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Articles

A novel residual learning-based deep learning model integrated with attention mechanism and SVM for identifying tea plant diseases

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Pages 471-484 | Received 30 Mar 2023, Accepted 07 Jul 2023, Published online: 20 Jul 2023

References

  • Yang H, Xue X, Li H, et al. The relative antioxidant activity and steric structure of green tea catechins–a kinetic approach. Food Chem. 2018;257:399–405. doi: 10.1016/j.foodchem.2018.03.043
  • India TB. State/region wise and month wise tea production data for the year 2021(final) – qty. in m.kgs, https://rb.gy/idee3t.
  • India TB. Major country wise exports, https://rb.gy/ttfalz.
  • Singh RS. Plant diseases. New Delhi,India: Oxford and IBH Publishing; 2018.
  • Chen Z, Chen X. The diagnosis of tea diseases and their control (in chinese) shanghai. Shanghai, China: Shanghai Scientific and Technical Publishers; 1990. p. 32–41.
  • Barthakur B. Recent approach of tocklai to plant protection in tea in North East India. Sci Cult. 2011;77(9/10):381–384.
  • Pandey AK, Sinniah GD, Babu A, et al. How the global tea industry copes with fungal diseases–challenges and opportunities. Plant Disease. 2021;105(7):1868–1879. doi: 10.1094/PDIS-09-20-1945-FE
  • Karunamoorthy B, Somasundereswari D. A defect tea leaf identification using image processing. Przeglad Elektrotechniczny. 2013;89:318–320. http://pe.org.pl/articles/2013/9/69.pdf.
  • Sun Y, Jiang Z, Zhang L, et al. Slic_svm based leaf diseases saliency map extraction of tea plant. Comput Electron Agriculture. 2019;157:102–109. doi: 10.1016/j.compag.2018.12.042
  • Mukhopadhyay S, Paul M, Pal R, et al. Tea leaf disease detection using multi-objective image segmentation. Multimed Tools Appl. 2021;80(1):753–771. doi: 10.1007/s11042-020-09567-1
  • Steven S. Tea leaf pest detection using support vector machine (svm) method in ptpn iv unit bah butong. INFOKUM. 2021 June;9(2):299–305. https://seaninstitute.org/infor/index.php/infokum/article/view/127.
  • Srivastava AR, Venkatesan M . Tea leaf disease prediction using texture-based image processing, in: Emerging Research in Data Engineering Systems and Computer Communications, Springer;2020.Tirupati, India p. 17-25
  • Karmokar BC, Ullah MS, Siddiquee MK, et al. Tea leaf diseases recognition using neural network ensemble. Int J Comput Appl. 2015;114(17):975–8887. doi: 10.5120/20071-1993
  • Billah M, Miah MBA, Hanifa A, et al. Adaptive neuro fuzzy inference system based tea leaf disease recognition using color wavelet. Commun Appl Electron. 2015;3(5):1–4. doi: 10.5120/cae2015651943
  • Jha S, Jain U, Kende A, et al. Disease detection in tea leaves using image processing. Int J Pharma Bio Sci. 2016;7(3):165–171.
  • Meng S, Wang S, Zhou T. Identification of tea red leaf spot and tea red scab based on hybrid feature optimization.China.Journal of Physics: Conference Series, Vol. 1486.2020. IOP Publishing .p. 052023
  • Binh PT, Nhung TC, Du DH. Detection and diagnosis gray spots on tea leaves using computer vision and multi-layer perceptron.Vietnam.Advances in Engineering Research and Application: Proceedings of the International Conference on Engineering Research and Applications, ICERA.2019.Springer, 2020. pp. 229–237
  • Ait El Asri S, Negabi I, El Adib S, et al. Enhancing building extraction from remote sensing images through unet and transfer learning. Int J Comput Appl. 2023; 45, 2023 - Issue 5:1–7. doi: 10.1080/1206212X.2023.2219117
  • Madhukar B, Bharathi S, Polnaya AM. Multi-scale convolution based breast cancer image segmentation with attention mechanism in conjunction with war search optimization. Int J Comput Appl. 2023;45:1–14. doi: 10.1080/1206212X.2023.2212945
  • Sibiya M, Sumbwanyambe M. A computational procedure for the recognition and classification of maize leaf diseases out of healthy leaves using convolutional neural networks. AgriEngineering. 2019;1(1):119–131. doi: 10.3390/agriengineering1010009
  • Fuentes A, Yoon S, Kim SC, et al. A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors. 2017;17(9):2022. doi: 10.3390/s17092022
  • Ahmad I, Hamid M, Yousaf S, et al. Optimizing pretrained convolutional neural networks for tomato leaf disease detection. Complexity. 2020;2020:1–6. doi: 10.1155/2020/8812019
  • Zhang K, Wu Q, Liu A, et al. Can deep learning identify tomato leaf disease? Adv. Multimedia. 2018;2018:6710865. doi: 10.1155/2018/6710865
  • Tahir MB, Khan MA, Javed K, et al. Withdrawn: recognition of apple leaf diseases using deep learning and variances-controlled features reduction. Microprocess Microsyst. 2021: 104027. 104027. doi: 10.1016/j.micpro.2021.104027
