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Communication dans un congrès

An Image Processing Method Based on Features Selection for Crop Plants and Weeds Discrimination Using RGB Images

Abstract : In the context of computer vision applied to precision agriculture, this paper presents an imaging system based on shape and intensity features, extracted from RGB images, for the discrimination between crop plants and weeds. A segmentation method with many constraints to overcome light acquisition conditions is used and coupled with morphological filtering suitable for denoising segmented images. A SVMs classifier based on a polynomial kernel function is implemented and a k-folds cross validation process is used to evaluate the performance of the SVMs classifier usable in 2 different configurations. On a training dataset, these 2 configurations are evaluated for the performance of classification in terms of true and false positive rates, according to ROC curves and area under curves. On a test dataset, these 2 configurations are exploited, giving both a relevant classification rate.
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Communication dans un congrès
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https://hal.univ-angers.fr/hal-02528729
Contributeur : Okina Université d'Angers <>
Soumis le : mercredi 1 avril 2020 - 23:40:35
Dernière modification le : jeudi 23 avril 2020 - 10:21:23

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Ali Ahmad, Rémy Guyonneau, Franck Mercier, Etienne Belin. An Image Processing Method Based on Features Selection for Crop Plants and Weeds Discrimination Using RGB Images. International Conference on Image and Signal Processing, ICISP 2018, 2018, Cherbourg, France. pp.3-10, ⟨10.1007/978-3-319-94211-7_1⟩. ⟨hal-02528729⟩

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