Visit from American University of Beirut

Watch the PhD Stud Yara Rizk present ECE, AUB for our group at ECE, AU

Presented by Ph.D. Student Yara Rizk, American University of Beirut (AUB) at Aarhus University, Department of Engineering

Yara Rizk from the Department of Electrical and Computer Engineering at the American University of Beirut (AUB) visits Aarhus University to present some of the work done in the Human Machine Interaction and Machine Learning lab at AUB.

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

RoboWeedMaps on TV2 Østjylland: Ny sprøjteteknik

“Man starter med at skaffe sig overblik over hvor ukrudttet er ude i ens kornmark, og så sprøjter man bagefter, men kun de steder, der er ukrudt. Sådan lyder den besnærrende enkle opskrift, som et hold forskere fra Aarhus Universitet lige nu arbejder på i samarbejder med flere teknologivirksomheder.”

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.

Weed recognition demo

Inspired by yesterday’s visit to Agritechnica and the booths by Bayer/Xarvio/Bosch demonstrating precision weed localization and spraying, I decided to make a small video of the our current weed-decrimination capabilities:

/ Mads Dyrmann

Re-release of plant seedlings dataset

The plant seedlings dataset, made in collaboration with University of Southern Denmark and Aarhus University in Flakkebjerg, has how been moved to this site.
The dataset contains images of approximately 960 unique plants belonging to 12 species at several growth stages. Take a look at it here.

Database sample

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