Detection of Amaranthus palmeri sp. Seedlings in Vegetable Farms Using Genetic Algorithm Optimized Support Vector Machine

W.K Wong; Muralindran Mariappan; Ali Chekima; Manimehala Nadarajan; Brendan Khoo

doi:10.4028/www.scientific.net/AMM.666.267

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 666Detection of Amaranthus palmeri sp. Seedlings in...

Detection of Amaranthus palmeri sp. Seedlings in Vegetable Farms Using Genetic Algorithm Optimized Support Vector Machine

Abstract:

This research is a part of a larger research scope to recognise individual weed species for weed scouting and spot weeding. Support Vector Machines are used to classify the presence of specified weeds (Amaranthus palmeri ) by analysing the shape of the weeds. Weed leaves are extracted using image dilation and erosion methods. Several shape feature types were proposed and a total of 59 features were used as the feature pool. The feature selection and fine tuning of the Support Vector Machine are performed using Genetic Algorithm. The outcome is a generalised classifier that enables classification of weed leaves with an average of 90.5% classification rate.

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Periodical:

Applied Mechanics and Materials (Volume 666)

Pages:

267-271

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.666.267

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Online since:

October 2014

Authors:

W.K Wong*, Muralindran Mariappan, Ali Chekima, Manimehala Nadarajan, Brendan Khoo

Keywords:

Support Vector Machine (SVM), Weed Species Recognition

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* - Corresponding Author

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