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Apple leaf disease recognition method with improved residual network

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Abstract

The occurrence of apple diseases has dramatically affected the quality and yield of apples. Disease monitoring is an important measure to ensure the healthy development of the apple industry. Based on a residual network (ResNet50), this paper proposes an MSO-ResNet (multistep optimization ResNet) apple leaf disease recognition model. By decomposing the convolution kernel, updating the identity mapping method, reducing the number of residual modules, and replacing the batch normalization layer, the identification accuracy and speed of the model are improved, and the number of model parameters is reduced. The experimental results show that the average precision, recall, and F1-score of the proposed model for leaf disease identification are 0.957, 0.958, and 0.957, respectively. The parameter memory is 14.77 MB, and the recognition time of each image is only 25.84 ms. The overall performance of the proposed model was better than that of the other models. The proposed model in this paper has high recognition performance and strong robustness and can provide critical technical support for the automatic recognition of apple leaf diseases.

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References

  1. Abbas A, Jain S, Gour M, Vankudothu S (2021) Tomato plant disease detection using transfer learning with C-GAN synthetic images. Computers and Electronics in Agriculture 187:106279

    Article  Google Scholar 

  2. Atila Ü, Uçar M, Akyol K, Uçar E (2021) Plant leaf disease classification using EfficientNet deep learning model. Ecological Informatics 61:101182

    Article  Google Scholar 

  3. Cao X, Cao T, Gao F, Guan X (2021) Risk-averse storage planning for improving RES hosting capacity under uncertain siting choice. IEEE transactions on sustainable energy. https://doi.org/10.1109/TSTE.2021.3075615

  4. Chaudhary A, Thakur R, Kolhe S, Kamal R (2020) A particle swarm optimization based ensemble for vegetable crop disease recognition. Computers and Electronics in Agriculture 178:105747

    Article  Google Scholar 

  5. Chen Y, Chen W, Chandra Pal S, Saha A, Chowdhuri I, Adeli B, Janizadeh S, Dineva AA, Wang X, Mosavi A (2021) Evaluation efficiency of hybrid deep learning algorithms with neural network decision tree and boosting methods for predicting groundwater potential. Geocarto Int:1–21

  6. Chen J, Chen J, Zhang D, Sun Y, Nanehkaran YA (2020) Using deep transfer learning for image-based plant disease identification. Computers and Electronics in Agriculture 173:105393

    Article  Google Scholar 

  7. Chen C, Wu Q, Li Z, Xiao L, Hu ZY (2020) Diagnosis of Alzheimer's disease based on deeply-fused nets. Comb Chem High Throughput Screen 24:781–789

    Article  Google Scholar 

  8. Duta IC, Liu L, Zhu F, Shao L (2020) Improved residual networks for image and video recognition. 25th international conference on pattern recognition (ICPR)

  9. Fei X, Wang J, Ying S, Hu Z, Shi J (2020) Projective parameter transfer based sparse multiple empirical kernel learning Machine for diagnosis of brain disease. Neurocomputing 413:271–283

    Article  Google Scholar 

  10. Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture 145:311–318

    Article  Google Scholar 

  11. Gokulnath BV, Usha DG (2021) Identifying and classifying plant disease using resilient LF-CNN. Ecological Informatics 63:101283

    Article  Google Scholar 

  12. Gong Y, Liu L, Ming Y, Bourdev L (2014) Compressing deep convolutional networks using vector quantization. Comput Sci

  13. Grinblat GL, Uzal LC, Larese MG, Granitto PM (2016) Deep learning for plant identification using vein morphological patterns. Computers and Electronics in Agriculture 127:418–424

    Article  Google Scholar 

  14. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. 2016 IEEE conference on computer vision and pattern recognition (CVPR)

  15. Hu Z, Wang J, Zhang C, Luo Z, Luo X, Xiao L et al. (2021) Uncertainty modeling for multi center autism Spectrum disorder classification using Takagi-Sugeno-Kang fuzzy systems. IEEE Trans Cogn Dev Syst

  16. Huang G, Liu Z, Laurens V, Weinberger KQ (2016) Densely connected convolutional networks. IEEE Computer Society

  17. Huang P, Zhao L, Jiang R, Wang T, Zhang X (2021) Self-filtering image dehazing with self-supporting module. Neurocomputing 432:57–69

    Article  Google Scholar 

  18. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. 32nd international conference on machine learning. Available: http://research.google.com/pubs/archive/43442.pdf

