Skip to main content
SpringerLink
Log in

A Comprehensive Study on Crop Disease Prediction Using Learning Approaches

  • Conference paper
  • First Online:
Computer Networks and Inventive Communication Technologies

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 141))

  • 456 Accesses

Abstract

The detection of plant disease is an important problem that requires concentrating on the effective production of agriculture and the economy. The traditional techniques were used to detect this plant disease, but it is a difficult task that needs a lot of time, work, and expertise. The recent research gathered more attention recently between researchers, practitioners, and academicians called automatic detection of plant disease. This automation uses two techniques called machine learning and deep learning that helps for the identification of plant disease at the earlier stage as it finds in leaves of plants. A complete examination was done for the evaluation using the modern study on the possibility of acquiring the machine learning models to find the plant disease. There are four methods of diseases and infection on crops. Primarily, various possible diseases and infections on various crop types are investigated with the cause for the happening and feasible symptoms for the identifications. A thorough investigation on the various pace is needed that is evolved in the detection of plant disease and the categorization with the help of deep learning and machine learning that are given. Different datasets that are there in online to detect plant disease are also provided. A complete investigation regarding different machine learning and deep learning depending on the classification models is discussed, which are given previously and are suggested over the world by various researches for the four above-mentioned crops related to the evaluation on performance, the used dataset, and the method of feature extraction. Finally, different difficulties are listed and presented when using machine learning and deep learning techniques to identify the plant disease and present the future research scope.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Atoum Y, Afridi MJ, Liu X, McGrath JM, Hanson LE (2016) On developing and enhancing plant-level disease rating systems in real fields. Pattern Recogn 53:287–299

    Article  Google Scholar 

  2. Chen L, Wang R, Yang J, Xue L, Hu M (2019) Multi-label image classification with recurrently learning semantic dependencies. Vis Comput 35(10):1361–1371

    Article  Google Scholar 

  3. Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311–318

    Article  Google Scholar 

  4. Guan W, Sun Y, Wang J (2017) Automatic image-based plant disease severity estimation using deep learning. Comput Intell Neurosci 2017:2917536

    Google Scholar 

  5. He N, Wang T, Chen P, Yan H, Jin Z (2018) An android malware detection method based on deep autoencoder. In: Proceedings of the 2018 artificial intelligence and cloud computing conference, pp 88–93

    Google Scholar 

  6. Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132–7141

    Google Scholar 

  7. Ji M, Zhang L, Wu Q (2019) Automatic grape leaf diseases identification via united model based on multiple convolutional neural networks. Inf Process Agric. https://doi.org/10.1016/j.inpa.2019.10.003

    Article  Google Scholar 

  8. Lee J, Seo W, Park JH, Kim DW (2019) Compact feature subset-based multi-label music categorization for mobile devices. Multimed Tools Appl 78(4):4869–4883

    Article  Google Scholar 

  9. Pouyanfar S, Wang T, Chen SC (2019) A multi-label multimodal deep learning framework for imbalanced data classification. In: 2019 IEEE conference on multimedia information processing and retrieval (MIPR), pp 199–204

    Google Scholar 

  10. Sun H, Wei J, Zhang J, Yang W (2014) A comparison of disease severity measurements using image analysis and visual estimates using a category scale for genetic analysis of resistance to bacterial spot in tomato. Eur J Plant Pathol 139(1):125–136

    Article  Google Scholar 

  11. Zhang Z, Hong WC (2019) Electric load forecasting by complete ensemble empirical mode decomposition adaptive noise and support vector regression with quantum-based dragonfly algorithm. Nonlinear Dyn 98(2):1107–1136

    Article  MathSciNet  Google Scholar 

  12. Zoph B, Vasudevan V, Shlens J, Le QV (2018) Learning transferable architectures for scalable image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8697–8710

    Google Scholar 

  13. Canizares MC, Rosas-Diaz T, Rodriguez-Negrete E, Hogenhout SA, Bedford ID, Bejarano ER, Navas-Castillo J, Moriones E (2015) Arabidopsis thaliana, an experimental host for tomato yellow leaf curl disease-associated begomoviruses by agroinoculation and whitefly transmission. Plant Pathol 64:265–271

    Google Scholar 

  14. Fuentes A, Yoon S, Youngki H, Lee Y, Park DS (2016) Characteristics of tomato plant diseases—a study for tomato plant disease identification. Proc Int Symp Inf Technol Converg 1:226–231

