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A sugar beet leaf disease classification method based on image processing and deep learning

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Abstract

Leaf spot disease, which causes 10 − 50% loss in sugar beet yield, causes great damage on the leaves. This disease physiologically appears as individual circular spots on the sugar beet leaves and over time spreads to the entire leaf, resulting in complete death of the leaf. Therefore, in our study, Faster R-CNN, SSD, VGG16, Yolov4 deep learning models were used directly, and Yolov4 deep learning model with image processing was used in a hybrid way for automatic determination of leaf spot disease on sugar beet and classification of severity. The proposed hybrid method for the diagnosis of diseases and identifying the severity were trained and tested using 1040 images, and the classification accuracy rate of the most successful method was found to be 96.47%. The proposed hybrid approach showed that the combined use of image processing and deep learning models yield more successful results than the analysis made using only deep learning models. In this way, both the time spent for the diagnosis of leaf spot disease on sugar beet will be reduced and human error will be eliminated, and the relevant pesticides will be sprayed to the plant at the right time.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Abade A, Ferreira PA, Vidal FB (2021) Plant diseases recognition on images using convolutional neural networks: a systematic review. Comput Electron Agric 185:106125. https://doi.org/10.1016/j.compag.2021.106125

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Adem K, Közkurt C (2019) Defect detection of seals in multilayer aseptic packages using deep learning. Turkish J Electr Eng Comput Sci 27(6):4220–4230

    Article  Google Scholar 

  4. Agarwal M, Gupta SK, Biswas KK (2020) Development of efficient CNN model for tomato crop disease identification.  Sustain Comput: Inform Syst 28:100407. https://doi.org/10.1016/j.suscom.2020.100407

    Article  Google Scholar 

  5. Altas Z, Ozguven MM, Yanar Y (2018) Determination of sugar beet leaf spot disease level (Cercospora beticola Sacc.) with image processing technique by using drone. Curr Investigations Agric Curr Res 5(3):621–631. https://doi.org/10.32474/CIACR.2018.05.000214

    Article  Google Scholar 

  6. Ampatzidis Y, De Bellis L, Luvisi A (2017) iPathology: robotic applications and management of plants and plant diseases. Sustainability 9(6):1010. https://doi.org/10.3390/su9061010

    Article  Google Scholar 

  7. Asraf A, Islam M, Haque M (2020) Deep learning applications to combat novel coronavirus (COVID-19) pandemic. SN Comput Sci 1(6):1–7

    Article  Google Scholar 

  8. Avelino J, Cristancho M, Georgiou S, Imbach P, Aguilar L, Bornemann G (2015) The coffee rust crises in Colombia and Central America (2008–2013): impacts, plausible causes and proposed solutions. Food Secur 7(2):303–321

    Article  Google Scholar 

  9. Ayon SI, Islam MM (2019) Diabetes prediction: a deep learning approach. Int J Inform Eng Electron Bus 12(2):21

    Google Scholar 

  10. Bai X, Li X, Fu Z, Lv X, Zhang L (2017) A fuzzy clustering segmentation method based on neighborhood grayscale ınformation for defining cucumber leaf spot disease images. Comput Electron Agric 136:157–165. https://doi.org/10.1016/j.compag.2017.03.004

    Article  Google Scholar 

  11. Barbedo JGA (2016) A review on the main challenges in automatic plant disease identification based on visible range images. Biosyst Eng 144:52–60

    Article  Google Scholar 

  12. Bochkovskiy A, Wang CY, Liao HYM (2020) YOLOv4: optimal speed and accuracy of object detection. ArXiv, Computer Vision and Pattern Recognition Volume, 2004, 10934

  13. Bock CH, Poole GH, Parker PE, Gottwald TR (2010) Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. CRC Crit Rev Plant Sci 29(2):59–107

    Article  Google Scholar 

  14. Camargo A, Smith JS (2009) An image processing based algorithm to automatically identify plant disease visual symptoms. Biosyst Eng 102(1):9–21

    Article  Google Scholar 

  15. Chen J, Chen J, Zhang D, Sun Y, Nanehkaran YA (2020) Using deep transfer learning for image-based plant disease identification. Comput Electron Agric 173:105393. https://doi.org/10.1016/j.compag.2020.105393

    Article  Google Scholar 

  16. Cruz AC, Luvisi A, De Bellis L, Ampatzidis Y (2017) X-FIDO: an effective application for detecting olive quick decline syndrome with deep learning and data fusion. Front Plant Sci 8:1741. https://doi.org/10.3389/fpls.2017.01741

