Skip to main content
SpringerLink
Log in

A Comprehensive Review on Crop Disease Prediction Based on Machine Learning and Deep Learning Techniques

  • Conference paper
  • First Online:
Third Congress on Intelligent Systems (CIS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 608))

Included in the following conference series:

Abstract

Leaf diseases cause direct crop losses in agriculture, and farmers cannot detect the disease early. If the diseases are not detected early and correctly, the farmer must undergo huge losses. It may lead to the wrong pesticide or over pesticide, directly affect crop productivity and economy, and indirectly affect human health. Sensitive crops have various leaf diseases, and early prediction of these diseases remains challenging. This paper reviews several machine learning (ML) and deep learning (DL) methods used for different crop disease segmentation and classification. In the last few years, computer vision and DL techniques have made tremendous progress in object detection and image classification. The study summaries the available research on different diseases on various crops based on machine learning (ML) and deep learning (DL) techniques. It also discusses the data sets used for research and the accuracy and performance of existing methods. It does mean that the methods and available data sets presented in this paper are not projected to replace published solutions for crop disease identification, perhaps to enhance them by finding the possible gaps. Seventy-five articles are analysed and reviewed to find essential issues that involve additional study for future research in this domain to promote continuous progress for data sets, methods, and techniques. It mainly focuses on image segmentation and classification techniques used to solve agricultural problems. Finally, this paper provides future research scope and challenges, limitations, and research gaps.

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 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
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. Shrivastava VK, Pradhan MK (2021) Rice plant disease classification using color features: a machine learning paradigm. J Plant Pathol 103(1):17–26

    Article  Google Scholar 

  2. Jothiaruna N, Joseph Abraham Sundar K, Ifjaz Ahmed M (2021) A disease spot segmentation method using comprehensive color feature with multi-resolution channel and region growing. Multimed Tools Appl 80(3):3327–3335

    Article  Google Scholar 

  3. Fuentes A, Yoon S, Kim SC, Park DS (2017) A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors 17(9):2022

    Google Scholar 

  4. 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 9:1162

    Article  Google Scholar 

  5. Pham TN, Tran LV, Dao SVT (2020) Early disease classification of mango leaves using feed-forward neural network and hybrid metaheuristic feature selection. IEEE Access 8:189960–189973

    Article  Google Scholar 

  6. Mia M, Roy S, Das SK, Rahman M (2020) Mango leaf disease recognition using neural network and support vector machine. Iran J Comput Sci 3(3):185–193

    Article  Google Scholar 

  7. Mustafa MS, Husin Z, Tan WK, Mavi MF, Farook RSM (2020) Development of automated hybrid intelligent system for herbs plant classification and early herbs plant disease detection. Neural Comput Appl 32(15):11419–11441

    Article  Google Scholar 

  8. Gayathri Devi T, Neelamegam P (2019) Image processing based rice plant leaves diseases in Thanjavur, Tamilnadu. Cluster Comput 22(6):13415–13428

    Article  Google Scholar 

  9. Kaur P, Pannu HS, Malhi AK (2019) Plant disease recognition using fractional-order Zernike moments and SVM classifier. Neural Comput Appl 31(12):8749–8768

    Article  Google Scholar 

  10. Andrushia AD, Trephena Patricia A (2020) Artificial bee colony optimization (ABC) for grape leaves disease detection. Evolv Syst 11(1):105–117

    Article  Google Scholar 

  11. Kan HX, Jin L, Zhou FL (2017) Classification of medicinal plant leaf image based on multi-feature extraction. Pattern Recogn Image Anal 27(3):581–587

    Article  Google Scholar 

  12. Kumar PL, Vinay Kumar Goud K, Vasanth Kumar G, Shijin Kumar PS (2020) Enhanced weighted sum back propagation neural network for leaf disease classification. Proc Mater Today

    Google Scholar 

  13. Singh S, Gupta S, Tanta A, Gupta R (2022) Extraction of multiple diseases in apple leaf using machine learning. Int J Image Graph 22(03):2140009

    Article  Google Scholar 

  14. Chouhan SS, Singh UP, Jain S (2021) Automated plant leaf disease detection and classification using fuzzy based function network. Wirel Pers Commun 121(3):1757–1779

    Article  Google Scholar 

  15. Nagi R, Tripathy SS (2021) Severity estimation of grapevine diseases from leaf images using fuzzy inference system. Agric Res 1–11

    Google Scholar 

  16. Deepa NR, Nagarajan N (2021) Kuan noise filter with Hough transformation based reweighted linear program boost classification for plant leaf disease detection. J Ambient Intell Hum Comput 12(6):5979–5992

    Article  Google Scholar 

  17. Athiraja A, Vijayakumar P (2021) Banana disease diagnosis using computer vision and machine learning methods. J Ambient Intell Hum Comput 12(6):6537–6556

