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A New Hybrid Model of Deep Learning ResNeXt-SVM for Weed Detection: Case Study

Author

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  • Brahim Jabir

    (LIMATI Laboratory, Sultan Moulay Slimane University, Morocco)

  • Noureddine Falih

    (LIMATI Laboratory, Sultan Moulay Slimane University, Morocco)

Abstract

A set of experiments has shown that deep learning as well as traditional learning can be used in the weed detection process and perform well, although sometimes these models cannot fully exploit and utilize the long-term dependency relationship between some key features of images and image labels. To remedy this known problem in the field of image classification, the authors have introduced a classifier known as the linear support vector machine (SVM). Specifically, they have combined a ResNeXt and SVM network to provide the ResNeXt-SVM framework that can deepen the exploitation of the structured features of images and the understanding of their content. The experimental results show that compared to other algorithm models such as ResNet, ResNeXt, and VGG, the proposed solution is more precise and efficient in classifying weeds.

Suggested Citation

  • Brahim Jabir & Noureddine Falih, 2022. "A New Hybrid Model of Deep Learning ResNeXt-SVM for Weed Detection: Case Study," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 18(2), pages 1-18, April.
    • Handle: RePEc:igg:jiit00:v:18:y:2022:i:2:p:1-18
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    Cited by:

    1. Haotian Pei & Youqiang Sun & He Huang & Wei Zhang & Jiajia Sheng & Zhiying Zhang, 2022. "Weed Detection in Maize Fields by UAV Images Based on Crop Row Preprocessing and Improved YOLOv4," Agriculture, MDPI, vol. 12(7), pages 1-18, July.

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