Abstract
Plants are the main components of our ecosystem. They play a vital role not only as a primary food resource but also as a fundamental element of our existence. Therefore, pathogens and diseases in plants are a serious threat, while the most prevalent treatment of visual symptoms is mainly done by inspecting the plant leaves. As an alternative to the process which traditionally takes time and lacks accuracy, various research projects seek to find feasible approaches for plant protection. Early recognition of plant diseases is a salient aspect of agriculture. In this paper, a deep learning-based system collaborating with IoT architecture and an android application has been proposed for recognizing 38 categories that contain healthy and disease-affected images of 14 species which include apple, corn, potato, bell pepper, tomato leaves, etc. from PlantVillage dataset. We have implemented Inception-v3, Inception-ResNet-v2, and fast.ai library’s ResNet34 Convolution Neural Networks along with the concept of learning vector quantization on the dataset. Our proposed system achieved the highest accuracy of 97.03% with ResNet34 architecture.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Singh, V., Misra, A.K.: Detection of plant leaf diseases using image segmentation and soft computing techniques. Inf. Proces. Agric. 4(1), 41–49 (2017)
Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., Stefanovic, D.: Deep neural networks based recognition of plant diseases by leaf image classification. Comput. Intell. Neurosci. 1–11 (2016)
Sabrol, H., Satish, K.: Tomato plant disease classification in digital images using classification tree. In: 2016 International Conference on Communication and Signal Processing (ICCSP), pp. 1242–1246. IEEE (2016)
Naik, M.R., Sivappagari, C.M.R.: Plant leaf and disease detection by using HSV features and SVM classifier. Int. J. Eng. Sci. Comput. 6, 3794–3797 (2016)
Mohanty, S.P., Hughes, D.P., Salathé, M.: Using deep learning for image-based plant disease detection. Front. Plant Sci. 7, 1419 (2016)
PlantVillage: Retrieved 17 Oct 2019, from https://plantvillage.psu.edu/
Francis, M., Deisy, C.: Disease detection and classification in agricultural plants using convolutional neural networks – a visual understanding. In: 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN), pp. 1063–1068. IEEE (2019)
Brahimi, M., Arsenovic, M., Laraba, S., Sladojevic, S., Boukhalfa, K., Moussaoui, A.: Deep learning for plant diseases: detection and saliency map visualisation. In: Human and Machine Learning, pp. 93–117. Springer, Cham (2018)
Fuentes, A., Yoon, S., Kim, S., Dong, P.: A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors. 17(9), 2022 (2017)
Zhang, L., Jia, J., Gui, G., Hao, X., Gao, W., Wang, M.: Deep learning based improved classification system for designing tomato harvesting robot. IEEE Access. 6, 67940–67950 (2018)
Ferentinos, K.P.: Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agric. 145, 311–318 (2018)
Zhang, S., Huang, W., Zhang, C.: Three-channel convolutional neural networks for vegetable leaf disease recognition. Cogn. Syst. Res. 53, 31–41 (2019)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Canziani, A., Paszke, A., Culurciello, E.: An analysis of deep neural network models for practical applications. arXiv preprint arXiv:1605.07678 (2016)
Venkatesh, G., Nurvitadhi, E., Marr, D.: Accelerating deep convolutional networks using low-precision and sparsity. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2861–2865. IEEE (2017)
Kohonen, T.: Self-Organization and Associative Memory, vol. 8. Springer Science & Business Media, Springer, Berlin, Heidelberg (2012)
Sardogan, M., Tuncer, A., Ozen, Y.: Plant leaf disease detection and classification based on CNN with LVQ algorithm. In: 2018 3rd International Conference on Computer Science and Engineering (UBMK), pp. 382–385. IEEE (2018)
Sánchez, J.S., Marqués, A.I.: An LVQ-based adaptive algorithm for learning from very small codebooks. Neurocomputing. 69(7–9), 922–927 (2006)
Upload an Object Using the AWS SDK for Java. Retrieved 2 Oct 2019, from https://docs.aws.amazon.com/AmazonS3/latest/dev/UploadObjSingleOpJava.html
Store and Retrieve Data with DynamoDB. Retrieved 2 Oct 2019, from https://docs.aws.amazon.com/mobile/sdkforxamarin/developerguide/getting-started-store-retrieve-data.html
Baldassarre, F., Morín, D. G., & Rodés-Guirao, L.: Deep koalarization: image colorization using cnns and inception-resnet-v2. arXiv preprint arXiv:1712.03400 (2017)
Xia, X., Xu, C., Nan, B.: Inception-v3 for flower classification. In: 2017 2nd International Conference on Image, Vision and Computing (ICIVC), pp. 783–787. IEEE (2017)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Pavel, M.I., Rumi, R.I., Fairooz, F., Jahan, S., Hossain, M.A. (2021). Deep Residual Learning Approach forPlant Disease Recognition. In: Raj, J.S. (eds) International Conference on Mobile Computing and Sustainable Informatics . ICMCSI 2020. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-49795-8_49
Download citation
DOI: https://doi.org/10.1007/978-3-030-49795-8_49
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-49794-1
Online ISBN: 978-3-030-49795-8
eBook Packages: EngineeringEngineering (R0)