  19. Jiang Z, Dong Z, Jiang W, Yang Y (2021) Recognition of rice leaf diseases and wheat leaf diseases based on multi-task deep transfer learning. Computers and Electronics in Agriculture 186:106184

    Article  Google Scholar 

  20. Jiang W, Jiang K, Zhang X, Ma Y (2015) Energy Optimization of Security-Critical Real-Time Applications with Guaranteed Security Protection. Journal of Systems Architecture 61:282–292

    Article  Google Scholar 

  21. Jiang F, Lu Y, Chen Y, Cai D, Li GF (2020) Image recognition of four rice leaf diseases based on deep learning and support vector machine. Computers and Electronics in Agriculture 179(2):105824

    Article  Google Scholar 

  22. Jiang W, Song Z, Zhan J, He Z, Wen X, Jiang K (2020) Optimized co-scheduling of mixed-precision neural network accelerator for real-time multitasking applications. Journal of Systems Architecture 110:101775

    Article  Google Scholar 

  23. 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. Ecological Informatics 61:101197

    Article  Google Scholar 

  24. Kamath R, Balachandra M, Prabhu S (2020) Crop and weed discrimination using Laws' texture masks. International Journal of Agricultural and Biological Engineering 13:191–197

    Article  Google Scholar 

  25. Krishnaswamy Rangarajan A, Purushothaman R, Pérez-Ruiz M (2021) Disease classification in aubergine with local symptomatic region using deep learning models. Biosystems Engineering 209:139–153

    Article  Google Scholar 

  26. Li Q, Cao Z, Zhong J, Li Q (2019) Graph representation learning with encoding edges. Neurocomputing 361:29–39

    Article  Google Scholar 

  27. Li Y, Liu Y, Guo Y-Z, Liao X-F, Hu B, Yu T (2021) Spatio-temporal-spectral hierarchical graph convolutional network with Semisupervised active learning for patient-specific seizure prediction. IEEE transactions on cybernetics. https://doi.org/10.1109/TCYB.2021.3071860

  28. Li Y, Liu J, Jiang Y, Liu Y, Lei B (2021) Virtual adversarial training based deep feature aggregation network from dynamic effective connectivity for MCI identification. IEEE transactions on medical imaging. https://doi.org/10.1109/TMI.2021.3110829

  29. Li Y, Liu J, Tang Z, Lei B (2020) Deep spatial-temporal feature fusion from adaptive dynamic functional connectivity for MCI identification. IEEE Trans Med Imaging 39:2818–2830

    Article  Google Scholar 

  30. Li Y, Nie J, Chao X (2020) Do we really need deep CNN for plant diseases identification? Computers and Electronics in Agriculture 178:105803

    Article  Google Scholar 

  31. Liu Y, Gao X (2021) The improved SqueezeNet model was used to identify several kinds of leaf diseases. Transactions of the Chinese Society of Agricultural Engineering 37:187–195

    Google Scholar 

  32. Liu Y, Quan F (2019) Plant disease recognition method and mobile application based on lightweight CNN. Transactions of the Chinese Society of Agricultural Engineering 35:194–204

    Google Scholar 

  33. 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. Computers and Electronics in Agriculture 189:106378

    Article  Google Scholar 

  34. Luo P, Zhang RM, Ren JM, Peng ZL, Li JY (2021) Switchable normalization for learning-to-normalize deep representation. IEEE Trans Pattern Anal Mach Intell 43:712–728

    Article  Google Scholar 

  35. Ma J, Du K, Zheng F, Zhang L, Gong Z, Sun Z (2018) A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network. Computers and Electronics in Agriculture 154:18–24

    Article  Google Scholar 

  36. Mousavi SM, Ghasemi M, Dehghan Manshadi M, Mosavi A (2021) Deep learning for wave energy converter modeling using long short-term memory. Mathematics 9:871

    Article  Google Scholar 

  37. Ngugi LC, Abelwahab M, Abo-Zahhad M (2021) Recent advances in image processing techniques for automated leaf pest and disease recognition – A review. Information Processing in Agriculture 8:27–51

    Article  Google Scholar 

  38. Niu M, Lin Y, Zou Q (2021) sgRNACNN: identifying sgRNA on-target activity in four crops using ensembles of convolutional neural networks. Plant Mol Biol 105:483–495

    Article  Google Scholar 

  39. Nosratabadi S, Ardabili S, Lakner Z, Mako C, Mosavi A (2021) Prediction of food production using machine learning algorithms of multilayer perceptron and ANFIS. Agriculture 11:408

