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A self-supervised overlapped multiple weed and crop leaf segmentation approach under complex light condition

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Abstract

One of the most damaging obstacles to crop production is weeds; weeds pose a serious risk to agricultural output. Due to the homogenous morphological properties of weeds, farmers are unable to identify and classify the weed leaves.This study can aid farmers in identifying, categorizing, and quantifying the true extent of crop yield reduction. Computer vision is a sophisticated technique widely used for weed and crop leaf identification and detection in the agricultural field. This work has used three different datasets, such as ‘Deep Weed’, ‘Crop Weed Filed Image Dataset (CWFID), and Multi-view Image Dataset for Weed Detection in Wheat Field (MMIDDWF), and collected 5090 images for training the model. This work uses segmentation techniques for vegetation and semantics for weed object detection. Furthermore, the masked image is distributed as small tiles; often the patches are square tiles, as in 25 × 25 (px), 50 × 50 (px), and 100 × 100 (px). This work has proposed a Deep Learning segmentation model named ‘Pyramid Scene Parsing Network-USegNet’ (PSPUSegNet) for data classification and compared the accuracy of the data from existing segmentation models such as UNet, SegNet, and USegNet. The suggested model, PSPUSegNet, obtained 96.98% precision, 97.98% recall, and 98.96% data accuracy in the Deep Weed dataset. The proposed model has self-supervised in term of deep learning mechanism.Our findings demonstrate that the deep weed dataset has achieved greater data accuracy compared to the CWFID and MMIDDWF datasets. The findings support the effectiveness of the suggested approach for weed species recognition.

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Data and source codes are available from the authors upon reasonable request.

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Authors and Affiliations

  1. Chandigarh University, Mohali, Punjab, India

    Anand Muni Mishra

  2. Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India

    Prabhjot Kaur

  3. School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India

    Mukund Pratap Singh

  4. Department of Computer Science & Engineering, Rashtrakavi Ramdhari Singh Dinkar College of Engineering, Begusarai, India

    Santar Pal Singh

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  1. Anand Muni Mishra
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  2. Prabhjot Kaur
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  3. Mukund Pratap Singh
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  4. Santar Pal Singh
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Contributions

AMM.: Conceptualization, data collection, and Methodology, Writing, PK: Conceptualization and Methodology, MPS: Methodology and Supervision, SPS: Validation and Supervision.

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Correspondence to Santar Pal Singh.

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Mishra, A.M., Kaur, P., Singh, M.P. et al. A self-supervised overlapped multiple weed and crop leaf segmentation approach under complex light condition. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18272-2

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  • DOI: https://doi.org/10.1007/s11042-024-18272-2

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