Abstract
With the COVID-19 pandemic outbreak, most countries have limited their grain exports, which has resulted in acute food shortages and price escalation in many countries. An increase in agriculture production is important to control price escalation and reduce the number of people suffering from acute hunger. But crop loss due to pests and plant diseases has also been rising worldwide, inspite of various smart agriculture solutions to control the damage. Out of several approaches, computer vision-based food security systems have shown promising performance, and some pilot projects have also been successfully implemented to issue advisories to farmers based on image-based farm condition monitoring. Several image processing, machine learning, and deep learning techniques have been proposed by researchers for automatic disease detection and identification. Although recent deep learning solutions are quite promising, most of them are either inspired by ILSVRC architectures with high memory and computational requirements, or light convolutional neural network (CNN) based models that have a limited degree of generalization. Thus, building a lightweight and compact CNN based model is a challenging task. In this paper, a transformer-based automatic disease detection model “PlantViT" has been proposed, which is a hybrid model of a CNN and a Vision Transformer. The aim is to identify plant diseases from images of leaves by developing a Vision Transformer-based deep learning technique. The model takes the capabilities of CNNs and the Vision Transformer. The Vision Transformer is based on a multi-head attention module. The experiment has been evaluated on two large-scale open-source plant disease detection datasets: PlantVillage and Embrapa. Experimental results show that the proposed model can achieve 98.61% and 87.87% accuracy on the PlantVillage and Embrapa datasets, respectively. The PlantViT can obtain significant improvement over the current state-of-the-art methods in plant disease detection.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Department of Economic and United Nation Social Affairs Population. World population prospects 2019. https://www.un.org/development/desa/publications/world-population-prospects-2019-highlights.html (2019). Accessed 30 May 2020
Food and Agriculture Organization of the United Nation. Mitigating impacts of covid-19 on food trade and markets (2019). http://www.fao.org/news/story/en/item/1268719/icode/. Accessed 30 Aug 2020
Savary, S., Willocquet, L., Pethybridge, S.J., Esker, P., McRoberts, N., Nelson, A.: The global burden of pathogens and pests on major food crops. Nat. Ecol. Evol. 3(3), 430–439 (2019)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25, 1097–1105 (2012)
Mohanty, S.P., Hughes, D.P., Salathé, M.: Using deep learning for image-based plant disease detection. Front. Plant Sci. 7, 1419 (2016)
Hughes, D., Salathé, M., et al.: An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv preprint arXiv:1511.08060 (2015)
Ferentinos, K.P.: Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agric. 145, 311–318 (2018)
Kamal, K.C., Yin, Z., Wu, M., Wu, Z.: Depthwise separable convolution architectures for plant disease classification. Comput. Electron. Agric. 165, 104948 (2019)
Too, E.C., Yujian, L., Njuki, S., Yingchun, L.: A comparative study of fine-tuning deep learning models for plant disease identification. Comput. Electron. Agric. 161, 272–279 (2019)
Kumar, S., Sharma, B., Sharma, V.K., Sharma, H., Bansal, J.C.: Plant leaf disease identification using exponential spider monkey optimization. Sustain. Comput. Inform. Syst. 28, 100283 (2020)
Lee, S.H., Goëau, H., Bonnet, P., Joly, A.: New perspectives on plant disease characterization based on deep learning. Comput. Electron. Agric. 170, 105220 (2020)
Argüeso, D., et al.: Few-shot learning approach for plant disease classification using images taken in the field. Comput. Electron. Agric. 175, 105542 (2020)
Jeevan, P., Sethi, A.: Vision Xformers: efficient attention for image classification. arXiv preprint arXiv:2107.02239 (2021)
Barbedo, J.G.A., Koenigkan, L.V., Santos, T.T.: Identifying multiple plant diseases using digital image processing. Biosyst. Eng. 147, 104–116 (2016)
Johannes, A., et al.: Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case. Comput. Electron. Agric. 138, 200–209 (2017)
Sharif, M., Khan, M.A., Iqbal, Z., Azam, M.F., Lali, M.I.U., Javed, M.Y.: Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection. Comput. Electron. Agric. 150, 220–234 (2018)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Liang, Q., Xiang, S., Yucheng, H., Coppola, G., Zhang, D., Sun, W.: PD2SE-Net: computer-assisted plant disease diagnosis and severity estimation network. Comput. Electron. Agric. 157, 518–529 (2019)
Karthik, R., Hariharan, M., Anand, S., Mathikshara, P., Johnson, A., Menaka, R.: Attention embedded residual CNN for disease detection in tomato leaves. Appl. Soft Comput. 86, 105933 (2020)
Sambasivam, G., Opiyo, G.D.: A predictive machine learning application in agriculture: cassava disease detection and classification with imbalanced dataset using convolutional neural networks. Egyptian Inform. J. 22(1), 27–34 (2021)
Chen, J., Wang, W., Zhang, D., Zeb, A., Nanehkaran, Y.A.: Attention embedded lightweight network for maize disease recognition. Plant Pathol. 70(3), 630–642 (2021)
Chen, J., Zhang, D., Zeb, A., Nanehkaran, Y.A.: Identification of rice plant diseases using lightweight attention networks. Expert Syst. Appl. 169, 114514 (2021)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)
Su, J., Lu, Y., Pan, S., Wen, B., Liu, Y.: RoFormer: enhanced transformer with rotary position embedding. arXiv preprint arXiv:2104.09864 (2021)
Barbedo, J.G.A., et al.: Annotated plant pathology databases for image-based detection and recognition of diseases. IEEE Latin Am. Trans. 16(6), 1749–1757 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Thakur, P.S., Khanna, P., Sheorey, T., Ojha, A. (2022). Vision Transformer for Plant Disease Detection: PlantViT. In: Raman, B., Murala, S., Chowdhury, A., Dhall, A., Goyal, P. (eds) Computer Vision and Image Processing. CVIP 2021. Communications in Computer and Information Science, vol 1567. Springer, Cham. https://doi.org/10.1007/978-3-031-11346-8_43
Download citation
DOI: https://doi.org/10.1007/978-3-031-11346-8_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-11345-1
Online ISBN: 978-3-031-11346-8
eBook Packages: Computer ScienceComputer Science (R0)