Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 427501
Title The role of textures to improve the detection accuracy of Rumex obtusifolius in robotic systems
Author(s) Hiremath, S.; Heijden, G.W.A.M. van der; Evert, F.K. van; Stein, A.
Source Weed Research 52 (2012)5. - ISSN 0043-1737 - p. 430 - 440.
Department(s) Biometris (WU MAT)
Biometris (PPO/PRI)
PPO/PRI AGRO Duurzame Bedrijfssystemen
Publication type Refereed Article in a scientific journal
Publication year 2012
Keyword(s) classification - weed - segmentation - grassland - features - cooccurrence - images - gabor - color
Abstract Rumex obtusifolius is a common weed that is difficult to control in organic farming systems. Among the proposed non-chemical treatment methods, robotic systems to mechanically remove the weed have potential for its automatic detection and removal. This article considers a recently developed robot with a real-time vision system capable of detecting R. obtusifolius in a pasture with the objective of improving its detection accuracy. We show that the texture measure used by the current system is equivalent to local variance and has limited value for detecting R. obtusifolius in a complex background like pasture. To improve the system, two different sets of visual texture features corresponding to Gray level Co-occurence Matrix (GLCM) and Laws’ filter masks were investigated. Through feature selection, we determined that GLCM features of contrast, entropy and correlation were the best among the two sets of features and were 25% more accurate in estimating the taproot location than the current system. We incorporated these texture features in a new segmentation algorithm and demonstrated its robustness by testing it on a data set of 92 images with high complexity in terms of variation in illumination and weed size. The new segmentation algorithm had a detection accuracy of 90%, with an average error of 141 mm in the estimation of the location of the taproot of R. obtusifolius, compared with 308 mm with the former algorithm
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