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RSA based improved YOLOv3 network for segmentation and detection of weed species

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Abstract

Weeds are among the major risks impacting agricultural production and quality, it is still difficult to create reliable weed identification and detection systems because of the unstructured field circumstances and substantial biological heterogeneity of weeds. The proposed work develops a weed detection model for achieving higher crop yield. Crop and weed images are taken as an input for the proposed method. The collection of data consists of raw-data that cannot produce high accuracy. So, a certain pre-processing technique is used in the proposed method for achieving high accuracy. Adaptive median filter, adaptive gamma correction and high boost filtering techniques are used as pre-processing techniques for noise removal, contrast enhancement and edge sharpening. Then the pre-processed image is segmented and detected according to the features and properties of the pixels in the image. Improved YOLOv3-technique is used in the proposed approach for segmentation and detection of weed species. RSA-optimization is used to select the hyperparameters of YOLOv3-optimally. The proposed method is tested with several metrics which attain better performance like 96% accuracy, 96% precision, 95% recall, 4% error and 95% specificity value. Thus the designed model detects weed-species in an effective manner and it is useful for achieving higher crop production.

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The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Department of Computer Science, Dhofar University, Salalah, 211, Oman

    Mukesh Madanan

  2. Centre for Computational Imaging and Machine Vision, Department of ECE, Sri Eshwar College of Engineering, Coimbatore, 641 202, Tamil Nadu, India

    N. Muthukumaran

  3. Department of Computer Science & Engineering, School of Computing Science and Engineering (SCSE), Galgotias University, Greater Noida, Uttar Pradesh, 203201, India

    Shrikant Tiwari

  4. Department of Electronics and Communication Engineering, Sri Ramakrishna Engineering College, Coimbatore, Tamilnadu, 641022, India

    A. Vijay

  5. Department of Physics, GLA University, Mathura, Uttar Pradesh, 281406, India

    Indranil Saha

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  1. Mukesh Madanan
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  2. N. Muthukumaran
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  4. A. Vijay
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  5. Indranil Saha
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The corresponding author claims the major contribution of the paper including formulation, analysis and editing. The co-authors provide guidance to verify the analysis result and manuscript editing.

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Correspondence to N. Muthukumaran.

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Madanan, M., Muthukumaran, N., Tiwari, S. et al. RSA based improved YOLOv3 network for segmentation and detection of weed species. Multimed Tools Appl 83, 34913–34942 (2024). https://doi.org/10.1007/s11042-023-16739-2

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  • DOI: https://doi.org/10.1007/s11042-023-16739-2

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