Skip to main content
SpringerLink
Log in

Real-time object detection method of melon leaf diseases under complex background in greenhouse

  • Original Research Paper
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

Abstract

Early disease detection in greenhouses is an important part of integrated disease management in modern agriculture. A real-time object detection method of melon leaf disease, Pruned-YOLO v5s+Shuffle (PYSS) is proposed in this research. First, for enhancing the feature extraction capability, the backbone of the YOLO v5s is reconstructed with ShuffleNet v2 Inverted Residual block. Then, to further downsize the model, the channel pruning method is used to prune and fine-tune the sparsely trained model. Finally, Pruned-YOLO v5s+Shuffle model is deployed to Jetson Nano, and the real-time performance is confirmed in melon greenhouses. The experimental results show that the proposed model has 93.2% and 98.2% mAP@0.5 for melon (Cucumis melon. L) powdery mildew and melon real leaves, respectively. Compared with YOLO v5s, the performance of our proposed model is improved 6.2% and 6.4% in the term of mAP@0.5 and precision, respectively. The model size and inference time are reduced 85% and 7.5%. In addition, the PYSS demonstrates the higher detection precision and faster inference speed in the comparison of YOLO v3, Faster R-CNN, RetinaNet, Cascade R-CNN, YOLO v4 and YOLO v5s. Being deployed to Jetson Nano, the detection results are displayed on the monitor in real time: mAP@0.5 is 96.7%, the model size is 1.1 MB, and the inference time is 13.8 ms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Guo, S., Zhang, C., Ren, Y. et al.: Application of comparative genomics in developing markers tightly linked to the pm-2f gene for powdery mildew resistance in melon (Cucumis melo l.). Euphytica 190(2), 157–168 (2013)

  2. Wu, Q., Zhang, H., Pei, Z., Jing, Y.: Identification on races of powdery mildew of melon in Huaibei. Molecular Plant Breeding (2017)

  3. Li, B., Zhao, Y., Zhu, Q., Zhang, Z., Fan, C., Amanullah, S., Gao, P., Luan, F.: Mapping of powdery mildew resistance genes in melon (cucumis melo l.) by bulked segregant analysis. Entia Horticult. 220, 160–167 (2017)

  4. Jorkesh, A., Safaei, M., Olfati, J.: Chemical and biological products for control of powdery mildew on cucumber. Int. J. Veg. Sci. pages 1–6 (2021)

  5. Pizolotto, C.A., Harrington, M., Brown, L., Murdock, M., Harrington, S., Marshall, J., Neher, O.T., Woodhall, J.W.: A real-time pcr assay for erysiphe betae and its effectiveness when used with different spore trapping methods. Eur. J. Plant Pathol. 162(2), 329–341 (2021)

    Article  Google Scholar 

  6. Hong, Y.J., Hossain, M.R., Kim, H.T., Park, J.I., Nou, I.S.: Identification of two new races of podosphaera xanthii causing powdery mildew in Melon in South Korea. Plant Pathol. J. 34(3), 182–190 (2018)

    Article  Google Scholar 

  7. Kunova, A., Pizzatti, C., Saracchi, M., Pasquali, M., Cortesi, P.: Grapevine powdery mildew: fungicides for its management and advances in molecular detection of markers associated with resistance in. Microorganisms 9(7), 1541 (2021)

    Article  Google Scholar 

  8. Qu, Y., Wu, P., Hu, J., Chen, Y., et al.: Molecular detection of the powdery mildew resistance genes in winter wheats dh51302 and shimai 26. J. Integr. Agric. 19(4), 931–940 (2020)

    Article  Google Scholar 

  9. Li, R., Wang, J. et al.: Artificial intelligence and wireless communications. Front. Inf. Technol. Electron. Eng. 21(10), 1413–1425 (2020)

  10. Bai, L., Wang, Z. et al.: An oil well dataset derived from satellite-based remote sensing. Remote Sens. 13(6) (2021)

  11. Abdolahnejad, M., Liu, P.X.: Deep learning for face image synthesis and semantic manipulations: a review and future perspectives. Artif. Intell. Rev. 53(8), 5847–5880 (2020)

    Article  Google Scholar 

  12. Min, S., Rim, B., Sung, N.J., Hong, M.: Deep learning in physiological signal data: a survey. Sensors 20(4) (2020)

  13. Cheng, G., Xie, X.X., Han, J.W., Guo, L., Xia, G.S.: Remote sensing image scene classification meets deep learning: challenges, methods, benchmarks, and opportunities. IEEE J. Select. Top. Appl. Earth Observ. Remote Sens. 13, 3735–3756 (2020)