    Article  Google Scholar 

  17. Cruz A, Ampatzidis Y, Pierro R, Materazzi A, Panattoni A, De Bellis L, Luvisi A (2019) Detection of grapevine yellows symptoms in Vitis vinifera L. with artificial intelligence. Comput Electron Agric 157:63–76. https://doi.org/10.1016/j.compag.2018.12.028

    Article  Google Scholar 

  18. Darwish A, Ezzat D, Ella Hassanien A (2020) An optimized model based on convolutional neural networks and orthogonal learning particle swarm optimization algorithm for plant diseases diagnosis. Swarm Evol Comput 52:100616. https://doi.org/10.1016/j.swevo.2019.100616

    Article  Google Scholar 

  19. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. IEEE, pp 248–255

  20. Gayathri S, Wise DJW, Shamini PB, Muthukumaran N (2020) Image analysis and detection of tea leaf disease using deep learning. In: 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE, pp 398–403

  21. Gavhale KR, Ujwalla G (2014) An overview of the research on crop leaves disease detection using image processing techniques. IOSR J Comput Eng 16(1):10–16

    Article  Google Scholar 

  22. Ghoury S, Sungur C, Durdu A (2019) Real-time diseases detection of grape and grape leaves using Faster R-CNN and SSD MobileNet Architectures. International Conference on Advanced Technologies, Computer Engineering and Science (ICATCES 2019), Apr 26–28, 2019 Alanya, Turkey

  23. Girshick R (2015) Fast R-CNN. Proceedings of the IEEE international conference on computer vision, Santiago, Chile, pp 1440–1448

  24. Gopalakrishnan K, Khaitan SK, Choudhary A, Agrawal A (2017) Deep convolutional neural networks with transfer learning for computer vision-based data-driven pavement distress detection. Constr Build Mater 157:322–330

    Article  Google Scholar 

  25. Hillnhuetter C, Mahlein AK (2008) Early detection and localization of sugar beet diseases: new approaches. Gesunde Pfianzen 60(4):143–149

    Google Scholar 

  26. Hu G, Wang H, Zhang Y, Wan M (2021) Detection and severity analysis of tea leaf blight based on deep learning. Comput Electr Eng 90:107023. https://doi.org/10.1016/j.compeleceng.2021.107023

    Article  Google Scholar 

  27. Huang H, Song Y, Yang J, Gui G, Adachi F (2019) Deep-learning-based millimeter-wave massive MIMO for hybrid precoding. IEEE Trans Veh Technol 68(3):3027–3032

    Article  Google Scholar 

  28. Islam M, Haque M, Iqbal H, Hasan M, Hasan M, Kabir MN (2020) Breast cancer prediction: a comparative study using machine learning techniques. SN Comput Sci 1(5):1–14

    Article  Google Scholar 

  29. Islam MM, Karray F, Alhajj R, Zeng J (2021) A review on deep learning techniques for the diagnosis of novel coronavirus (COVID-19). IEEE Access 9:30551–30572

    Article  Google Scholar 

  30. Jiang H, Learned-Miller E (2017) Face detection with the Faster R-CNN. In: 2017 12th IEEE international conference on automatic face & gesture recognition (FG 2017), pp 650–657

  31. Kim Y (2014) Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP, Doha, Qatar, October 2014. Association for Computational Linguistics, pp 1746–1751

  32. Krishnamoorthy N, Prasad LVN, Kumar CSP, Subedi B, Abraha HB, Sathishkumar VE (2021) Rice leaf diseases prediction using deep neural networks with transfer learning. Environ Res 198:111275. https://doi.org/10.1016/j.envres.2021.111275

    Article  Google Scholar 

  33. Kundu N, Rani G, Dhaka VS, Gupta K, Nayak SC, Verma S, Ijaz MF, Woźniak M (2021) IoT and interpretable machine learning based framework for disease prediction in pearl millet. Sensors 21:5386. https://doi.org/10.3390/s21165386

    Article  Google Scholar 

  34. Li Z, Zhou F (2017) FSSD: feature fusion single shot multibox detector. arXiv preprintarXiv:1712.00960

  35. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Yang Fu C, Berg AC (2016) SSD: single shot multibox detector. European Conference on Computer Vision, ECCV 2016: Computer Vision – ECCV 2016, pp 21–37. https://doi.org/10.1007/978-3-319-46448-0_2

  36. Liu F, Wang Y, Wang FC, Zhang YZ, Lin J (2019) Intelligent and secure content-based image retrieval for mobile users. IEEE Access 7(99):1–1. https://doi.org/10.1109/ACCESS.2019.2935222

    Article  Google Scholar 

  37. Liu C, Zhu H, Guo W, Han X, Chen C, Wu H (2021) EFDet: an efficient detection method for cucumber disease under natural complex environments. Comput Electron Agric 189:106378. https://doi.org/10.1016/j.compag.2021.106378

