Automatic detection of cereal rows by means of pattern recognition techniques

• Published in: Computers and electronics in agriculture 2019-07, Vol.162, p.677-688
• Main Authors: Tenhunen, Henri, Pahikkala, Tapio, Nevalainen, Olli, Teuhola, Jukka, Mattila, Heta, Tyystjärvi, Esa
• Format: Article
• Language: English
• Subjects: Aerial photography; Agricultural practices; Algorithms; Center of gravity; Clustering; Computer vision; Drone aircraft; Feature extraction; Fourier transform; Fourier transforms; Herbicides; Image segmentation; Measuring instruments; Object recognition; Pattern recognition; Precision agriculture; Principal component analysis; Principal components analysis; Production costs; Seeds; Weeds
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2019.05.002

•A method for row detection from photographs taken from cereal fields was developed.•The method was tested with drone photographs from a rye field.•2D Fourier transform is used to find the angle between the rows and the picture axis.•High speed and high precision were achieved. 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 1452×969 pixels approximately in 0.11 s, with the accuracy of 94 per cent.