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Lightweight improved yolov5 model for cucumber leaf disease and pest detection based on deep learning

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Abstract

Many diseases and pests affect cucumber leaves as they grow. A lack of prompt control may result in reduced yields or crop failure. Early and correct detection of cucumber leaf diseases and pests is essential to prevent cucumber yield reduction. Manual detection consumes a large amount of human resources and exhibits poor real-time performance. In addition, there is a lack of public datasets for cucumber leaf disease images in real-world scenarios. To this end, firstly, this study collected and established a new cucumber leaf pest and disease dataset in a real-world scenario. Secondly, to accurately detect cucumber leaf diseases and pests, an improved cucumber leaf disease and pest detection model was proposed from scratch based on the original YOLOv5l model. To reduce the model size, the Bottleneck CSP module was used instead of C3 as the backbone and neck network section. Owing to the reduction in the number of parameters, number of layers, and calculations, the detection impact was significantly improved, in addition to that, the improved model able to detect even a small leaf spot of disease. Finally, the authenticity of the proposed model is demonstrated by incorporating the convolutional block attention module (CBAM) into both the improved and original YOLOv5l model. The experimental results showed that the mean average precision (mAP) of the improved model was 80.10%; whereas, the precision and recall were 73.8%, 73.9%, respectively. The improved model weight occupies only 13.6 MB of the memory. The overall improved model performance outperformed that of the original YOLOv5 model.

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Data availability

The dataset generated during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The Authors would like to thank the University of Raparin and Erbil Polytechnique University for their support during the preparation of this study. Many thanks for the reviewers for their valuable comments and suggestions on the manuscript which improved the performance of the paper. This research was carried out at the Artificial Intelligent Research (AIR) Laboratory at the University of Raparin.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Computer Science, College of Basic Education, University of Raparin, Ranya, Kurdistan Region, Iraq

    Saman M. Omer

  2. Department of Technical Information System Engineering, Erbil Technical Engineering College, Erbil Polytechnic University, Erbil, Kurdistan Region, Iraq

    Saman M. Omer & Shavan K. Askar

  3. Department of Computer Science, Knowledge University, Erbil, 44001, Kurdistan Region, Iraq

    Kayhan Z. Ghafoor

  4. Department of Software and Informatics Engineering, Salahaddin University-Erbil, Erbil, 44001, Kurdistan Region, Iraq

    Kayhan Z. Ghafoor

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  1. Saman M. Omer
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  2. Kayhan Z. Ghafoor
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  3. Shavan K. Askar
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Contributions

Conceptualization contributed by SO, KG and SA; methodology contributed by SO; data curation contributed by SO; writing—original draft preparation contributed by SO, KG and SA; writing—review and editing contributed by SO, KG. and SA; supervision contributed by KG. and SA.; All authors have read and agreed to the published version of the manuscript.

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Correspondence to Saman M. Omer.

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Omer, S.M., Ghafoor, K.Z. & Askar, S.K. Lightweight improved yolov5 model for cucumber leaf disease and pest detection based on deep learning. SIViP 18, 1329–1342 (2024). https://doi.org/10.1007/s11760-023-02865-9

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