Skip to main content
SpringerLink
Log in

Automatic guava disease detection using different deep learning approaches

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In many countries, agriculture plays a major role in the economy. The health of the crop is therefore very important, but there are many plant diseases that are difficult to diagnose. A close inspection is necessary in many cases, or an expert’s advice is required. As a result, it is important to address diseases in plants. Several attempts have been made to develop programs that detect diseases in plants because of the ever-rising growth of computer vision and deep learning. This paper focuses on detecting diseases in guava fruits. We use a dataset with pictures of 4 common diseases found in the fruit namely Phytopthra, Red Rust, Scab, Styler and Root. With the help of transfer learning and Convolutional Neural Networks (CNN) this paper train various models on the dataset and compare the results. The results we evaluated using accuracy, precision, Recall and F1 score. We had multiple models with test accuracy of 99%, with highest accuracy of 99.62% for DenseNet169. The results of this study are also compared with previous methods. According to the results, the proposed methods achieved better results than the previous approach.

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
Algorithm 1
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24

Similar content being viewed by others

Data availability

The paper uses the publicly available dataset for Guava Leaves and Fruits for Guava Disease. The dataset is openly available at https://data.mendeley.com/datasets/x84p2g3k6z/1

References

  1. Ashraf A, Naz S, Shirazi SH, Razzak I, Parsad M (2021) Deep transfer learning for alzheimer neurological disorder detection. Multimed Tools Appl 80(20):30117–30142

    Article  Google Scholar 

  2. Atila Ü, Uçar M, Akyol K, Uçar E (2021) Plant leaf disease classification using efficientnet deep learning model. Ecol Inform 61:101182

    Article  Google Scholar 

  3. Bailer C, Habtegebrial T, Stricker D et al. (2018) Fast feature extraction with cnns with pooling layers. arXiv preprint arXiv:1805.03096

  4. Bansal M, Kumar M, Sachdeva M, Mittal A (2021) Transfer learning for image classification using vgg19: Caltech-101 image data set. J Ambient Intell Human Comput:1–12

  5. Basri H, Syarif I, Sukaridhoto S (2018) Faster r-cnn implementation method for multi-fruit detection using tensorflow platform. In 2018 international electronics symposium on knowledge creation and intelligent computing (IES-KCIC), pages 337–340. IEEE

  6. Bhange M, Hingoliwala HA (2015) Smart farming: pomegranate disease detection using image processing. Procedia Comput Sci 58:280–288

    Article  Google Scholar 

  7. Biewald L(2020) Experiment tracking with weights and biases. Software available from wandb.com.

  8. Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T (2018) The rise of deep learning in drug discovery. Drug Discov Today 23(6):1241–1250

    Article  Google Scholar 

  9. Chen J, Zhang D, Nanehkaran YA (2020) Identifying plant diseases using deep transfer learning and enhanced lightweight network. Multimed Tools Appl 79(41):31497–31515

    Article  Google Scholar 

  10. Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1800–1807

  11. Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. pages 1800–1807

  12. Faizal S (2022) Automated identification of tree species by bark texture classification using convolutional neural networks. arXiv preprint arXiv:2210.09290

  13. Geetharamani G, Pandian A (2019) Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Comput Elect Eng 76:323–338

    Article  Google Scholar 

  14. Gokulnath BV et al (2021) Identifying and classifying plant disease using resilient lf-cnn. Ecol Inform 63:101283

    Article  Google Scholar 

  15. Habib MT, Anup Majumder AZM, Jakaria MA, Uddin MS, Ahmed F (2020) Machine vision based papaya disease recognition. J King Saud Univ-Comp Inform Sci 32(3):300–309

    Google Scholar 

  16. Hassan SKM, Maji AK (2022) Deep feature-based plant disease identification using machine learning classifier. Innov Syst Softw Eng:1–11

  17. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. Proceed IEEE Conf Comput Vision Patt Recogn:770–778

  18. Helong Y, Cheng X, Cheng C, Heidari AA, Liu J, Cai Z-N, Chen H (2022) Apple leaf disease recognition method with improved residual network. Multimed Tools Appl 81:03

    Google Scholar 

  19. Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861

  20. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4700–4708

  21. Hussain M, Bird JJ, Faria DR (2018) A study on cnn transfer learning for image classification. In: UK workshop on computational intelligence. Springer, pp 191–202

    Google Scholar 

  22. Jang E, Gu S, Poole B (2016) Categorical reparameterization with gumbel-softmax. arXiv preprint arXiv:1611.01144

  23. Jiang Z, Dong Z, Jiang W, Yang Y (2021) Recognition of rice leaf diseases and wheat leaf diseases based on multi-task deep transfer learning. Comput Electron Agric 186:106184

    Article  Google Scholar 

  24. Joshi RC, Kaushik M, Dutta MK, Srivastava A, Choudhary N (2021) Virleafnet: automatic analysis and viral disease diagnosis using deep-learning in vigna Mungo plant. Ecol Inform 61:101197

    Article  Google Scholar 

  25. Kiruba B, Arjunan P (2023) Paddy doctor: A visual image dataset for automated paddy disease classification and benchmarking. In Proceedings of the 6th Joint International Conference on Data Science & Management of Data (10th ACM IKDD CODS and 28th COMAD), pages 203–207