    Article  Google Scholar 

  40. Pei H, Yang B, Liu J, Chang K (2020) Active surveillance via group sparse Bayesian learning. IEEE transactions on pattern analysis and machine intelligence. https://doi.org/10.1109/TPAMI.2020.3023092

  41. Qiu S, Hao Z, Wang Z, Liu L, Liu J, Zhao H et al (2021) Sensor Combination Selection Strategy for Kayak Cycle Phase Segmentation Based on Body Sensor Networks. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2021.3102856

  42. Qiu S, Zhao H, Jiang N, Wu D, Song G, Zhao H et al (2021) Sensor network oriented human motion capture via wearable intelligent system. International Journal of Intelligent Systems. https://doi.org/10.1002/int.22689

  43. Saber A, Sakr M, Abo-Seida OM, Keshk A, Chen H (2021) A novel deep-learning model for automatic detection and classification of breast Cancer using the transfer-learning technique. IEEE Access 9:71194–71209

    Article  Google Scholar 

  44. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) Inverted residuals and linear bottlenecks: Mobile networks for classification, Detection and Segmentation

  45. Sethy PK, Barpanda NK, Rath AK, Behera SK (2020) Deep feature based rice leaf disease identification using support vector machine. Computers and Electronics in Agriculture 175:105527

    Article  Google Scholar 

  46. Sheng B, Xiang D, Ye P (2015) Convergence rate of semi-supervised gradient learning algorithms. International Journal Of Wavelets, Multiresolution and Information Processing 13:1550021

    Article  MathSciNet  MATH  Google Scholar 

  47. Suganya Devi K, Srinivasan P, Bandhopadhyay S (2020) H2K – A robust and optimum approach for detection and classification of groundnut leaf diseases. Computers and Electronics in Agriculture 178:105749

    Article  Google Scholar 

  48. Sun H, Xu H, Liu B, He D, He J, Zhang H et al (2021) MEAN-SSD: A novel real-time detector for apple leaf diseases using improved light-weight convolutional neural networks. Computers and Electronics in Agriculture 189:106379

    Article  Google Scholar 

  49. Tan M, Le Q (2019) Efficientnet: rethinking model scaling for convolutional neural networks. International Conference on Machine Learning

  50. Tavoosi J, Zhang C, Mohammadzadeh A, Mobayen S, Mosavi AH (2021) Medical Image Interpolation Using Recurrent Type-2 Fuzzy Neural Network. Frontiers in Neuroinformatics 15:667375

    Article  Google Scholar 

  51. Tian K, Li JH, Zeng JF, Evans A, Zhang LN (2019) Segmentation of tomato leaf images based on adaptive clustering number of K-means algorithm. Computers and Electronics in Agriculture 165:104962

    Article  Google Scholar 

  52. Wang B, Ma F, Ge L, Ma H, Wang H, Mohamed MA (2020) Icing-EdgeNet: a pruning lightweight edge intelligent method of discriminative driving channel for ice thickness of transmission lines. IEEE Trans Instrum Meas 70:1–12

    Article  Google Scholar 

  53. Wang R, Wu Z, Lou J (2021) Attention-based dynamic user modeling and deep collaborative filtering recommendation. Expert Syst Appl 188:116036

    Article  Google Scholar 

  54. Wang T, Zhao L, Huang P, Zhang X, Xu J (2021) Haze concentration adaptive network for image dehazing. Neurocomputing 439:75–85

    Article  Google Scholar 

  55. Wang C, Zhou J, Wu H (2020) Improved multi-scale ResNet identification of vegetable leaf diseases. Transactions of the Chinese Society of Agricultural Engineering 36:209–217

    Google Scholar 

  56. Wójtowicz A, Piekarczyk J, Czernecki B, Ratajkiewicz H (2021) A random forest model for the classification of wheat and rye leaf rust symptoms based on pure spectra at leaf scale. Journal of Photochemistry and Photobiology B: Biology 223:112278

    Article  Google Scholar 

  57. Wu Z, Li G, Shen S, Cui Z, Lian X, Xu G (2021) Constructing dummy query sequences to protect location privacy and query privacy in location-based services. World Wide Web 24:25–49

    Article  Google Scholar 

  58. Wu Z, Li R, Xie J, Zhou Z, Guo J, Xu X (2020) A user sensitive subject protection approach for book search service. J Assoc Inf Sci Technol 71:183–195

