Skip to main content
SpringerLink
Log in

“Tomato-Village”: a dataset for end-to-end tomato disease detection in a real-world environment

  • Regular Paper
  • Published:
Multimedia Systems Aims and scope Submit manuscript

Abstract

Tomato is one of the most extensively grown vegetables in any country, and their diseases can significantly affect yield and quality. Accurate and early detection of tomato diseases is crucial for reducing losses and improving crop management. Current deep learning and CNN research have resulted in the availability of multiple CNN designs, making automated plant disease identification viable rather than traditional visual inspection-based disease detection. When using deep learning Methods, the dataset serves one of the most crucial roles in disease prediction. PlantVillage is the most widely used publicly available dataset for tomato disease detection, but it was created in a laboratory/controlled environment, and models trained on it do not perform well on real-world images. Some natural or real-world datasets are available, but they are private and not publicly available. Also, when attempting to predict tomato diseases on the field in the Jodhpur and Jaipur districts of Rajasthan, India, we found that the majority of diseases are leaf miner, spotted wilt virus, and Nnutrition deficiency diseases, but there are no public datasets containing such categories. To overcome these challenges, we propose the creation of a new dataset called “Tomato-Village” with three variants: (a) multiclass tomato disease classification, (b) multilabel tomato disease classification, and (c) object detection-based tomato disease detection. To our best knowledge, “Tomato-Village” will be the first such dataset to be available publicly. Further, we have applied the various CNN architectures/models on this dataset, and baseline results were drawn. The “Tomato-Village” dataset, their baseline results, and related source code will be available at https://github.com/mamta-joshi-gehlot/Tomato-Village.

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

Similar content being viewed by others

Data availability

The “Tomato-Village” dataset, their baseline results, and related source code will be available at https://github.com/mamta-joshi-gehlot/Tomato-Village.

References

  1. Kumar, M., Pathak, H.: An economic study on production and marketing of tomato (Solanum lycopersicum) in Janjgir district, Chhattisgarh India. Pharma Innov. J. 11, 1459–1463 (2022)

    Google Scholar 

  2. Singh, C., Banafar, K.N.S., Kumar, D.: An economic analysis of tomato production in Jashpur district of Chhattisgarh state. Pharma Innov. J. 11, (2022)

  3. Tiwari, A., Bin Afroz, S., Kumar, V.: Market vulnerabilities and potential of horticulture crops in India: with special reference to top crops. Indian J. Agric. Mark. 35, (2021)

  4. Department of economic affairs ministry of finance government of india: india : economic survey 2022–23, New Delhi (2023)

  5. Das, S., Jahan, M.: Production and profitability intervention of summer hybrid tomato: a farm level review in Bangladesh. Am. J. Econ. Bus. Adm. 14, 21–30 (2022). https://doi.org/10.3844/ajebasp.2022.21.30

    Article  Google Scholar 

  6. Vanitha, S.M., Chinnappa Reddy, B.V., Gajanana, T.M.: Economic analysis of profitability in tomato production at different seasons and market prices: a study in Kolar district of Karnataka. Int. J. Agric. Sci. 10, 6961–6966 (2018)

    Google Scholar 

  7. Gehlot, M., Gandhi, G.C.: “EffiNet-TS”: A deep interpretable architecture using EfficientNet for plant disease detection and visualization. J. Plant Dis. Prot. (2023). https://doi.org/10.1007/s41348-023-00707-x

    Article  Google Scholar 

  8. Roy, A.M., Bhaduri, J.: A deep learning enabled multi-class plant disease detection model based on computer vision. AI. 2, 413–428 (2021). https://doi.org/10.3390/ai2030026

    Article  Google Scholar 

  9. Kirola, M., Joshi, K., Chaudhary, S., Singh, N., Anandaram, H., Gupta, A.: Plants diseases prediction framework: a image-based system using deep learning. In: 2022 IEEE World Conference on Applied Intelligence and Computing (AIC). pp. 307–313. IEEE (2022)

  10. Pawar, S., Shedge, S., Panigrahi, N., Jyoti, A.P., Thorave, P., Sayyad, S.: Leaf disease detection of multiple plants using deep learning. in: 2022 international conference on machine learning, big data, cloud and parallel computing (COM-IT-CON). pp. 241–245. IEEE (2022)

  11. Gehlot, M., Saini, M.L.: Analysis of different cnn architectures for tomato leaf disease classification. In: 2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE). pp. 1–6. IEEE (2020)

