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Handling hypercolumn deep features in machine learning for rice leaf disease classification

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Abstract

Rice leaf disease, which is a plant disease, causes a decrease in rice production and more importantly, environmental pollution. 10–15% of the losses in rice production are due to rice plant diseases. Automatic recognition of rice leaf disease by computer-assisted expert systems is a promising solution to overcome this problem and to bear the shortage of field experts in this field. Many studies have been conducted using features extracted from deep learning architectures, so far. This study includes keypoint detection on the image, hypercolumn deep feature extraction from CNN layers, and classification stages. The hypercolumn is a vector that contains the activations of all CNN layers for a pixel. Keypoints are prominent points in the images that define what stands out in the image. The first step of the model proposed in this study includes the detection of keypoints on the image and then the extraction of hypercolumn features based on the interest points. In the second step, machine learning experiments are carried out by running classifier algorithms on the features extracted. The evaluation results show that the proposed approach in this paper can detect rice leaf diseases. Furthermore, the Random Forest classifier presented a very successful performance on hypercolumn deep features with 93.06% accuracy, 89.58% sensitivity, 94.79% specificity, and 89.58% precision. As a result, the proposed approach can be integrated into computer-aided rice leaf disease diagnosis systems and so support field experts.

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References

  1. Al-Hiary H, Bani-Ahmad S, Reyalat M, et al (2011) Fast and accurate detection and classification of plant diseases

  2. Alshdaifat NFF, Talib AZ, Osman MA (2020) Improved deep learning framework for fish segmentation in underwater videos. Ecol Inform 59:101121. https://doi.org/10.1016/j.ecoinf.2020.101121

    Article  Google Scholar 

  3. Anagnostis A, Tagarakis AC, Asiminari G, Papageorgiou E, Kateris D, Moshou D, Bochtis D (2021) A deep learning approach for anthracnose infected trees classification in walnut orchards. Comput Electron Agric 182:105998. https://doi.org/10.1016/j.compag.2021.105998

    Article  Google Scholar 

  4. Arivazhagan S, Shebiah RN, Ananthi S, Varthini SV (2013) Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features

  5. Bari BS, Islam MN, Rashid M, Hasan MJ, Razman MAM, Musa RM, Ab Nasir AF, Majeed APA (2021) A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework. PeerJ Comput Sci 7:e432. https://doi.org/10.7717/PEERJ-CS.432/SUPP-1

    Article  Google Scholar 

  6. Bhattacharya S, Mukherjee A, Phadikar S (2020) A deep learning approach for the classification of Rice leaf diseases. In: Advances in Intelligent Systems and Computing. Springer, pp. 61–69

  7. Capinha C, Ceia-Hasse A, Kramer AM, Meijer C (2021) Deep learning for supervised classification of temporal data in ecology. Ecol Inform:101252. https://doi.org/10.1016/j.ecoinf.2021.101252

  8. Chen J, Zhang D, Zeb A, Nanehkaran YA (2021) Identification of rice plant diseases using lightweight attention networks. Expert Syst Appl 169:114514. https://doi.org/10.1016/j.eswa.2020.114514

    Article  Google Scholar 

  9. Garcia J, Barbedo A (2013) Digital image processing techniques for detecting, quantifying and classifying plant diseases

  10. Ghosal S, Sarkar K (2020) Rice leaf diseases classification using CNN with transfer learning. In: 2020 IEEE Calcutta conference, CALCON 2020 - proceedings. Institute of Electrical and Electronics Engineers Inc., pp 230–236

  11. Gutiérrez S, Hernández I, Ceballos S, Barrio I, Díez-Navajas AM, Tardaguila J (2021) Deep learning for the differentiation of downy mildew and spider mite in grapevine under field conditions. Comput Electron Agric 182:105991. https://doi.org/10.1016/j.compag.2021.105991

    Article  Google Scholar 

  12. Jalal A, Salman A, Mian A, Shortis M, Shafait F (2020) Fish detection and species classification in underwater environments using deep learning with temporal information. Ecol Inform 57:101088. https://doi.org/10.1016/j.ecoinf.2020.101088