  26. Rangarajan AK, Purushothaman R, Pérez-Ruiz M (2021) Disease classification in aubergine with local symptomatic region using deep learning models. Biosyst Eng 209:139–153

    Article  Google Scholar 

  27. LeCun Y, Kavukcuoglu K, Farabet C (2010) Convolutional networks and applications in vision. In Proceedings of 2010 IEEE international symposium on circuits and systems, pages 253–256. IEEE

  28. Lo WW, Yang X, Wang Y (2019) An xception convolutional neural network for malware classification with transfer learning. In 2019 10th IFIP international conference on new technologies, mobility and security (NTMS), pages 1–5. IEEE

  29. Madhusudhan L (2015) Agriculture role on indian economy. Bus Econ J 6(4):1

    Google Scholar 

  30. Misra AK (2004) Guava diseases — their symptoms, causes and management. Springer, Netherlands, Dordrecht, pp 81–119

  31. Moses K, Miglani A, Kankar PK et al (2022) Deep cnn-based damage classification of milled rice grains using a high-magnification image dataset. Comput Electron Agric 195:106811

    Article  Google Scholar 

  32. Muresan H, Oltean M (2017) Fruit recognition from images using deep learning. arXiv preprint arXiv:1712.00580

  33. Nandhini S, Ashokkumar K (2022) An automatic plant leaf disease identification using densenet-121 architecture with a mutation-based henry gas solubility optimization algorithm. Neural Comput Applic 34(7):5513–5534

    Article  Google Scholar 

  34. Nandi R, Palash A, Siddique N, Zilani M (2022) Device-friendly guava fruit and leaf disease detection using deep learning

  35. Patilkulkarni S et al (2021) Visual speech recognition for small scale dataset using vgg16 convolution neural network. Multimed Tools Appl 80(19):28941–28952

    Article  Google Scholar 

  36. Paul A, Pramanik R, Malakar S, Sarkar R (2022) An ensemble of deep transfer learning models for handwritten music symbol recognition. Neural Comput Applic 34(13):10409–10427

    Article  Google Scholar 

  37. Qureshi I, Ma J, Abbas Q (2021) Diabetic retinopathy detection and stage classification in eye fundus images using active deep learning. Multimed Tools Appl 80(8):11691–11721

    Article  Google Scholar 

  38. Rahman T, Chowdhury MEH, Khandakar A, Islam KR, Islam KF, Mahbub ZB, Kadir MA, Kashem S (2020) Transfer learning with deep convolutional neural network (cnn) for pneumonia detection using chest x-ray. Appl Sci 10(9):3233

    Article  Google Scholar 

  39. Rajbongshi A, Sazzad S, Shakil R, Akter B, Sara U (2022) A comprehensive guava leaves and fruits dataset for guava disease recognition. Data in Brief 42:108174

    Article  Google Scholar 

  40. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition:4510–4520

  41. Shelke A, Mehendale N (2022) A cnn-based android application for plant leaf classification at remote locations. Neural Comput Applic:1–7

  42. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556

  43. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556,

  44. Singh R, Athisayamani S et al (2020) Banana leaf diseased image classification using novel heap auto encoder (hae) deep learning. Multimed Tools Appl 79(41):30601–30613

    Google Scholar 

  45. Sun Y, Renfu L, Yuzhen L, Kang T, Pan L (2019) Detection of early decay in peaches by structured-illumination reflectance imaging. Postharvest Biol Technol 151:68–78

    Article  Google Scholar 

  46. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V,Rabinovich A (2015) Going deeper with convolutions. pages 1–9

  47. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2818–2826

  48. Tiwari V, Joshi RC, Dutta MK (2021) Dense convolutional neural networks based multiclass plant disease detection and classification using leaf images. Ecol Inform 63:101289

    Article  Google Scholar 

  49. Vora K, Padalia D (2022) An ensemble of convolutional neural networks to detect foliar diseases in apple plants. arXiv preprint arXiv:2210.00298

  50. Zhang Z (2018) Improved Adam optimizer for deep neural networks. In 2018 IEEE/ACM 26th international symposium on quality of service (IWQoS), pages 1–2. Ieee

  51. Zhou J, Li J, Wang C, Huarui W, Zhao C, Wang Q (2021) A vegetable disease recognition model for complex background based on region proposal and progressive learning. Comput Electron Agric 184:106101

    Article  Google Scholar 

Download references

Funding

No funding was received to assist with the preparation of this manuscript.

Author information

Authors and Affiliations

  1. Indian Institute of Information Technology, Pune, India

    Vaibhav Tewari, Noamaan Abdul Azeem & Sanjeev Sharma

Authors
  1. Vaibhav Tewari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Noamaan Abdul Azeem
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sanjeev Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vaibhav Tewari.

Ethics declarations

Conficts of interest

The authors declare that they have no confict of interest.

Additional information

Publisher’s note

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

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

Tewari, V., Azeem, N.A. & Sharma, S. Automatic guava disease detection using different deep learning approaches. Multimed Tools Appl 83, 9973–9996 (2024). https://doi.org/10.1007/s11042-023-15909-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-15909-6

Keywords

Search

Navigation