  12. Sakkarvarthi, G., Sathianesan, G.W., Murugan, V.S., Reddy, A.J., Jayagopal, P., Elsisi, M.: Detection and classification of tomato crop disease using convolutional neural network. Electronics (Basel) 11, 3618 (2022). https://doi.org/10.3390/electronics11213618

    Article  Google Scholar 

  13. Guerrero-Ibañez, A., Reyes-Muñoz, A.: Monitoring tomato leaf disease through convolutional neural networks. Electronics (Basel) 12, 229 (2023). https://doi.org/10.3390/electronics12010229

    Article  Google Scholar 

  14. Ozbilge, E., Ulukok, M.K., Toygar, O., Ozbilge, E.: Tomato disease recognition using a compact convolutional neural network. IEEE Access 10, 77213–77224 (2022). https://doi.org/10.1109/ACCESS.2022.3192428

    Article  Google Scholar 

  15. Ahmed, S., Hasan, Md.B., Ahmed, T., Sony, Md.R.K., Kabir, Md.H.: Less is more: lighter and faster deep neural architecture for tomato leaf disease classification. IEEE Access 10, 68868–68884 (2022). https://doi.org/10.1109/ACCESS.2022.3187203

    Article  Google Scholar 

  16. Yang, G., Chen, G., He, Y., Yan, Z., Guo, Y., Ding, J.: Self-supervised collaborative multi-network for fine-grained visual categorization of tomato diseases. IEEE Access 8, 211912–211923 (2020). https://doi.org/10.1109/ACCESS.2020.3039345

    Article  Google Scholar 

  17. Wu, Y., Xu, L.: Image generation of tomato leaf disease identification based on adversarial-VAE. Agriculture 11, 981 (2021). https://doi.org/10.3390/agriculture11100981

    Article  Google Scholar 

  18. Wu, Q., Chen, Y., Meng, J.: DCGAN-based data augmentation for tomato leaf disease identification. IEEE Access 8, 98716–98728 (2020). https://doi.org/10.1109/ACCESS.2020.2997001

    Article  Google Scholar 

  19. Wu, Y., Xu, L., Goodman, D.E.: Tomato leaf disease identification and detection based on deep convolutional neural network. Intell Autom Soft Comput. 28, 561–576 (2021)

    Article  Google Scholar 

  20. Abbas, A., Jain, S., Gour, M., Vankudothu, S.: Tomato plant disease detection using transfer learning with C-GAN synthetic images. Comput. Electron. Agric. 187, 106279 (2021). https://doi.org/10.1016/j.compag.2021.106279

    Article  Google Scholar 

  21. Albattah, W., Nawaz, M., Javed, A., Masood, M., Albahli, S.: A novel deep learning method for detection and classification of plant diseases. Complex Intell. Syst. 8, 507–524 (2022). https://doi.org/10.1007/s40747-021-00536-1

    Article  Google Scholar 

  22. Liu, J., Wang, X.: Plant diseases and pests detection based on deep learning: a review. Plant Methods 17, 22 (2021). https://doi.org/10.1186/s13007-021-00722-9

    Article  Google Scholar 

  23. Saleem, M.H., Velayudhan, K.K., Potgieter, J., Arif, K.M.: Weed identification by single-stage and two-stage neural networks: a study on the impact of image resizers and weights optimization algorithms. Front. Plant Sci. (2022). https://doi.org/10.3389/fpls.2022.850666

    Article  Google Scholar 

  24. Zhang, Y., Song, C., Zhang, D.: Deep Learning-based object detection improvement for tomato disease. IEEE Access. 8, 56607–56614 (2020). https://doi.org/10.1109/ACCESS.2020.2982456

    Article  Google Scholar 

  25. Nawaz, M., Nazir, T., Javed, A., Masood, M., Rashid, J., Kim, J., Hussain, A.: A robust deep learning approach for tomato plant leaf disease localization and classification. Sci. Rep. 12, 18568 (2022). https://doi.org/10.1038/s41598-022-21498-5

    Article  Google Scholar 

  26. Wang, X., Liu, J., Zhu, X.: Early real-time detection algorithm of tomato diseases and pests in the natural environment. Plant Methods 17, 43 (2021). https://doi.org/10.1186/s13007-021-00745-2

    Article  Google Scholar 

  27. Liu, J., Wang, X.: Tomato diseases and pests detection based on improved Yolo V3 convolutional neural network. Front. Plant Sci. (2020). https://doi.org/10.3389/fpls.2020.00898

