Overview
The Institute of Chemical Engineering, Biotechnology and Environmental Technology, Faculty of Engineering, University of Southern Denmark is seeking a highly motivated graduate to undertake research in real-time discrimination and quantification of crops and weeds during their early growth stages in outdoor field conditions. The Ph.D. position is part of two projects: 1 - GUDP (Grønt- Udviklings- og DemenstrationsProgram) project with the title: “Graduation of fungicides and herbicides in potatoes and cereals”, and 2 – an Innovation project titled: “BrainWeed - Teach-in system for adaptive high speed crop / weed classification and targeting”. The primary focus of the Ph.D. project will be on:
● Robust plant segmentation based on vision under natural light conditions
● Robust real time plant discrimination
● Machine learning
It is believed that the development of sensor-based site-specific plant protection will be one of the key elements in achieving the politically stated goals of reducing the use of pesticides. Development of sensor-based site-specific crop protection systems are a prerequisite for agricultural industry realizing the potential of reducing pesticide use caused by variable presence of pests within the individual fields and between fields. Only through the use of automated sensor-based site-specific pest control can the reduction potential be achieved together with an improved operating economy. Thus the latter is a prerequisite for maintaining the competitiveness within the primary agricultural sector.
The overall project aim is to exploit the commercialized sensor technology for reduced use of pesticides in controlling weeds, potato blight and fungal diseases in cereals, and the burning of potato canopies and perennial weeds prior and after harvest. Hence, the project aim is a further development and strengthening of the 'intelligent sprayer boom' (DISB) to carry out multiple spraying tasks where variable pest application is appropriate. The upgraded DISB will be able to collect data for subsequent identification of weed species. The weed position and its population density are demanded among consultants for decisions of future pest control strategies and possibly adjustments of crop rotation. The target audience is agriculture and companies interested in spraying techniques and integrated pest management (IPM). Further, the BrainWeed project demonstrates an innovative prototype robot is an intelligent version of the existing weeding robot ROBOVATOR where the operator can now easily teach the robot new crops. After this example sugar beet recognition is performed a speed of at least 1½ mph (5.4 km / h) and a confidence of over 90%. This can only be achieved because Maribo Seeds with a robot from Frank Poulsen Engineering in 2010 and 2011 collects crop images from sprouting to the crop leaves closes. In collaboration with University of Southern Denmark the basis is established for increased automation in organic weed control and less dependence on seasonal labor.
Applicants for this Ph.D. scholarship must have a master degree in practical computer science, computer vision and/or machine learning. The applicant must have experience within:
● Sensor technology
● Machine learning
● Plant discrimination based on computer vision
● Embedded software and hardware design
Application, salary, etc.:
Appointment as a PhD Research Fellow is for three years. Employment stops automatically at the end of the period. The holder of the Fellowship is not allowed to have other paid employment during the three-year period.
The successful applicant will be employed in accordance with the agreement of 1.October 2008 on salaried PhD scholars between the Ministry of Finance and AC (the Danish Confederation of Professional Associations).
The successful candidate will be enrolled at this university in accordance with faculty regulations and the Danish Ministerial Order on the PhD Program at the Universities (PhD order).
For further information on the position and conditions of employment please contact Associate Professor Rasmus Nyholm Jørgensen (rasj@kbm.sdu.dk) or the head of department Professor Lars Porskjær Christensen (lpc@kbm.sdu.dk).
Students must have either graduated from a Scandinavian MSc program, an MSc program in English, have English as their native language, or alternatively have passed a language test at the level demanded by University of Southern Denmark, Faculty of Engineering (minimum scores: IELTS 6.5 points; Paper-based TOEFL 575 points; Computer based TOEFL 230, Internet-based TOEFL 88 points).