    Article  Google Scholar 

  13. Jiang F, Lu Y, Chen Y, Cai D, Li G (2020) Image recognition of four rice leaf diseases based on deep learning and support vector machine. Comput Electron Agric 179:105824. https://doi.org/10.1016/j.compag.2020.105824

    Article  Google Scholar 

  14. 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. https://doi.org/10.1016/j.ecoinf.2020.101197

    Article  Google Scholar 

  15. Kahl S, Wood CM, Eibl M, Klinck H (2021) BirdNET: a deep learning solution for avian diversity monitoring. Ecol Inform 61:101236. https://doi.org/10.1016/j.ecoinf.2021.101236

    Article  Google Scholar 

  16. Khirade SD, Patil AB (2015) Plant disease detection using image processing. In: proceedings - 1st international conference on computing, communication, control and automation, ICCUBEA 2015. Institute of Electrical and Electronics Engineers Inc., pp 768–771

  17. Lee SH, Goëau H, Bonnet P, Joly A (2020) New perspectives on plant disease characterization based on deep learning. Comput Electron Agric 170:105220. https://doi.org/10.1016/j.compag.2020.105220

    Article  Google Scholar 

  18. Liu X, Wang C, Bai J, Liao G (2020) Fine-tuning pre-trained convolutional neural networks for gastric precancerous disease classification on magnification narrow-band imaging images. Neurocomputing 392:253–267. https://doi.org/10.1016/J.NEUCOM.2018.10.100

    Article  Google Scholar 

  19. Lu Y, Yi S, Zeng N, Liu Y, Zhang Y (2017) Identification of rice diseases using deep convolutional neural networks. Neurocomputing 267:378–384. https://doi.org/10.1016/J.NEUCOM.2017.06.023

    Article  Google Scholar 

  20. Lumini A, Nanni L (2019) Deep learning and transfer learning features for plankton classification. Ecol Inform 51:33–43. https://doi.org/10.1016/j.ecoinf.2019.02.007

    Article  Google Scholar 

  21. Majeed Y, Zhang J, Zhang X, Fu L, Karkee M, Zhang Q, Whiting MD (2020) Deep learning based segmentation for automated training of apple trees on trellis wires. Comput Electron Agric 170:105277. https://doi.org/10.1016/j.compag.2020.105277

    Article  Google Scholar 

  22. Patidar S, Pandey A, Shirish BA, Sriram A (2020) Rice Plant disease detection and classification using deep residual learning. In: Communications in Computer and Information Science. Springer, pp. 278–293

  23. Peng S, Tang Q, Zou Y (2009) Current status and challenges of rice production in China. Plant Prod Sci 12:3–8

    Article  Google Scholar 

  24. Phadikar S (2012) Classification of Rice leaf diseases based onMorphological changes. International Journal of Information and Electronics Engineering https://doi.org/10.7763/ijiee.2012.v2.137

  25. Prajapati HB, Shah JP, Dabhi VK (2017) Detection and classification of rice plant diseases. Intel Decision Technol 11:357–373. https://doi.org/10.3233/IDT-170301

    Article  Google Scholar 

  26. Ramcharan A, Baranowski K, McCloskey P, Ahmed B, Legg J, Hughes DP (2017) Deep learning for image-based cassava disease detection. Front Plant Sci 8:8. https://doi.org/10.3389/fpls.2017.01852

    Article  Google Scholar 

  27. Rosin PL (1999) Measuring corner properties. Comput Vis Image Underst 73:291–307. https://doi.org/10.1006/CVIU.1998.0719

    Article  Google Scholar 

  28. Rublee E, Rabaud V, Konolige K, Bradski G (2011) ORB: an efficient alternative to SIFT or SURF. In: 2011 international conference on computer vision. IEEE, pp 2564–2571

