real-time Detection for Autonomous Vehicles in Agriculture.
Peter’s PhD study was part of the now completed SAFE project, which sought “to develop autonomous agricultural machinery that will be able to harvest green biomass and cultivate row crops – without animals or humans being exposed to any type of safety risk”. Peter’s work focused on detecting obstacles and traversable areas using RGB and thermal cameras. He achieved this by both incorporating state-of-the-art methods from computer vision to his specific domain as well as developing his own novel methods, such as DeepAnomaly.

Congratulations to Peter from everyone in the Vision Group. Thank you for your collaboration, source of inspiration and our discussions throughout the years. We hope to see you back in the group soon.

Peter’s presentation at his defence can be seen in the video below.

Innovative afternoon at SEGES

Today Rasmus, Søren and Mads were presenting image-processing related results from the SmartGrass, CloverSense and RoboWeedMaps projects. Thanks to everyone who showed up and for the discussions afterwards.

Note: All videos are in Danish.

Metadiscussion of Deep Learning in Agriculture

Automatic recognition of weeds

Automatic clover-grass estimation

SAFE project demonstration day

The SAFE project officially ended 21st September 2017 with a demonstration day at HCA Airport in Odense, Denmark.

All project participants showed their achievements throughout the 4-year project, and several semi-autonomous robots were showcased both indoors and outdoors.

From Aarhus University, we contributed with a dataset demonstration video indoors, and online real-time obstacle detection and avoidance on an outdoor robot, using a combination of stereo camera and lidar. In the image below, our perception system is mounted on top of the small robot on the left.