    Article  Google Scholar 

  18. Ishak S, Rahiman MHF (2015) Leaf disease classification using artificial neural network. J Tek 77(17)

    Google Scholar 

  19. Chouhan SS, Kaul A, Singh UP, Jain S (2018) Bacterial foraging optimization based radial basis function neural network (BRBFNN) for identification and classification of plant leaf diseases: an automatic approach towards plant pathology. IEEE Access 6:8852–8863

    Article  Google Scholar 

  20. Deshapande AS, Giraddi SG, Karibasappa KG, Desai SD (2019) Fungal disease detection in maize leaves using Haar wavelet features. Information and communication technology for intelligent systems. Springer, Singapore, pp 275–286

    Google Scholar 

  21. Devi MB, Amarendra K (2021) Machine learning-based application to detect pepper leaf diseases using HistGradientBoosting classifier with fused HOG and LBP features. Smart technologies in data science and communication. Springer, Singapore, pp 359–369

    Chapter  Google Scholar 

  22. Tian K, Li J, Zeng J, Evans A, Zhang L (2019) Segmentation of tomato leaf images based on adaptive clustering number of K-means algorithm. Comput Electron Agric 165:104962

    Article  Google Scholar 

  23. Ramesh S, Vydeki D (2018) Rice blast disease detection and classification using machine learning algorithm. In: 2018 2nd international conference on micro-electronics and telecommunication engineering (ICMETE). IEEE, pp 255–259

    Google Scholar 

  24. Wani JA, Sharma S, Muzamil M, Ahmed S, Sharma S, Singh S (2021) Machine learning and deep learning based computational techniques in automatic agricultural diseases detection: methodologies, applications, and challenges. Arch Comput Methods Eng 1–37

    Google Scholar 

  25. Pantazi XE, Moshou D, Tamouridou AA (2019) Automated leaf disease detection in different crop species through image features analysis and One Class Classifiers. Comput Electron Agric 156:96–104

    Article  Google Scholar 

  26. Li Y, Chao X (2021) Semi-supervised few-shot learning approach for plant diseases recognition. Plant Methods 17(1):1–10

    Article  Google Scholar 

  27. Agarwal M, Gupta SK, Biswas KK (2020) Development of efficient CNN model for tomato crop disease identification. Sustain Comput: Inform Syst 28:100407

    Google Scholar 

  28. Amara J, Bouaziz B, Algergawy A (2017) A deep learning-based approach for banana leaf diseases classification. Datenbanksysteme für Business, Technologie und Web (BTW 2017)-Workshopband

    Google Scholar 

  29. Wu Q, Chen Y, Meng J (2020) DCGAN-based data augmentation for tomato leaf disease identification. IEEE Access 8:98716–98728

    Article  Google Scholar 

  30. Zhao S, Peng Y, Liu J, Wu S (2021) Tomato leaf disease diagnosis based on improved convolution neural network by attention module. Agriculture 11(7):651

    Article  Google Scholar 

  31. Chen X, Zhou G, Chen A, Yi J, Zhang W, Hu Y (2020) Identification of tomato leaf diseases based on combination of ABCK-BWTR and B-ARNet. Comput Electron Agric 178:105730

    Article  Google Scholar 

  32. Ahmad I, Hamid M, Yousaf S, Shah ST, Ahmad MO (2020) Optimizing pretrained convolutional neural networks for tomato leaf disease detection. Complexity 2020

    Google Scholar 

  33. Lee T-Y, Lin I-A, Yu J-Y, Yang J-m, Chang Y-C (2021) High efficiency disease detection for potato leaf with convolutional neural network. SN Comput Sci 2(4):1–11

    Article  Google Scholar 

  34. Rashid J, Khan I, Ali G, Almotiri SH, AlGhamdi MA, Masood K (2021) Multi-level deep learning model for potato leaf disease recognition. Electronics 10(17):2064

    Article  Google Scholar 

  35. Tiwari D, Ashish M, Gangwar N, Sharma A, Patel S, Bhardwaj S (2020) Potato leaf diseases detection using deep learning. In: 2020 4th International conference on intelligent computing and control systems (iciccs). IEEE, pp 461–466

    Google Scholar 

  36. Liu B, Ding Z, Tian L, He D, Li S, Wang H (2020) Grape leaf disease identification using improved deep convolutional neural networks. Front Plant Sci 11:1082

    Article  Google Scholar 

  37. Xie X, Ma Y, Liu B, He J, Li S, Wang H (2020) A deep-learning-based real-time detector for grape leaf diseases using improved convolutional neural networks. Front Plant Sci 11:751

    Article  Google Scholar 

  38. Ahila Priyadharshini R, Arivazhagan S, Arun M, Mirnalini A (2019) Maize leaf disease classification using deep convolutional neural networks. Neural Comput Appl 31(12):8887–8895