    Google Scholar 

  15. Chen H, Qi XJ, Cheng JZ, Heng PA (2016) Deep contextual networks for neuronal structure segmentation. In: Thirtieth AAAI conference on artificial intelligence. Association for the Advancement of Artificial Intelligence (AAAI) Publications

    Google Scholar 

  16. Fuentes AF, Yoon S, Lee J, Park DS (2018) High-performance deep neural network-based tomato plant diseases and pests diagnosis system with refinement filter bank. Front Plant Sci 9:1–15. https://doi.org/10.3389/fpls.2018.01162

    Article  Google Scholar 

  17. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition, pp 770–778. https://doi.org/10.1109/CVPR.2016.90

  18. Lin D, Dai J, Jia J, He K, Sun J (2016) Scribblesup: scribble-supervised convolutional networks for semantic segmentation, pp 3159–3167. https://doi.org/10.1109/CVPR.2016.344

  19. Milioto A, Lottes P, Stachniss C (2018) Real-time semantic segmentation of crop and weed for precision agriculture robots leveraging background knowledge in CNNs, pp 2229–2235. https://doi.org/10.1109/ICRA.2018.8460962

  20. Mohanty SP, Hughes DP, Salathé M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:1419. https://doi.org/10.3389/fpls.2016.01419

    Article  Google Scholar 

  21. Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 779–788. https://doi.org/10.1109/CVPR.2016.91. Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time

  22. Zhang Q, Chen MS, Li B (2017) A visual navigation algorithm for paddy field weeding robot based on image understanding. Comput Electron Agric 143:66–78. https://doi.org/10.1016/j.compag.2017.09.008

    Article  Google Scholar 

  23. Yamamoto K, Togami T, Yamaguchi N (2017) Super-resolution of plant disease images for the acceleration of image-based phenotyping and vigor diagnosis in agriculture. Sensors 17(11):2557

    Article  Google Scholar 

  24. Barbedo JGA (2018) Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification. Comput Electron Agric 153:46–53

    Article  Google Scholar 

  25. Park K, Hong YK, Kim GH, Lee J (2018) Classification of apple leaf conditions in hyper-spectral images for diagnosis of Marssonina blotch using mrmr and deep neural network. Comput Electron Agric 148:179–187

    Article  Google Scholar 

  26. Yang Z, Luo T, Wang D, Hu Z, Gao J, Wang L (2018) Learning to navigate for fine-grained classification. In: Proceedings of the European conference on computer vision (ECCV), pp 420–435

    Google Scholar 

  27. Ghazi MM, Yanikoglu B, Aptoula E (2017) Plant identification using deep neural networks via optimization of transfer learning parameters. Neurocomputing 235:228–235

    Article  Google Scholar 

  28. Chen SW, Shivakumar SS, Dcunha S, Das J, Okon E, Qu C, Taylor CJ, Kumar V (2017) Counting apples and oranges with deep learning: a data-driven approach. IEEE Robotics and Automation Letters 2(2):781–788

    Article  Google Scholar 

  29. He K, Zhang X, Ren S, Sun J (2016) Identity mappings in deep residual networks. European conference on computer vision. Springer, Belin, pp 630–645

    Google Scholar 

  30. Singh A, Ganapathysubramanian B, Singh AK, Sarkar S (2015) Machine learning for high throughput stress phenotyping in plants. Trends Plant Sci 21:110–124. https://doi.org/10.1016/j.tplants.2015.10.015

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Technology, Annamalai University, Chidambaram, Tamilnadu, India

    S. Sandeepkumar & K. Jagan Mohan

Authors
  1. S. Sandeepkumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. Jagan Mohan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Sandeepkumar .

Editor information

Editors and Affiliations

  1. Department of CSE, RVS Technical Campus, Coimbatore, Tamil Nadu, India

    S. Smys

  2. Department of Telecommunication Engineering, Czech Technical University in Prague, Prague, Czech Republic

    Pavel Lafata

  3. Gerald Schwartz School of Business, St. Francis Xavier University, Antigonish, NS, Canada

    Ram Palanisamy

  4. Department of Computer Science, Texas Southern University, Houston, TX, USA

    Khaled A. Kamel

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sandeepkumar, S., Mohan, K.J. (2023). A Comprehensive Study on Crop Disease Prediction Using Learning Approaches. In: Smys, S., Lafata, P., Palanisamy, R., Kamel, K.A. (eds) Computer Networks and Inventive Communication Technologies. Lecture Notes on Data Engineering and Communications Technologies, vol 141. Springer, Singapore. https://doi.org/10.1007/978-981-19-3035-5_8

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-3035-5_8

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-3034-8

  • Online ISBN: 978-981-19-3035-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation