Resnet-Based Approach For Detection And Classification Of Plant Leaf Diseases
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Abstract
Plant diseases may cause large yield losses, endangering both the stability of the economy and the supply of food. Convolutional Neural Networks (CNNs), in particular, are deep neural networks that have shown remarkable effectiveness in completing image categorization tasks, often outperforming human ability. It has numerous applications in voice processing, picture and video processing, and natural language processing (NLP). It has also grown into a centre for research on plant protection in agriculture, including the assessment of pest ranges and the diagnosis of plant diseases. In two plant phenotyping tasks, the function of a CNN (Convolutional Neural Networks) structure based on Residual Networks (ResNet) is investigated in this study. The majority of current studies on Species Recognition (SR) and plant infection detection have used balanced datasets for accuracy and experimentation as the evaluation criteria. This study, however, made use of an unbalanced dataset with an uneven number of pictures, organised the data into several test cases and classes, conducted data augmentation to improve accuracy, and—most importantly—used multiclass classifier assessment settings that were helpful for an asymmetric class distribution. Furthermore with all these frequent issues, the paper addresses selecting the size of the data collection, classifier depth, necessary training time, and assessing the efficacy of the classifier when using various test scenarios. The Species Recognising (SR) and Identifying of Health and Infection Leaves (IHIL) tasks in this study have shown substantial improvement in performance for the ResNet 20 (V2) architecture, with Precision of 91.84% & 84.00%, Recall of 91.67% and 83.14%, and F1 scores of 91.49% & 83.19%, respectively.
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References
Q.-F. Wu, Y.-P. Chen, and J. Meng, “DCGAN-based data augmentation for tomato leaf disease identification,” IEEE Access, vol. 8, pp. 98716–98728, 2020.
B. Liu, C. Tan, S.-Q. Li, J.-R. He, and H.-Y. Wang, “A data augmentation method based on generative adversarial networks for grape leaf disease identification,” IEEE Access, vol. 8, pp. 102188– 102198, 2020.
M. Brahimi, K. Boukhalfa, and A. Moussaoui, “Deep learning for tomato diseases: Classification and symptoms visualization,” Appl. Artif. Intell., vol. 31, no. 4, pp. 299–315, May 2017.
M. Brahimi, M. Arsenovic, S. Laraba, S. Sladojevic, K. Boukhalfa, and A. Moussaoui, “Deep learning for plant diseases: Detection and saliency map visualization,” in Human and machine learning, Boston, BSN, USA: Springer, Jun. 2018, pp. 93-117.
A. C. Cruz, A. Luvisi, L. De Bellis, and Y. Ampatzidis, “Visionbased plant disease detection system using transfer and deep learning,” in 2017 Asabe Annu. Int. Meet., GEG, USA, Jul. 2017, pp. 16–19.
M. Brahimi, S. Mahmoudi, K. Boukhalfa, and A. Moussaoui, “Deep interpretable architecture for plant diseases classification,” in Signal Process.: Algorithms, Architectures, Arrangements, and Applications (SPA), POZ, POL, Jul. 2019, pp. 111–116.
C. DeChant, T. Wiesner-Hanks, S. Chen, E. L. Stewart, J. Yosinski, M. A. Gore, R. J. Nelson, and H. Lipson, “Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning,” Phytopathology, vol. 107, no. 11, pp. 1426– 1432, Aug. 2017.
Shankar, K.; Zhang, Y.; Liu, Y.; Wu, L.; Chen, C.H. Hyperparameter tuning deep learning for diabetic retinopathy fundus image classification. IEEE Access 2020, 8, 118164–118173.
Gulshan, V.; Peng, L.; Coram, M.; Stumpe, M.C.; Wu, D.; Narayanaswamy, A.; Venugopalan, S.; Widner, K.; Madams, T.; Cuadros, J.; et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016, 316, 2402–2410.
Qummar, S.; Khan, F.G.; Shah, S.; Khan, A.; Shamshirband, S.; Rehman, Z.U.; Khan, I.A.; Jadoon, W. A deep learning ensemble approach for diabetic retinopathy detection. IEEE Access 2019, 7, 150530–150539.