    Article  Google Scholar 

  39. Boulent J, Foucher S, Théau J, St-Charles P-L (2019) Convolutional neural networks for the automatic identification of plant diseases. Front Plant Sci 10:941

    Article  Google Scholar 

  40. Liu J, Wang X (2020) Tomato diseases and pests detection based on improved Yolo V3 convolutional neural network. Front Plant Sci 11:898

    Article  Google Scholar 

  41. Argüeso D, Picon A, Irusta U, Medela A, San-Emeterio MG, Bereciartua A, Alvarez-Gila A (2020) Few-Shot Learning approach for plant disease classification using images taken in the field. Comput Electron Agric 175:105542

    Article  Google Scholar 

  42. Wang X, Liu J, Zhu X (2021) Early real-time detection algorithm of tomato diseases and pests in the natural environment. Plant Methods 17(1):1–17

    Article  Google Scholar 

  43. Liu B, Tan C, Li S, He J, Wang H (2020) A data augmentation method based on generative adversarial networks for grape leaf disease identification. IEEE Access 8:102188–102198

    Article  Google Scholar 

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

    Article  Google Scholar 

  45. Tarek H, Aly H, Eisa S, Abul-Soud M (2022) Optimized deep learning algorithms for tomato leaf disease detection with hardware deployment. Electronics 11(1):140

    Article  Google Scholar 

  46. Yuan Y, Chen L, Wu H, Li L (2021) Advanced agricultural disease image recognition technologies: a review. Info Process Agric

    Google Scholar 

  47. Hu WJ, Fan J, Du YX, Li BS, Xiong N, Bekkering E (2020) MDFC-ResNet: an agricultural IoT system to accurately recognize crop diseases. IEEE Access 8:115287–115298

    Article  Google Scholar 

  48. Rangarajan AK, Purushothaman R, Ramesh A (2018) Tomato crop disease classification using pre-trained deep learning algorithm. Procedia Comput Sci 133:1040–1047

    Article  Google Scholar 

  49. Zhong Y, Zhao M (2020) Research on deep learning in apple leaf disease recognition. Comput Electron Agric 168:105146. https://www.overleaf.com/project/62a34ad1ec9409bba29b64aa

  50. Lin K, Gong L, Huang Y, Liu C, Pan J (2019) Deep learning-based segmentation and quantification of cucumber powdery mildew using convolutional neural network. Front Plant Sci 10:155

    Article  Google Scholar 

  51. Fuentes A, Yoon S, Park DS (2020) Deep learning-based techniques for plant diseases recognition in real-field scenarios. In: International conference on advanced concepts for intelligent vision systems. Springer, Cham, pp 3–14

    Google Scholar 

  52. Gadekallu et al (2021) A novel PCA-whale optimization-based deep neural network model for classification of tomato plant diseases using GPU. J Real-Time Image Process 18(4):1383–1396

    Google Scholar 

  53. Zhang ZY, He XY, Sun XH, Guo LM, Wang JH, Wang FS (2015) Image recognition of maize leaf disease based on GA-SVM. Chem Eng Trans 46:199–204

    Google Scholar 

  54. Aravind KR, Raja P, Aniirudh R, Mukesh KV, Ashiwin R, Vikas G (2018) Grape crop disease classification using transfer learning approach. In: International conference on ISMAC in computational vision and bio-engineering. Springer, Cham

    Google Scholar 

  55. Jun L, Xuewei W (2020) Early recognition of tomato gray leaf spot disease based on MobileNetv2-YOLOv3 model. Plant Methods 16(1):1–16

    Google Scholar 

  56. Brahimi et al (2017) Deep learning for tomato diseases: classification and symptoms visualization. Appl Artif Intell 31(4):299–315

    Google Scholar 

  57. Brahimi M, Boukhalfa K, Moussaoui A (2020) Identification of cash crop diseases using automatic image segmentation algorithm and deep learning with expanded dataset. Comput Electron Agric 177:105712

    Article  Google Scholar 

  58. Math RKM, Dharwadkar NV (2022) Early detection and identification of grape diseases using convolutional neural networks. J Plant Dis Prot 129(3):521–532

    Article  Google Scholar 

  59. Barbedo JGA (2019) Plant disease identification from individual lesions and spots using deep learning. Biosyst Eng 180:96–107

    Article  Google Scholar 

  60. Kalaivani S, Shantharajah SP, Padma T (2020) Agricultural leaf blight disease segmentation using indices based histogram intensity segmentation approach. Multimed Tools Appl 79(13):9145–9159

    Article  Google Scholar 

  61. Zhang S, You Z, Wu X (2019) Plant disease leaf image segmentation based on superpixel clustering and EM algorithm. Neural Comput Appl 31(2):1225–1232