  29. Sethy PK, Barpanda NK, Rath AK, Behera SK (2020) Deep feature based rice leaf disease identification using support vector machine. Comput Electron Agric 175:105527. https://doi.org/10.1016/j.compag.2020.105527

    Article  Google Scholar 

  30. Sharif H, Hölzel M (2017) A comparison of prefilters in ORB-based object detection. Pattern Recogn Lett 93:154–161. https://doi.org/10.1016/J.PATREC.2016.11.007

    Article  Google Scholar 

  31. Shivali Amit W, Harikrishnan R (2021) A deep learning-based approach in classification and validation of tomato leaf disease. Traitement du signal 38:699–709. https://doi.org/10.18280/TS.380317

  32. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. 3rd international conference on learning representations, ICLR 2015 - conference track proceedings

  33. Tang H, Wang B, Chen X (2020) Deep learning techniques for automatic butterfly segmentation in ecological images. Comput Electron Agric 178:105739. https://doi.org/10.1016/j.compag.2020.105739

    Article  Google Scholar 

  34. Tetila EC, Machado BB, Astolfi G, Belete NAS, 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 

  35. Tian L, Xue B, Wang Z, Li D, Yao X, Cao Q, Zhu Y, Cao W, Cheng T (2021) Spectroscopic detection of rice leaf blast infection from asymptomatic to mild stages with integrated machine learning and feature selection. Remote Sens Environ 257:112350. https://doi.org/10.1016/j.rse.2021.112350

    Article  Google Scholar 

  36. Toğaçar M, Özkurt KB, Ergen B, Cömert Z (2020) BreastNet: a novel convolutional neural network model through histopathological images for the diagnosis of breast cancer. Phy A: Statis Mechan App 545:123592. https://doi.org/10.1016/J.PHYSA.2019.123592

    Article  Google Scholar 

  37. Toğaçar M, Özkurt KB, Ergen B, Cömert Z (2020) BreastNet: a novel convolutional neural network model through histopathological images for the diagnosis of breast cancer. Phy A: Statis Mechan App 545:123592. https://doi.org/10.1016/J.PHYSA.2019.123592

    Article  Google Scholar 

  38. Toğaçar M, Cömert Z, Ergen B (2021) Enhancing of dataset using DeepDream, fuzzy color image enhancement and hypercolumn techniques to detection of the Alzheimer’s disease stages by deep learning model. Neural Comput & Applic 33:1–13. https://doi.org/10.1007/s00521-021-05758-5

    Article  Google Scholar 

  39. Villon S, Mouillot D, Chaumont M, Darling ES, Subsol G, Claverie T, Villéger S (2018) A deep learning method for accurate and fast identification of coral reef fishes in underwater images. Ecol Inform 48:238–244. https://doi.org/10.1016/j.ecoinf.2018.09.007

    Article  Google Scholar 

  40. 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. https://doi.org/10.3389/FPLS.2020.00751/BIBTEX

    Article  Google Scholar 

  41. Xiong Y, Liang L, Wang L, She J, Wu M (2020) Identification of cash crop diseases using automatic image segmentation algorithm and deep learning with expanded dataset. Comput Electron Agric 177:105712. https://doi.org/10.1016/j.compag.2020.105712

    Article  Google Scholar 

  42. Yan Q, Yang B, Wang W, Wang B, Chen P, Zhang J (2020) Apple leaf diseases recognition based on an improved convolutional neural network. Sensors 20:3535. https://doi.org/10.3390/s20123535

    Article  Google Scholar 

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Acknowledgements

Author would like to thank Prajapati et al. [25] to provide the public rice leaf dataset.

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No funding is declared.

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The author declares that he has no conflicts of interest.

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  1. Department of Computer Engineering, Kastamonu University, Kastamonu, Turkey

    Kemal Akyol

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Akyol, K. Handling hypercolumn deep features in machine learning for rice leaf disease classification. Multimed Tools Appl 82, 19503–19520 (2023). https://doi.org/10.1007/s11042-022-14318-5

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