    Article  Google Scholar 

  38. Ma J, Du K, Zheng F, Zhang L, Gong Z, Sun Z (2018) A recognition method for cucumber diseases using leaf symptom ımages based on deep convolutional neural network. Comput Electron Agric 154:18–24. https://doi.org/10.1016/j.compag.2018.08.048

    Article  Google Scholar 

  39. Marcal ARS, Cunha M (2019) Development of an image-based system to assess agricultural fertilizer spreader pattern. Comput Electron Agric 162:380–388. https://doi.org/10.1016/j.compag.2019.04.031

    Article  Google Scholar 

  40. Mohamed FR, Smith K, Larry J (2005) Evaluating fungicides for controlling Cercospora leaf spot on sugar beet. Crop Prot 24:79–86

    Article  Google Scholar 

  41. Mutka AM, Bart RS (2015) Image-based phenotyping of plant disease symptoms. Front Plant Sci 5:734. https://doi.org/10.3389/fpls.2014.00734

    Article  Google Scholar 

  42. Nazki H, Yoon S, Fuentes A, Park DS (2019) Unsupervised image translation using adversarial networks for ımproved plant disease recognition. Comput Electron Agric 168:105117. https://doi.org/10.1016/j.compag.2019.105117

    Article  Google Scholar 

  43. Ning C, Zhou H, Song Y, Tang J (2017) Inception single shot multibox detector for object detection. Proceedings of the IEEE International Conference on Multimedia and Expo Workshops (ICMEW) 10–14 July 2017. IEEE, 978-1-5386-0560-8/17

  44. Ozguven MM (2018) The newest agricultural technologies. Curr Investigations Agric Curr Res 5(1):573–580. https://doi.org/10.32474/CIACR.2018.05.000201

    Article  Google Scholar 

  45. Özgüven MM (2018) Hassas tarım. Akfon Yayınları, Ankara (in Turkish). ISBN: 978-605-68762-4-0

  46. Özgüven MM (2019) Technological concepts and their differences. International Erciyes Agriculture, Animal & Food Sciences Conference 24–27 April 2019- Erciyes University – Kayseri, Turkiye

  47. Ozguven MM (2020) Deep learning algorithms for automatic detection and classification of mildew disease in cucumber. Fresenius Environ Bull 29:7081–7087

    Google Scholar 

  48. Ozguven MM, Adem K (2019) Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms. Physica A 535:122537

    Article  Google Scholar 

  49. Ozguven MM, Altas Z (2022) A new approach to detect mildew disease on cucumber (Pseudoperonospora cubensis) leaves with image processing. J Plant Pathol. https://doi.org/10.1007/s42161-022-01178-z

  50. Özgüven MM, Beyaz A, Ormanoğlu N, Aktaş T, Emekci M, Ferizli AG, Çilingir İ, Çolak A (2020) Hasat Sonrası Ürünlerin Korunmasına Yönelik Mekanizasyon Otomasyon ve Mücadele Teknikleri. Türkiye Ziraat Mühendisliği IX. Teknik Kongresi. Ocak 2020, Ankara. Bildiriler Kitabı-1, s.301–324 (In Turkish)

  51. Pal S (2016) Transfer learning and fine tuning for cross domain image classification with Keras. GitHub: transfer learning and fine tuning for cross domain image classification with Keras

  52. 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

  53. Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 6:1137–1149

    Article  Google Scholar 

  54. Ren Y, Zhu C, Xiao S (2018) Object detection based on Fast/Faster RCNN employing fully convolutional architectures. Hindawi Math Probl Eng 3598316, 7 pages. https://doi.org/10.1155/2018/3598316

  55. Rossi V (1995) Effect of host resistance in decreasing infection rate of cercospora leaf spot epidemics on sugarbeet. Phytopathol Mediterr 34:149–156

    Google Scholar 

  56. Rumpf T, Mahlein AK, Steiner U, Oerke E-C, Dehne H-W, Plümer L (2010) Early detection and classification of crop diseases with support vector machines based on hyperspectral reflectance. Comput Electron Agric 74(1):91–99. https://doi.org/10.1016/j.compag.2010.06.009

    Article  Google Scholar 

  57. Savary S, Willocquet L (2014) Simulation modeling in botanical epidemiology and crop loss analysis. The plant health instructor, 173p

  58. Sharma P, Berwal YPS, Ghai W (2020) Performance analysis of deep learning CNN models for disease detection in plants using image segmentation. Inform Process Agric 7(4):566–574. https://doi.org/10.1016/j.inpa.2019.11.001