  • Selvaraj MG, Vergara A, Ruiz H, et al. Ai-powered banana diseases and pest detection. Plant Methods. 2019;15:1–11. doi: 10.1186/s13007-018-0385-5
  • Ma J, Du K, Zheng F, et al. A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network. Comput Electron Agric. 2018;154:18–24. doi: 10.1016/j.compag.2018.08.048
  • Singh UP, Chouhan SS, Jain S, et al. Multilayer convolution neural network for the classification of mango leaves infected by anthracnose disease. IEEE Access. 2019;7:43721–43729. doi: 10.1109/Access.6287639
  • Howlader MR, Habiba U, Faisal RH, et al. Automatic recognition of guava leaf diseases using deep convolution neural network, in: 2019 international conference on electrical, computer and communication engineering (ECCE), IEEE, Bangladesh, 2019, pp. 1–5
  • Chen J, Liu Q, Gao L. Visual tea leaf disease recognition using a convolutional neural network model. Symmetry. 2019;11(3):343. doi: 10.3390/sym11030343
  • Xiaoxiao S, Shaomin M, Yongyu X , et al. Image recognition of tea leaf diseases based on convolutional neural network, in: 2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), IEEE, China, 2018, pp. 304–309
  • Bhowmik S, Talukdar AK, Sarma KK. Detection of disease in tea leaves using convolution neural network, in: 2020 Advanced Communication Technologies and Signal Processing (ACTS), IEEE, India, 2020, pp. 1–6
  • Latha R, Sreekanth G, Suganthe R, et al. Automatic detection of tea leaf diseases using deep convolution neural network, in: 2021 International Conference on Computer Communication and Informatics (ICCCI), IEEE, India, 2021, pp. 1–6
  • Gayathri S, Wise DJW, Shamini PB, et al. Image analysis and detection of tea leaf disease using deep learning, in: 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), IEEE, New Jersey, 2020, pp. 398–403
  • Krisnandi D, Pardede HF, Yuwana RS, et al. Diseases classification for tea plant using concatenated convolution neural network. CommIT (Communication and Information Technology) J. 2019;13(2):67–77. doi: 10.21512/commit.v13i2.5886
  • Hu G, Yang X, Zhang Y, et al. Identification of tea leaf diseases by using an improved deep convolutional neural network. Sustainable Comput: Inf Syst. 2019;24:100353. doi: 10.1016/j.suscom.2019.100353
  • Mao T, Liu F, Huang B, et al. Research on the method of tea disease recognition based on deep learning, in: New Developments of IT, IoT and ICT Applied to Agriculture, Springer, Singapore, 2021, pp. 119–128.
  • Ramdan A, Heryana A, Arisal A, et al. Transfer learning and fine-tuning for deep learning-based tea diseases detection on small datasets, in: 2020 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET), IEEE, New Jersey, 2020, pp. 206–211.
  • Lee S-H, Lin S-R, Chen S-F. Identification of tea foliar diseases and pest damage under practical field conditions using a convolutional neural network. Plant Pathol. 2020;69(9):1731–1739. doi: 10.1111/ppa.v69.9
  • Hu G, Wang H, Zhang Y, et al. Detection and severity analysis of tea leaf blight based on deep learning. Comput Electr Eng. 2021;90:107023. doi: 10.1016/j.compeleceng.2021.107023
  • Bao W, Fan T, Hu G, et al. Detection and identification of tea leaf diseases based on ax-retinanet. Sci Rep. 2022;12(1):2183. doi: 10.1038/s41598-022-06181-z
  • Xue Z, Xu R, Bai D, et al. Yolo-tea: a tea disease detection model improved by yolov5. Forests. 2023;14(2):415. doi: 10.3390/f14020415
  • Wang Y, Xu R, Bai D, et al. Integrated learning-based pest and disease detection method for tea leaves. Forests. 2023;14(5):1012. doi: 10.3390/f14051012
  • Soeb MJA, Jubayer MF, Tarin TA, et al. Tea leaf disease detection and identification based on yolov7 (yolo-t). Sci Rep. 2023;13(1):6078. doi: 10.1038/s41598-023-33270-4
  • He K, Zhang X, Ren S, et al. Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, IEEE, Las Vegas, NV, USA, 2016, pp. 770–778.
  • Howard AG, Zhu M, Chen B, et al. Mobilenets: efficient convolutional neural networks for mobile vision applications. Preprint arXiv:1704.04861, 2017.
  • Elleuch M, Maalej R, Kherallah M. A new design based-svm of the cnn classifier architecture with dropout for offline arabic handwritten recognition. Procedia Comput Sci. 2016;80:1712–1723. doi: 10.1016/j.procs.2016.05.512
  • Woo S, Park J, Lee J-Y, et al. Cbam: Convolutional block attention module, in: Proceedings of the European conference on computer vision (ECCV), Springer, KAIST, Korea, 2018, pp. 3–19.

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