Narsaiah, C.; Manoj, P.; Raju, A.G. Study on Awareness and Assessment of Diabetic Retinopathy in Diabetic Patients Attending Ophthalmology Clinic at a Tertiary Care Hospital, Telangana State. J. Contemp. Med. Res. 2019, 6, K9–K13.
Bansal, P.; Gupta, R.P.; Kotecha, M. Frequency of diabetic retinopathy in patients with diabetes mellitus and its correlation with duration of diabetes mellitus. Med. J. Dr. Patil Univ. 2013, 6, 366.
Fong, D.S.; Aiello, L.; Gardner, T.W.; King, G.L.; Blankenship, G.; Cavallerano, J.D.; Ferris, F.L.; Klein, R. Retinopathy in diabetes. Diabetes Care 2004, 27, s84–s87.
Fiorani, F.; Schurr, U. Future Scenarios for Plant Phenotyping. Annu. Rev. Plant Biol. 2013, 64, 267–291.
Kaur, S.; Pandey, S.; Goel, S. Plants Disease Identification and Classification through Leaf Images: A Survey. Arch. Comput. Methods Eng. 2018, 26, 507–530.
Barré, P.; Stöver, B.C.; Müller, K.F.; Steinhage, V. LeafNet: A computer vision system for automatic plant species identification. Ecol. Inform. 2017, 40, 50–56.
Loganathan, P., & Karthikeyan, R. (2021). Residual Neural Network (ResNet) Based Plant Leaf Disease Detection and Classification. Turkish Online Journal of Qualitative Inquiry, 12(6).
Ding, R., Qiao, Y., Yang, X., Jiang, H., Zhang, Y., Huang, Z. ... & Liu, H. (2022). Improved ResNet Based Apple Leaf Diseases Identification. IFAC-PapersOnLine, 55(32), 78-82.
Sri, V. S., Sailu, V. H., Pradeepthi, U., Sai, P. M., & Kavitha, M. (2023). Disease Detection using Region-Based Convolutional Neural Network and ResNet. Data and Metadata, 2, 135-135.
Luo, Y., Sun, J., Shen, J., Wu, X., Wang, L., & Zhu, W. (2021). Apple leaf disease recognition and sub-class categorization based on improved multi-scale feature fusion network. IEEE Access, 9, 95517-95527.
Liang, S., & Deng, X. (2022, October). Research on rice leaf disease identification based on ResNet. In 2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) (pp. 1409-1413). IEEE.
Patidar, S., Pandey, A., Shirish, B. A., & Sriram, A. (2020). Rice plant disease detection and classification using deep residual learning. In Machine Learning, Image Processing, Network Security and Data Sciences: Second International Conference, MIND 2020, Silchar, India, July 30-31, 2020, Proceedings, Part I 2 (pp. 278-293). Springer Singapore.
Owomugisha, G.; Mwebaze, E. Machine Learning for Plant Disease Incidence and Severity Measurements from Leaf Images. In Proceedings of the 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), Anaheim, CA, USA, 18 December 2016; pp. 158–163.
Pawara, P.; Okafor, E.; Surinta, O.; Schomaker, L.; Wiering, M. Comparing Local Descriptors and Bags of Visual Words to Deep Convolutional Neural Networks for Plant Recognition. In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods, Porto, Portugal, 24 February 2017; SCITEPRESS: Setúbal, Portugal, 2017; Volume 24, pp. 479–486.
Xiong, J.; Yu, D.; Liu, S.; Shu, L.; Wang, X.; Liu, Z. A Review of Plant Phenotypic Image Recognition Technology Based on Deep Learning. Electronics 2021, 10, 81.
Lee, S.H.; Chan, C.S.; Mayo, S.J.; Remagnino, P. How deep learning extracts and learns leaf features for plant classification. Pattern Recognit. 2017, 71, 1–13.
Zhang, S.; Zhang, C. Plant Species Recognition Based on Deep Convolutional Neural Networks. In Proceedings of the International Conference on Intelligent Computing, Liverpool, UK, 7 August 2017; Springer: Cham, Switzerland, 2017; pp. 282–289.