    Article  Google Scholar 

  59. Shin J, Chang YK, Heung B, Nguyen-Quang T, Price GW, Al-Mallahi A (2021) A deep learning approach for RGB image-based powdery mildew disease detection on strawberry leaves. Comput Electron Agric 183:106042. https://doi.org/10.1016/j.compag.2021.106042

    Article  Google Scholar 

  60. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. The 3rd International Conference on Learning Representations (ICLR2015). https://arxiv.org/abs/1409.1556

  61. Song HA, Lee S-Y (2013) Hierarchical representation using NMF. International conference on neural information processing, pp 466–473

  62. Soylu S, Boyraz N, Zengin M, Şahin M, Değer T, Sarı S, Erence Y (2012) Bitkisel Üretim Çiftçi Rehberi. Konya Şeker A.Ş. Konya

  63. Sujatha R, Chatterjee JM, Jhanjhi NZ, Brohi SN (2021) Performance of deep learning vs machine learning in plant leaf disease detection. Microprocess Microsyst 80:103615. https://doi.org/10.1016/j.micpro.2020.103615

    Article  Google Scholar 

  64. Temniranrat P, Kiratiratanapruk K, Kitvimonrat A, Sinthupinyo W, Patarapuwadol S (2021) A system for automatic rice disease detection from rice paddy images serviced via a chatbot. Comput Electron Agric 185:106156. https://doi.org/10.1016/j.compag.2021.106156

    Article  Google Scholar 

  65. Tetila EC, Machado BB, Astolfi G, De Souza Belete NA, Amorim WP, Roel AR, Pistori H (2020) Detection and classification of soybean pests using deep learning with UAV images. Comput Electron Agric 179:105836. https://doi.org/10.1016/j.compag.2020.105836

    Article  Google Scholar 

  66. Vaidya B, Paunwala C (2019) Deep learning architectures for object detection and classification. In: Smart techniques for a smarter planet, pp 53–79

  67. Wang Q, Qi F (2019) Tomato diseases recognition based on Faster RCNN. IEEE, 10th International Conference on Information Technology in Medicine and Education (ITME), 78-1-7281–3918–0. https://doi.org/10.1109/ITME.2019.00176

  68. Wang C, Du P, Wu H, Li J, Zhao C, Zhu H (2021) A cucumber leaf disease severity classification method based on the fusion of DeepLabV3 + and U-Net. Comput Electron Agric 189:106373. https://doi.org/10.1016/j.compag.2021.106373

    Article  Google Scholar 

  69. Whitney ED, Duffus JE (1991) Compendium of beet diseases and insects. The American Phytopathological Society, APS PRESS. ISBN O-89054-070-5, USA S:8

  70. Wu D, Lv S, Jiang M, Song H (2020) Using channel pruning-based YOLO v4 deep learning algorithm for the real-time and accurate detection of apple flowers in natural environments. Comput Electron Agric 178:105742

    Article  Google Scholar 

  71. Yadav S, Sengar N, Singh A, Singh A, Dutta MK (2021) Identification of disease using deep learning and evaluation of bacteriosis in peach leaf. Ecol Inf 61:101247. https://doi.org/10.1016/j.ecoinf.2021.101247

    Article  Google Scholar 

  72. Yu S, Xiao D, Kanagasingam Y (2017) Exudate detection for diabetic retinopathy with convolutional neural networks. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp 1744–1747

  73. Zhang S, Zhang S, Zhang C, Wang X, Shi Y (2019) Cucumber leaf disease identification with global pooling dilated convolutional neural network. Comput Electron Agric 162:422–430. https://doi.org/10.1016/j.compag.2019.03.012

    Article  Google Scholar 

  74. Zhang K, Wu Q, Chen Y (2021) Detecting soybean leaf disease from synthetic image using multi-feature fusion Faster R-CNN. Comput Electron Agric 183:106064. https://doi.org/10.1016/j.compag.2021.106064

    Article  Google Scholar 

  75. Zhao C, Chan SSF, Cham WK, Chu LM (2015) Plant identification using leaf shapes—a pattern counting approach. Pattern Recogn 48(10):3203–3215. https://doi.org/10.1016/j.patcog.2015.04.004

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering, Sivas University of Science and Technology, Sivas, Turkey

    Kemal Adem

  2. Department of Biosystems Engineering, Tokat Gaziosmanpasa University, Tokat, Turkey

    Mehmet Metin Ozguven & Ziya Altas

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  1. Kemal Adem
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  2. Mehmet Metin Ozguven
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Adem, K., Ozguven, M.M. & Altas, Z. A sugar beet leaf disease classification method based on image processing and deep learning. Multimed Tools Appl 82, 12577–12594 (2023). https://doi.org/10.1007/s11042-022-13925-6

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