Abstract
In proposed work, a compressed version of UNet has been developed using Differential Evolution for segmenting the diseased regions in leaf images. The compressed model has been evaluated on potato late blight leaf images from PlantVillage dataset. The compressed model needs only 6.8% of space needed by original UNet architecture, and the inference time for disease classification is twice as fast without loss in performance metric of mean Intersection over Union (IoU).
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
References
Anwar, S., Hwang, K., Sung, W.: Structured pruning of deep convolutional neural networks. ACM J. Emerg. Technol. Comput. Syst. (JETC) 13(3), 1–18 (2017)
Beheshti, N., Johnsson, L.: Squeeze u-net: a memory and energy efficient image segmentation network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 364–365 (2020)
Chakraborty, U.K.: Advances in Differential Evolution, vol. 143. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-68830-3
Feoktistov, V.: Differential Evolution. Springer, Heidelberg (2006). https://doi.org/10.1007/978-0-387-36896-2
Ganesh, P., Volle, K., Burks, T., Mehta, S.: Deep orange: mask R-CNN based orange detection and segmentation. IFAC-PapersOnLine 52(30), 70–75 (2019)
Han, S., Pool, J., Tran, J., Dally, W.: Learning both weights and connections for efficient neural network. In: Advances in Neural Information Processing Systems, pp. 1135–1143 (2015)
He, Y., Zhang, X., Sun, J.: Channel pruning for accelerating very deep neural networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1389–1397 (2017)
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)
Islam, M., Dinh, A., Wahid, K., Bhowmik, P.: Detection of potato diseases using image segmentation and multiclass support vector machine. In: 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1–4. IEEE (2017)
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)
Lee, U., Chang, S., Putra, G.A., Kim, H., Kim, D.H.: An automated, high-throughput plant phenotyping system using machine learning-based plant segmentation and image analysis. PLoS ONE 13(4), e0196615 (2018)
Li, H., Kadav, A., Durdanovic, I., Samet, H., Graf, H.P.: Pruning filters for efficient convnets. arXiv preprint arXiv:1608.08710 (2016)
Lin, K., Gong, L., Huang, Y., Liu, C., Pan, J.: Deep learning-based segmentation and quantification of cucumber powdery mildew using convolutional neural network. Front. Plant Sci. 10, 155 (2019)
Liu, Z., Li, J., Shen, Z., Huang, G., Yan, S., Zhang, C.: Learning efficient convolutional networks through network slimming. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2736–2744 (2017)
Ma, J., Du, K., Zheng, F., Zhang, L., Gong, Z., Sun, Z.: A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network. Comput. Electron. Agric. 154, 18–24 (2018)
Mohanty, S.P., Hughes, D.P., Salathé, M.: Using deep learning for image-based plant disease detection. Front. Plant Sci. 7, 1419 (2016)
Ronneberger, Olaf, Fischer, Philipp, Brox, Thomas: U-Net: convolutional networks for biomedical image segmentation. In: Navab, Nassir, Hornegger, Joachim, Wells, William M., Frangi, Alejandro F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Samala, R.K., Chan, H.P., Hadjiiski, L.M., Helvie, M.A., Richter, C., Cha, K.: Evolutionary pruning of transfer learned deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis. Phys. Med. Biol. 63(9), 095005 (2018)
Wang, Z., Li, F., Shi, G., Xie, X., Wang, F.: Network pruning using sparse learning and genetic algorithm. Neurocomputing 404, 247–256 (2020)
Yang, Chuanguang, An, Zhulin, Li, Chao, Diao, Boyu, Xu, Yongjun: Multi-objective pruning for CNNs using genetic algorithm. In: Tetko, Igor V., Kůrková, Věra, Karpov, Pavel, Theis, Fabian (eds.) ICANN 2019. LNCS, vol. 11728, pp. 299–305. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30484-3_25
Zhang, Q., Zhang, M., Chen, T., Sun, Z., Ma, Y., Yu, B.: Recent advances in convolutional neural network acceleration. Neurocomputing 323, 37–51 (2019)
Zhou, J., Fu, X., Zhou, S., Zhou, J., Ye, H., Nguyen, H.T.: Automated segmentation of soybean plants from 3D point cloud using machine learning. Comput. Electron. Agric. 162, 143–153 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
A Appendix I
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Agarwal, M., Gupta, S.K., Biswas, K.K. (2021). Plant Leaf Disease Segmentation Using Compressed UNet Architecture. In: Gupta, M., Ramakrishnan, G. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12705. Springer, Cham. https://doi.org/10.1007/978-3-030-75015-2_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-75015-2_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-75014-5
Online ISBN: 978-3-030-75015-2
eBook Packages: Computer ScienceComputer Science (R0)