%0 Journal Article %1 Hemming2001 %A Hemming, J. %A Rath, T. %D 2001 %J Journal of Agricultural Engineering Research %K machinevision weeddetection %N 3 %P 233 - 243 %R DOI: 10.1006/jaer.2000.0639 %T Computer-Vision-based Weed Identification under Field Conditions using Controlled Lighting %U http://www.sciencedirect.com/science/article/B6WH1-45RFHPH-17/2/65d876213551c660610d01b130095bf6 %V 78 %X The methods of digital image analysis were used to develop an identification system for weeds in crops. Two vegetable crops (cabbage and carrots) and a number of naturally occurring weed species were used to develop the classification algorithms. Considering the rougher environment, special attention was given to the open-field experiments. The images were obtained with a device that provided controlled lighting conditions. The analysis was carried out off-line. Eight different morphological features and three colour features were calculated for each single object to build a joint feature space. On the basis of sample data sets of each class, statistics were carried out to determine the features, which are suitable for discrimination. A membership function based on a fuzzy logic approach was formed and used for the classification. The experiments showed that colour features can help to increase the classification accuracy. Moreover, colour was used successfully for the segmentation procedure of plants and soil. Depending on growth stage, weed density and method of calculation between 51 and 95% of the plants were classified correctly. Problems still exists by separating and allocating single plants in plant stands where the plants have grown together. Compared to other studies the plant identification system presented is an improvement, especially considering that the experiments were carried out under field conditions.

@article{Hemming2001, abstract = {The methods of digital image analysis were used to develop an identification system for weeds in crops. Two vegetable crops (cabbage and carrots) and a number of naturally occurring weed species were used to develop the classification algorithms. Considering the rougher environment, special attention was given to the open-field experiments. The images were obtained with a device that provided controlled lighting conditions. The analysis was carried out off-line. Eight different morphological features and three colour features were calculated for each single object to build a joint feature space. On the basis of sample data sets of each class, statistics were carried out to determine the features, which are suitable for discrimination. A membership function based on a fuzzy logic approach was formed and used for the classification. The experiments showed that colour features can help to increase the classification accuracy. Moreover, colour was used successfully for the segmentation procedure of plants and soil. Depending on growth stage, weed density and method of calculation between 51 and 95% of the plants were classified correctly. Problems still exists by separating and allocating single plants in plant stands where the plants have grown together. Compared to other studies the plant identification system presented is an improvement, especially considering that the experiments were carried out under field conditions.}, added-at = {2009-06-24T13:50:50.000+0200}, author = {Hemming, J. and Rath, T.}, biburl = {https://www.bibsonomy.org/bibtex/2731d6c75ad1eb2930e0274db878e8f37/midtiby}, description = {ScienceDirect - Journal of Agricultural Engineering Research : PA—Precision Agriculture : : Computer-Vision-based Weed Identification under Field Conditions using Controlled Lighting}, doi = {DOI: 10.1006/jaer.2000.0639}, interhash = {f7ff3154db65c9e6f58be4730ed77f60}, intrahash = {731d6c75ad1eb2930e0274db878e8f37}, issn = {0021-8634}, journal = {Journal of Agricultural Engineering Research}, keywords = {machinevision weeddetection}, number = 3, pages = {233 - 243}, timestamp = {2009-06-24T13:50:50.000+0200}, title = {Computer-Vision-based Weed Identification under Field Conditions using Controlled Lighting}, url = {http://www.sciencedirect.com/science/article/B6WH1-45RFHPH-17/2/65d876213551c660610d01b130095bf6}, volume = 78, year = 2001 }