    Article  Google Scholar 

  14. Sun, S., Yang, Y., Song, H. et al.: A fast and effective video vehicle detection method leveraging feature fusion and proposal temporal link. J. Real-time Image Process. 18(4, SI), 1261–1274 (2021)

  15. Liu, C., Zhu, H.J., Guo, W., Han, X., Chen, C., Wu, H.R.: Efdet: an efficient detection method for cucumber disease under natural complex environments. Comput. Electron. Agric. 189 (2021)

  16. Anagnostis, A., Tagarakis, A.C., Asiminari, G., Papageorgiou, E., Kateris, D., Moshou, D., Bochtis, D.: A deep learning approach for anthracnose infected trees classification in walnut orchards. Comput. Electron. Agric. 182 (2021)

  17. Song, C.L., Zhang, Y., Zhang, D.W.: Deep learning-based object detection improvement for tomato disease. Ieee Access 8, 56607–56614 (2020)

    Article  Google Scholar 

  18. Wang, H.Q., Xia, W., Yu, F., Hong, R.: A high-precision lightweight smoke detection model based on se attention mechanism. In: 2nd International Conference on Consumer Electronics and Computer Engineering, 2(2), 941–944 (2022)

  19. Wang, Z., Ma, L.: SYOLO: an efficient pedestrian detection. IOP Conf. Ser. Mater. Sci. Eng. 768, 072067 (2020)

    Article  Google Scholar 

  20. Girshick, R., Redmon, J., Divvala, S., Farhadi, A.: You only look once: Unified, real-time object detection (2015)

  21. Ma, N., Zhang, X., Zheng, H., Sun, J.: Shufflenet v2: Practical guidelines for efficient cnn architecture design (2018)

  22. Lin, M., Zhang, X., Zhou, X., Sun, J.: Shufflenet: an extremely efficient convolutional neural network for mobile devices (2017)

  23. Huang, G., Liu, S., Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks (2016)

  24. Huang, G., Liu, S., Maaten, L., Weinberger, K.Q.: Condensenet: an efficient densenet using learned group convolutions (2017)

  25. Liu, Z., Li, J., Shen, Z., Huang, G., Yan, S., Zhang, C.: Learning efficient convolutional networks through network slimming (2017)

  26. Redmon, J., Farhadi, A.: Yolov3: an incremental improvement (2018)

  27. Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y. M.: Yolov4: optimal speed and accuracy of object detection (2020)

  28. Girshick, R., Ren, S., He, K., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks (2015)

  29. Lin, T., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection (2017)

  30. Cai, Z.W., Vasconcelos, N.: Cascade r-cnn: Delving into high quality object detection (2017)

  31. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition (2015)

Download references

Author information

Authors and Affiliations

  1. College of Information and Technology, Jilin Agriculture University, Xincheng, Changchun, 130118, Jilin, China

    Yanlei Xu, Qingyuan Chen, Shuolin Kong, Lu Xing, Qi Wang, Xue Cong & Yang Zhou

Authors
  1. Yanlei Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qingyuan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shuolin Kong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lu Xing
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xue Cong
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yang Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization, YX, QC and ZY; methodology, YX, ZY, and SK; software, YX, QC, and YZ; validation, YX, QC, LX, QW, and YZ; formal analysis, YX and YZ; investigation, YX and YZ; resources, YX and YZ; data curation, YX, QC, XC, and QW; writing—original draft preparation, YX, QC, and YZ; writing—review and editing, YX, QC, and YZ. All the authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Yang Zhou.

Ethics declarations

Funding

This work is supported by the Science and Technology Development Plan Project of Changchun [Grant number 21ZGN28]; the Jilin Provincial Science and Technology Department International Exchange and Cooperation Project [Grant number 20200801014GH].

Conflict of interest

The authors declare no conflict of interest.

Availability of data and materials

Not applicable.

Code availability

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Qingyuan Chen, Shuolin Kong, Lu Xing, Qi Wang, Xue Cong, and Yang Zhou have contributed equally to this work.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, Y., Chen, Q., Kong, S. et al. Real-time object detection method of melon leaf diseases under complex background in greenhouse. J Real-Time Image Proc 19, 985–995 (2022). https://doi.org/10.1007/s11554-022-01239-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-022-01239-7

Keywords

Search

Navigation