Implementation of YOLOv7 for Pest Detection-Reference-Cited by-同舟云学术

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Implementation of YOLOv7 for Pest Detection

Published:2023 Issue: Volume: Page:156-165
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:
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Author:

Nayar Pratibha,Chhibber Shivank,Dubey Ashwani Kumar^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-34222-6_13

Reference18 articles.

1. Liu, L., et al.: PestNet: an end-to-end deep learning approach for large-scale multi-class pest detection and classification. IEEE Access 7, 45301–45312 (2019)

2. Liu, J., et al.: Plant diseases and pests detection based on deep learning: a review. Plant Methods 17, 22 (2021)

3. Selvaraj, M.G., Vergara, A., Ruiz, H., et al.: AI-powered banana diseases and pest detection. Plant Methods 15, 92 (2019)

4. Wang, C.Y., et al.: YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors, arXiv preprint (2022)

5. Dong, S., Zhang, J., Wang, F., Wang, X.: YOLO-pest: a real-time multi-class crop pest detection model. In: International Conference on Computer Application and Information Security 12260 (2022)

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