    Article  Google Scholar 

  28. Fuentes, A., Yoon, S., Lee, M.H., Park, D.S.: Improving accuracy of tomato plant disease diagnosis based on deep learning with explicit control of hidden classes. Front. Plant Sci. (2021). https://doi.org/10.3389/fpls.2021.682230

    Article  Google Scholar 

  29. Thangaraj, R., Anandamurugan, S., Pandiyan, P., Kaliappan, V.K.: Artificial intelligence in tomato leaf disease detection: a comprehensive review and discussion. J. Plant Dis. Prot. 129, 469–488 (2022). https://doi.org/10.1007/s41348-021-00500-8

    Article  Google Scholar 

  30. Hughes, David.P., Salathe, M.: An open access repository of images on plant health to enable the development of mobile disease diagnostics. (2015)

  31. Mohanty, S.P., Hughes, D.P., Salathé, M.: Using deep learning for image-based plant disease detection. Front. Plant Sci. (2016). https://doi.org/10.3389/fpls.2016.01419

    Article  Google Scholar 

  32. Singh, D., Jain, N., Jain, P., Kayal, P., Kumawat, S., Batra, N.: PlantDoc: a dataset for visual plant disease detection. In: Proceedings of the 7th ACM IKDD CoDS and 25th COMAD. pp. 249–253. ACM, New York, NY, USA (2020)

  33. Huang, M.-L., Chang, Y.-H.: Dataset of Tomato Leaves, Mendeley Data, V1. https://doi.org/10.17632/ngdgg79rzb.1 (2020)

  34. Tian, K.: Tomato leaf image dataset, Mendeley Data, V1. https://doi.org/10.17632/369cky7n39.1 (2020)

  35. Adivarekar, B., Bomma, P., Joshi, M., Khan, S., Mohammed, M., Narvekar, M.: Tomato Leaf Dataset, Mendeley Data, V1. https://doi.org/10.17632/rv3kxfv47y.1 (2020)

  36. LabelImg: LabelImg, https://github.com/heartexlabs/labelImg

  37. Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Information 11, 125 (2020). https://doi.org/10.3390/info11020125

    Article  Google Scholar 

  38. Kaggle Leaf Detection Dataset: Kaggle Leaf Detection Dataset, https://www.kaggle.com/datasets/alexo98/leaf-detection

  39. Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. (2022)

  40. Jocher, G., Chaurasia, A., Qiu, J.: YOLOv8 by Ultralytics, https://github.com/ultralytics/ultralytics. (2023)

  41. Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A ConvNet for the 2020s. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 11966–11976. IEEE (2022)

  42. Howard, A., Sandler, M., Chen, B., Wang, W., Chen, L.-C., Tan, M., Chu, G., Vasudevan, V., Zhu, Y., Pang, R., Adam, H., Le, Q.: Searching for MobileNetV3. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV). pp. 1314–1324. IEEE (2019)

Download references

Funding

No funding.

Author information

Authors and Affiliations

  1. Poornima University, Jaipur, India

    Mamta Gehlot, Rakesh Kumar Saxena & Geeta Chhabra Gandhi

Authors
  1. Mamta Gehlot
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rakesh Kumar Saxena
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Geeta Chhabra Gandhi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mamta Gehlot.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Communicated by B. Bao.

Publisher's Note

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

Appendix

Appendix

See for Tables 10, 11, 12, 13, and 14

Table 10 The state-of-art tomato disease classification results on multiclass variant of “Tomato-Village” dataset with 50 epochs
Full size table
Table 11 MobileNetV3 architectures results on multiclass variant of “Tomato-Village” dataset with more than 50 epochs
Full size table
Table 12 The state-of-art tomato disease classification results on the augmented multiclass variant of “Tomato-Village” dataset with 50 epochs
Full size table
Table 13 The state-of-art tomato disease classification results on multilabel variant of “Tomato-Village” dataset with 50 epochs
Full size table
Table 14 The state-of-art tomato disease classification results on the augmented multilabel variant of “Tomato-Village” dataset with 50 epochs
Full size table

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) 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

Gehlot, M., Saxena, R.K. & Gandhi, G.C. “Tomato-Village”: a dataset for end-to-end tomato disease detection in a real-world environment. Multimedia Systems 29, 3305–3328 (2023). https://doi.org/10.1007/s00530-023-01158-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00530-023-01158-y

Keywords

Search

Navigation