%0 Journal Article %1 Wang2007 %A Wang, N. %A Zhang, N. %A Wei, J. %A Stoll, Q. %A Peterson, D.E. %D 2007 %J Biosystems Engineering %K classification machinevision wheat %N 3 %P 276 - 285 %R DOI: 10.1016/j.biosystemseng.2007.08.007 %T A real-time, embedded, weed-detection system for use in wheat fields %U http://www.sciencedirect.com/science/article/B6WXV-4PYRKF7-1/2/a30feeef41983682bbb68bfcca3fb37f %V 98 %X Two optical weed sensors and their control modules (a central-control module, a global positioning system unit, and a spray-control module) were successfully integrated into a real-time, embedded system. The system components were networked using a controller area network. The system was tested extensively in two wheat fields. With good training, the system generally reached weed-detection accuracies greater than 70%. The addition of a light-blocking screen and artificial lights facilitated use of the system at night or under variable light conditions. Classification models trained with multiple weed species improved classification accuracy. Classification accuracy was also affected by the position of the sensor relative to the targets during training.

@article{Wang2007, abstract = {Two optical weed sensors and their control modules (a central-control module, a global positioning system unit, and a spray-control module) were successfully integrated into a real-time, embedded system. The system components were networked using a controller area network. The system was tested extensively in two wheat fields. With good training, the system generally reached weed-detection accuracies greater than 70%. The addition of a light-blocking screen and artificial lights facilitated use of the system at night or under variable light conditions. Classification models trained with multiple weed species improved classification accuracy. Classification accuracy was also affected by the position of the sensor relative to the targets during training.}, added-at = {2009-06-03T09:13:53.000+0200}, author = {Wang, N. and Zhang, N. and Wei, J. and Stoll, Q. and Peterson, D.E.}, biburl = {https://www.bibsonomy.org/bibtex/21ec5e64cf1602fdf67885d92039eee99/midtiby}, description = {ScienceDirect - Biosystems Engineering : A real-time, embedded, weed-detection system for use in wheat fields}, doi = {DOI: 10.1016/j.biosystemseng.2007.08.007}, interhash = {9442603d2a4458b157ecb497adbbd119}, intrahash = {1ec5e64cf1602fdf67885d92039eee99}, issn = {1537-5110}, journal = {Biosystems Engineering}, keywords = {classification machinevision wheat}, number = 3, pages = {276 - 285}, timestamp = {2009-06-03T09:13:54.000+0200}, title = {A real-time, embedded, weed-detection system for use in wheat fields}, url = {http://www.sciencedirect.com/science/article/B6WXV-4PYRKF7-1/2/a30feeef41983682bbb68bfcca3fb37f}, volume = 98, year = 2007 }