    Article  Google Scholar 

  62. Shantkumari M, Uma SV (2021) Grape leaf segmentation for disease identification through adaptive Snake algorithm model. Multimed Tools Appl 80(6):8861–8879

    Article  Google Scholar 

  63. Deenan S, Janakiraman S, Nagachandrabose S (2020) image segmentation algorithms for Banana leaf disease diagnosis. J Inst Eng (India): Ser C 101(5):807–820

    Google Scholar 

  64. Balasubramaniam P, Ananthi VP (2016) Segmentation of nutrient deficiency in incomplete crop images using intuitionistic fuzzy C-means clustering algorithm. Nonlinear Dyn 83(1):849–866

    Article  Google Scholar 

  65. Chouhan SS, Singh UP, Jain S (2020) Web facilitated anthracnose disease segmentation from the leaf of mango tree using radial basis function (RBF) neural network. Wirel Pers Commun 113(2):1279–1296

    Article  Google Scholar 

  66. Anand R, Veni S, Aravinth J (2016) An application of image processing techniques for detection of diseases on brinjal leaves using k-means clustering method. In: 2016 international conference on recent trends in information technology (ICRTIT). IEEE, pp 1–6

    Google Scholar 

  67. Wahab AHBA, Zahari R, Lim TH (2019) Detecting diseases in chilli plants using K-means segmented support vector machine. In: 2019 3rd international conference on imaging, signal processing and communication (ICISPC). IEEE, pp 57–61

    Google Scholar 

  68. Sai Reddy B, Neeraja S (2022) Plant leaf disease classification and damage detection system using deep learning models. Multimed Tools Appl 81:24021–24040

    Article  Google Scholar 

  69. Pravin Kumar SK, Sumithra MG, Saranya N (2019) Artificial bee colony-based fuzzy c means (ABC-FCM) segmentation algorithm and dimensionality reduction for leaf disease detection in bioinformatics. J Supercomput 75(12):8293–8311

    Article  Google Scholar 

  70. Sumithra MG, Saranya N (2021) Particle Swarm Optimization (PSO) with fuzzy c means (PSO-FCM)-based segmentation and machine learning classifier for leaf diseases prediction. Concurr Comput: Pract Exp 33(3):e5312

    Google Scholar 

  71. Kavitha Lakshmi R, Savarimuthu N (2021) DPD-DS for plant disease detection based on instance segmentation. J Ambient Intell Hum Comput 1–11

    Google Scholar 

  72. Yuan Y, Xu Z, Lu G (2021) SPEDCCNN: spatial pyramid-oriented encoder-decoder cascade convolution neural network for crop disease leaf segmentation. IEEE Access 9:14849–14866

    Article  Google Scholar 

  73. Du MG, Zhang SW (2015) Crop disease leaf image segmentation based on genetic algorithm and maximum entropy. In: Applied mechanics and materials, vol 713. Trans Tech Publications Ltd, pp 1670–1674

    Google Scholar 

  74. Patil MA, Adamuthe AC, Umbarkar AJ (2020) Smartphone and IoT based system for integrated farm monitoring. In: Techno-Societal 2018. Springer, Cham, pp 471–478

    Google Scholar 

  75. Patil MA, Manohar M (2022) Enhanced radial basis function neural network for tomato plant disease leaf image segmentation. Ecol Inform 70:101752

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the Department of CSE, School of Engineering and Technology, Christ (Deemed to be University), Kengeri Campus, Bangalore, India, that include non-financial support.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, School of Engineering and Technology, Christ (Deemed to be University), Bangalore, 560074, Karnataka, India

    Manoj A. Patil & M. Manohar

  2. Department of Information Technology, Vasavi College of Engineering, Hyderabad, 500032, Telangana, India

    Manoj A. Patil

Authors
  1. Manoj A. Patil
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Manohar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manoj A. Patil .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India

    Sandeep Kumar

  2. Department of Computer Science and Engineering, Rajasthan Technical University, Kota, Rajasthan, India

    Harish Sharma

  3. Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India

    K. Balachandran

  4. School of Civil, Environmental and Architectural Engineering, Korea University, Seoul, Korea (Republic of)

    Joong Hoon Kim

  5. South Asian University, New Delhi, India

    Jagdish Chand Bansal

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

Patil, M.A., Manohar, M. (2023). A Comprehensive Review on Crop Disease Prediction Based on Machine Learning and Deep Learning Techniques. In: Kumar, S., Sharma, H., Balachandran, K., Kim, J.H., Bansal, J.C. (eds) Third Congress on Intelligent Systems. CIS 2022. Lecture Notes in Networks and Systems, vol 608. Springer, Singapore. https://doi.org/10.1007/978-981-19-9225-4_36

Download citation

Publish with us

Policies and ethics

Search

Navigation