Classification of Bidens in wheat farms

Zhang, Zheng‐Zhi; Kodagoda, Sarath; Ruiz, David; Katupitiya, Jayantha; Dissanayake, Gamini

doi:10.1504/ijcat.2010.034740

Cited by 8 publications

(1 citation statement)

References 13 publications

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“…Regarding the features used for discrimination, many studies have used colour, shape (biological morphology) and texture on an individual basis or in combination (Ahmed, Al-Mamun, Bari, Hossain, & Kwan, 2012;Åstrand & Baerveldt, 2002;Gebhardt & Kühbauch, 2007;Nieuwenhuizen, Tang, Hofstee, Müller, & Van Henten, 2007;Pérez, López, Benlloch, & Christensen, 2000;Persson & Åstrand, 2008;Slaughter, Giles, & Downey, 2008;Swain, Nørremark, Jørgensen, Midtiby, & Green, 2011;Zhang, Kodagoda, Ruiz, Katupitiya, & Dissanayake, 2010). These features are intuitive and easy-to-implement but may have limited discrimination ability under ambient lighting conditions.…”

Section: Introductionmentioning

confidence: 99%

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

Suh

Hofstee

IJsselmuiden

et al. 2018

Biosystems Engineering

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One of the most important steps in vision-based weed detection systems is the classification of weeds growing amongst crops. In 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 Transformation (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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