    Article  Google Scholar 

  59. Wu Z, Li G, Zhu H, Cui Z, Huang H (2017) An efficient Wikipedia semantic matching approach to text document classification. Inf Sci 393:15–28

    Article  MathSciNet  Google Scholar 

  60. Wu Z, Shen S, Lian X, Su X, Chen E (2020) A dummy-based user privacy protection approach for text information retrieval. Knowl-Based Syst 195:105679

    Article  Google Scholar 

  61. Wu Z, Shen S, Zhou H, Li H, Lu C, Zou D (2021) An effective approach for the protection of user commodity viewing privacy in e-commerce website. Knowl-Based Syst 220:106952

    Article  Google Scholar 

  62. Wu Z, Wang R, Li Q, Lian X, Xu G (2020) A location privacy-preserving system based on query range cover-up for location-based services. IEEE Transactions on Vehicular Technology 69:5

    Google Scholar 

  63. Xu Y, He R, Zhai Y Eds (2021) Weed identification method based on lightweight convolutional network in natural field environment (Journal of Jilin University(Engineering and Technology Edition)

  64. Yadav S, Sengar N, Singh A, Singh A, Dutta MK (2021) Identification of disease using deep learning and evaluation of bacteriosis in peach leaf. Ecological Informatics 61:101247

    Article  Google Scholar 

  65. Yang G, Bao Y, Liu Z (2017) Location and recognition of tea garden pests based on image significance analysis and convolutional neural network. Transactions of the Chinese Society of Agricultural Engineering 33:156–162

    Google Scholar 

  66. Yang F, Moayedi H, Mosavi A (2021) Predicting the degree of dissolved oxygen using three types of multi-layer perceptron-based artificial neural networks. Sustainability 13:9898

    Article  Google Scholar 

  67. Yang C, Zhao H, Bruzzone L, Benediktsson JA, Liang Y, Liu B et al (2020) Lunar impact crater identification and age estimation with Chang’E data by deep and transfer learning. Nature Communications 11:6358

    Article  Google Scholar 

  68. Ying C, Huang Z, Ying C (2018) Accelerating the image processing by the optimization strategy for deep learning algorithm DBN. EURASIP J Wirel Commun Netw 2018:1–8

    Article  Google Scholar 

  69. Zhang X, Wang T, Wang J, Tang G, Zhao L (2020) Pyramid channel-based feature attention network for image dehazing. Comput Vis Image Underst 197:103003

    Article  Google Scholar 

  70. Zhang L, Zhang Z, Wang W, Jin Z, Su Y, Chen H (2021) Research on a covert communication model realized by using smart contracts in Blockchain environment. IEEE Systems Journal. https://doi.org/10.1109/JSYST.2021.3057333

  71. Zhang L, Zhang Z, Wang W, Waqas R, Zhao C, Kim S et al (2020) A Covert Communication Method Using Special Bitcoin Addresses Generated by Vanitygen. Computers, Materials & Continua 65:597–616

    Article  Google Scholar 

  72. Zhang L, Zou Y, Wang W, Jin Z, Su Y, Chen H (2021) Resource Allocation and Trust Computing for Blockchain-Enabled Edge Computing System. Computers & Security 105:102249

    Article  Google Scholar 

  73. Zhong Y, Zhao M (2020) Research on deep learning in apple leaf disease recognition. Computers and Electronics in Agriculture 168:105146

    Article  Google Scholar 

  74. Zhou J, Li J, Wang C, Wu H, Zhao C, Wang Q (2021) A vegetable disease recognition model for complex background based on region proposal and progressive learning. Computers and Electronics in Agriculture 184:106101

    Article  Google Scholar 

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Funding

This research was funded by the National Social Science Foundation of China (No. U19A2061), the Science and Technology Development Program of Jilin Province (20190301024NY), and the Science and Technology Development Program of Jilin Province (No. 20200301047RQ).

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Authors and Affiliations

  1. College of Information Technology, Jilin Agricultural University, Changchun, 130118, China

    Helong Yu, Xianhe Cheng & Jiawen Liu

  2. College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Chengcheng Chen

  3. Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China

    Ali Asghar Heidari, Zhennao Cai & Huiling Chen

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  1. Helong Yu
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Yu, H., Cheng, X., Chen, C. et al. Apple leaf disease recognition method with improved residual network. Multimed Tools Appl 81, 7759–7782 (2022). https://doi.org/10.1007/s11042-022-11915-2

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