Machine vision detection parameters for plant species identification

George E. Meyer; Timothy W. Hindman; Koppolu Laksmi

doi:10.1117/12.336896

14 January 1999 Machine vision detection parameters for plant species identification

George E. Meyer, Timothy W. Hindman, Koppolu Laksmi

Proceedings Volume 3543, Precision Agriculture and Biological Quality; (1999) https://doi.org/10.1117/12.336896
Event: Photonics East (ISAM, VVDC, IEMB), 1998, Boston, MA, United States

Abstract

Machine vision based on classical image processing techniques has the potential to be a useful tool for plant detection and identification. Plant identification is needed for weed detection, herbicide application or other efficient chemical spot spraying operations. The key to successful detection and identification of plants as species types is the segmentation of plants form background pixel regions. In particular, it would be beneficial to segment individual leaves form tops of canopies as well. The segmentation process yields an edge or binary image which contains shape feature information. Results indicate that red-green-blue formats might provide the best segmentation criteria, based on models of human color perception. The binary image can be also used as a template to investigate textural features of the plant pixel region, using gray image co-occurrence matrices. Texture features considers leaf venation, colors, or additional canopy structure that might be used to identify various type of grasses or broadleaf plants.

Citation Download Citation

George E. Meyer, Timothy W. Hindman, and Koppolu Laksmi "Machine vision detection parameters for plant species identification", Proc. SPIE 3543, Precision Agriculture and Biological Quality, (14 January 1999); https://doi.org/10.1117/12.336896

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