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 536466
Title Sugar beet and volunteer potato classification using Bag-of-Visual-Words model, Scale-Invariant Feature Transform, or Speeded Up Robust Feature descriptors and crop row information
Author(s) Suh, Hyun K.; Hofstee, Jan Willem; IJsselmuiden, Joris; Henten, Eldert J. van
Source Biosystems Engineering 166 (2018). - ISSN 1537-5110 - p. 210 - 226.
Department(s) WUR GTB Tuinbouw Technologie
Farm Technology Group
NVAO Programmes
Publication type Refereed Article in a scientific journal
Publication year 2018
Availibility Full text available from 2020-02-01
Keyword(s) Bag-of-Visual-Words - Posterior probability - SIFT - SURF - Weed classification
Abstract One of the most important steps in vision-based weed detection systems is the classification of weeds growing amongst crops. In the EU SmartBot project it was required to effectively control more than 95% of volunteer potatoes and ensure less than 5% of damage of sugar beet. Classification features such as colour, shape and texture have been used individually or in combination for classification studies but they have proved unable to reach the required classification accuracy under natural and varying daylight conditions. A classification algorithm was developed using a Bag-of-Visual-Words (BoVW) model based on Scale-Invariant Feature Transform (SIFT) or Speeded Up Robust Feature (SURF) features with crop row information in the form of the Out-of-Row Regional Index (ORRI). The highest classification accuracy (96.5% with zero false-negatives) was obtained using SIFT and ORRI with Support Vector Machine (SVM) which is considerably better than previously reported research although its 7% false-positives deviated from the requirements. The average classification time of 0.10–0.11 s met the real-time requirements. The SIFT descriptor showed better classification accuracy than the SURF, but classification time did not vary significantly. Adding location information (ORRI) significantly improved overall classification accuracy. SVM showed better classification performance than random forest and neural network. The proposed approach proved its potential under varying natural light conditions, but implementing a practical system, including vegetation segmentation and weed removal may potentially reduce the overall performance and more research is needed.
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