A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Automatic detection of cereal rows by means of pattern recognition techniques
Tekijät: Tenhunen Henri, Pahikkala Tapio, Nevalainen Olli, Teuhola Jukka, Mattila Heta, Tyystjärvi Esa
Kustantaja: ELSEVIER SCI LTD
Julkaisuvuosi: 2019
Journal: Computers and Electronics in Agriculture
Tietokannassa oleva lehden nimi: COMPUTERS AND ELECTRONICS IN AGRICULTURE
Lehden akronyymi: COMPUT ELECTRON AGR
Vuosikerta: 162
Aloitussivu: 677
Lopetussivu: 688
Sivujen määrä: 12
ISSN: 0168-1699
eISSN: 1872-7107
DOI: https://doi.org/10.1016/j.compag.2019.05.002
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/41308105
Automatic locating of weeds from fields is an active research topic in precision agriculture. A reliable and practical plant identification technique would enable the reduction of herbicide amounts and lowering of production costs, along with reducing the damage to the ecosystem. When the seeds have been sown row-wise, most weeds may be located between the sowing rows. The present work describes a clustering-based method for recognition of plantlet rows from a set of aerial photographs, taken by a drone flying at approximately ten meters. The algorithm includes three phases: segmentation of green objects in the view, feature extraction, and clustering of plants into individual rows. Segmentation separates the plants from the background. The main feature to be extracted is the center of gravity of each plant segment. A tentative clustering is obtained piecewise by applying the 2D Fourier transform to image blocks to get information about the direction and the distance between the rows. The precise sowing line position is finally derived by principal component analysis. The method was able to find the rows from a set of photographs of size 1452 x 969 pixels approximately in 0.11 s, with the accuracy of 94 per cent.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
