Abstract
Deep learning is emerging as an automatic and accurate model for image classification. Plant diseases are significant threats to food security. Rapid and accurate identification of plant pathology is difficult due to the lack of infrastructure and techniques. The recent advancements of deep learning in computer vision have paved a new horizon for plant pathology diagnosis. Early detection of plant pathology is a demanding task today. This paper proposes a deep convolutional neural network model for the accurate and rapid identification of plant disease. The deep convolutional neural network is designed based on Hypergraph modeling. The plant village dataset covers 38 different classes of 14 other plants. Experimental results show that the proposed model provides the maximum accuracy of 99.7%. Precision, recall, and F1 scores are computed to validate the model. Micro precision and Micro recall analysis are performed to validate the model at the micro-level. Furthermore, it is proved that the proposed model outperforms all the state-of-the-art deep learning models for plant disease detection based on images.
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References
Athanikar G, Badar P (2016) Potato leaf diseases detection and classification system. Int J Comput Sci Mob Comput 5(2):76–88
Deepa S, Umarani R (2017) Steganalysis on images using SVM with selected hybrid features of Gini index feature selection algorithm. Int J Adv Res Comput Sci 8:5
Dyrmann M, Karstoft H, Midtiby HS (2016) Plant species classification using a deep convolutional neural network. Biosyst Eng 151:72–80
Ehler LE (2006) Integrated pest management (IPM): definition, historical development and implementation, and the other IPM. Pest Manag Sci 62(9):787–789
Faithpraise F, Birch P, Young R, Obu J, Faithpraise B, Chatwin C (2013) Automatic plant pest detection and recognition using k-means clustering algorithm and correspondence filters. Int J Adv Biotechnol Res 4(2):189–199
Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311–318
Guettari N, Capelle-Laizé AS, Carré P (2016) Blind image steganalysis based on evidential k-nearest neighbors. IEEE, pp 2742–2746
Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907
Kodovsky J, Fridrich J, Holub V (2011) Ensemble classifiers for steganalysis of digital media. IEEE Trans Inf Forensics Secur 7(2):432–444
Mohanty SP, Hughes DP, Salathé M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:1419
Ramezani M, Ghaemmaghami S (2010) Towards genetic feature selection in image steganalysis. In 2010 7th IEEE Consumer Communications and Networking Conference. IEEE, pp 1-4
Reyes AK, Caicedo JC, Camargo JE (2015) Fine-tuning deep convolutional networks for plant recognition. CLEF (Working Notes) 1391:467–475
Sheikhan M, Pezhmanpour M, Moin MS (2012) Improved contourlet-based steganalysis using binary particle swarm optimization and radial basis neural networks. Neural Comput Appl 21(7):1717–1728
Sa I, Ge Z, Dayoub F, Upcroft B, Perez T, McCool C (2016) Deepfruits: A fruit detection system using deep neural networks. Sensors 16(8):1222
Samanta D, Chaudhury PP, Ghosh A (2012) Scab diseases detection of potato using image processing. Int J Comput Trends Technol 3(1):109–113
Sanchez PA, Swaminathan MS (2005) Cutting world hunger in half. Science 307(5708):357–359
Shi H, Zhang Y, Zhang Z, Ma N, Zhao X, Gao Y, Sun J (2018) Hypergraph-induced convolutional networks for visual classification. IEEE Trans Neural Netw Learn Syst 30(10):2963–2972
Sladojevic S, Arsenovic M, Anderla A, Culibrk D, Stefanovic D (2016) Deep neural networks based recognition of plant diseases by leaf image classification. Computational intelligence and neuroscience, vol. 2016, Hindawi Publications
Strange RN, Scott PR (2005) Plant disease: a threat to global food security. Annu Rev Phytopathol 43:83–116
Tai AP, Martin MV, Heald CL (2014) The threat to future global food security from climate change and ozone air pollution. Nat Clim Chang 4(9):817–821
Too EC, Yujian L, Njuki S, Yingchun L (2019) A comparative study of fine-tuning deep learning models for plant disease identification. Comput Electron Agric 161:272–279
UNEP (2013) Smallholders, food security, and the environment. International Fund for Agricultural Development, Rome
Wang H, Li G, Ma Z, Li X (2012, May) Application of neural networks to image recognition of plant diseases. In 2012 International Conference on Systems and Informatics (ICSAI2012) (pp.2159-2164). IEEE
Zhou D, Huang J, Schölkopf B (2007) Learning with hypergraphs: Clustering, classification, and embedding. In Advances in neural information processing systems, pp 1601-1608
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Sasikaladevi, N. Robust and fast Plant Pathology Prognostics (P3) tool based on deep convolutional neural network. Multimed Tools Appl 81, 7271–7283 (2022). https://doi.org/10.1007/s11042-022-11902-7
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DOI: https://doi.org/10.1007/s11042-022-11902-7