Please send the application, marked "Position No. 114005" and enclosures, which should include the following:
• Detailed CV, including list of publications (if any)
• Copy of diploma
• A brief one-page proposal for study plan
to University of Southern Denmark, The Faculty of Engineering, Campusvej 55, 5230 Odense M, Denmark
Employment starts: 1 March, 2011
Closing date 9 February, 2011 at 12 noon
Location: Odense
Mark application Job ID 114005 and send it to:
University of Southern Denmark
The Faculty of Engineering
Campusvej 55
5230 Odense M
Denmark
Please quote Scholarization.blogspot.com on your application when applying for this scholarship
The Institute of Chemical Engineering, Biotechnology and Environmental Technology, Faculty of Engineering, University of Southern Denmark is seeking a highly motivated graduate to undertake research in real-time discrimination and quantification of crops and weeds during their early growth stages in outdoor field conditions. The Ph.D. position is part of two projects: 1 - GUDP (Grønt- Udviklings- og DemenstrationsProgram) project with the title: “Graduation of fungicides and herbicides in potatoes and cereals”, and 2 – an Innovation project titled: “BrainWeed - Teach-in system for adaptive high speed crop / weed classification and targeting”. The primary focus of the Ph.D. project will be on:
● Robust plant segmentation based on vision under natural light conditions
● Robust real time plant discrimination
● Machine learning
It is believed that the development of sensor-based site-specific plant protection will be one of the key elements in achieving the politically stated goals of reducing the use of pesticides. Development of sensor-based site-specific crop protection systems are a prerequisite for agricultural industry realizing the potential of reducing pesticide use caused by variable presence of pests within the individual fields and between fields. Only through the use of automated sensor-based site-specific pest control can the reduction potential be achieved together with an improved operating economy. Thus the latter is a prerequisite for maintaining the competitiveness within the primary agricultural sector.
The overall project aim is to exploit the commercialized sensor technology for reduced use of pesticides in controlling weeds, potato blight and fungal diseases in cereals, and the burning of potato canopies and perennial weeds prior and after harvest. Hence, the project aim is a further development and strengthening of the 'intelligent sprayer boom' (DISB) to carry out multiple spraying tasks where variable pest application is appropriate. The upgraded DISB will be able to collect data for subsequent identification of weed species. The weed position and its population density are demanded among consultants for decisions of future pest control strategies and possibly adjustments of crop rotation. The target audience is agriculture and companies interested in spraying techniques and integrated pest management (IPM). Further, the BrainWeed project demonstrates an innovative prototype robot is an intelligent version of the existing weeding robot ROBOVATOR where the operator can now easily teach the robot new crops. After this example sugar beet recognition is performed a speed of at least 1½ mph (5.4 km / h) and a confidence of over 90%. This can only be achieved because Maribo Seeds with a robot from Frank Poulsen Engineering in 2010 and 2011 collects crop images from sprouting to the crop leaves closes. In collaboration with University of Southern Denmark the basis is established for increased automation in organic weed control and less dependence on seasonal labor.
Applicants for this Ph.D. scholarship must have a master degree in practical computer science, computer vision and/or machine learning. The applicant must have experience within:
● Sensor technology
● Machine learning
● Plant discrimination based on computer vision
● Embedded software and hardware design
Application, salary, etc.:
Appointment as a PhD Research Fellow is for three years. Employment stops automatically at the end of the period. The holder of the Fellowship is not allowed to have other paid employment during the three-year period.
The successful applicant will be employed in accordance with the agreement of 1.October 2008 on salaried PhD scholars between the Ministry of Finance and AC (the Danish Confederation of Professional Associations).
The successful candidate will be enrolled at this university in accordance with faculty regulations and the Danish Ministerial Order on the PhD Program at the Universities (PhD order).
For further information on the position and conditions of employment please contact Associate Professor Rasmus Nyholm Jørgensen (rasj@kbm.sdu.dk) or the head of department Professor Lars Porskjær Christensen (lpc@kbm.sdu.dk).
Students must have either graduated from a Scandinavian MSc program, an MSc program in English, have English as their native language, or alternatively have passed a language test at the level demanded by University of Southern Denmark, Faculty of Engineering (minimum scores: IELTS 6.5 points; Paper-based TOEFL 575 points; Computer based TOEFL 230, Internet-based TOEFL 88 points).
Please send the application, marked "Position No. 114005" and enclosures, which should include the following:
• Detailed CV, including list of publications (if any)
• Copy of diploma
• A brief one-page proposal for study plan
to University of Southern Denmark, The Faculty of Engineering, Campusvej 55, 5230 Odense M, Denmark
Employment starts: 1 March, 2011
Closing date 9 February, 2011 at 12 noon
Location: Odense
Mark application Job ID 114005 and send it to:
University of Southern Denmark
The Faculty of Engineering
Campusvej 55
5230 Odense M
Denmark
Please quote Scholarization.blogspot.com on your application when applying for this scholarship
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