SE-CapsNet: Automated evaluation of plant disease severity based on feature extraction through Squeeze and Excitation (SE) networks and Capsule networks
Keywords:Capsule Networks, Convolutional Neural Networks, Deep Learning, Plant Disease Severity, Squeeze and Excitation Networks
AbstractDiseases in plants have an adverse effect on the quantity of the overall food production as well as the quality of the yield. Early detection, diagnosis and treatment can greatly reduce losses, both economic and ecological, i.e. reduction in the use of agrochemicals due to timely detection of the disease, would help in mitigating the environmental impact. In this paper, the authors have proposed an improved feature computation approach based on Squeeze and Excitation Networks, before processing by the original Capsule Networks (CapsNet) for classification. Two SE networks, one based on AlexNet and another on ResNet have been combined with Capsule Networks, for estimating the disease severity in plants. Leaf images for the devastating Late Blight disease occurring in the Tomato crop have been utilized from the PlantVillage dataset. The images, divided into four severity stages i.e. healthy, early, middle and end, are downscaled, enhanced and given as input to the SE networks. The feature maps generated from the two networks are separately given as input to the Capsule Network for classification and their performances are compared with the original CapsNet, on two image sizes 32X32 and 64X64. SE-Alex-CapsNet achieves the highest accuracy of 92.76% and SE-Res-CapsNet achieves the highest accuracy of 94.4% with 64X64 image size, as compared to CapsNet that results in 85.53% accuracy. On the basis of the performances, the proposed techniques can be exploited for disease severity assessment in other crops as well and can be extended to other areas of applications such as plant species classification, weed identification etc.
Al-Hmouz, R. (2020). Deep learning autoencoder approach: Automatic recognition of artistic Arabic calligraphy types. Kuwait Journal of Science, 47(3).
Barbedo, J. G. A. (2019). Plant disease identification from individual lesions and spots using deep learning. Biosystems Engineering, 180, 96-107.
Camargo, A., & Smith, J. S. (2009). An image-processing based algorithm to automatically identify plant disease visual symptoms. Biosystems engineering, 102(1), 9-21.
Camargo, A., & Smith, J. S. (2009). Image pattern classification for the identification of disease causing agents in plants. Computers and Electronics in Agriculture, 66(2), 121-125.
Campbell, C. L., & Neher, D. A. (1994). Estimating disease severity and incidence. In Epidemiology and management of root diseases (pp. 117-147). Springer, Berlin, Heidelberg.
Chen, J., Chen, J., Zhang, D., Sun, Y., & Nanehkaran, Y. A. (2020). Using deep transfer learning for image-based plant disease identification. Computers and Electronics in Agriculture, 173, 105393.
Dong, M., Mu, S., Su, T., & Sun, W. (2019, August). Image Recognition of Peanut Leaf Diseases Based on Capsule Networks. In International CCF Conference on Artificial Intelligence (pp. 43-52). Springer, Singapore.
Duong, L. T., Nguyen, P. T., Di Sipio, C., & Di Ruscio, D. (2020). Automated fruit recognition using EfficientNet and MixNet. Computers and Electronics in Agriculture, 171, 105326.
Dyrmann, M., Karstoft, H., & Midtiby, H. S. (2016). Plant species classification using deep convolutional neural network. Biosystems Engineering, 151, 72-80.
Espejo-Garcia, B., Mylonas, N., Athanasakos, L., Fountas, S., & Vasilakoglou, I. (2020). Towards weeds identification assistance through transfer learning. Computers and Electronics in Agriculture, 171, 105306.
Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311-318.
GITHUB. https://github.com/XifengGuo/ CapsNet-Keras. Last Accessed: July 2020.
Hang, J., Zhang, D., Chen, P., Zhang, J., & Wang, B. (2019). Classification of Plant Leaf Diseases Based on Improved Convolutional Neural Network. Sensors, 19(19), 4161.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
Heckmann, D., Schlüter, U., & Weber, A. P. (2017). Machine learning techniques for predicting crop photosynthetic capacity from leaf reflectance spectra. Molecular plant, 10(6), 878-890.
Hu, J., Shen, L., & Sun, G. (2018). Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7132-7141).
Hughes, D., & Salathé, M. (2015). An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv preprint arXiv:1511.08060.
Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and electronics in agriculture, 147, 70-90.
Karlekar, A., & Seal, A. (2020). SoyNet: Soybean leaf diseases classification. Computers and Electronics in Agriculture, 172, 105342.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).
Li, Y. et al. (2019). The recognition of rice images by UAV based on capsule network. Cluster Computing, 22(4), 9515-9524.
Liang, Q., Xiang, S., Hu, Y., Coppola, G., Zhang, D., & Sun, W. (2019). PD2SE-Net: Computer-assisted plant disease diagnosis and severity estimation network. Computers and electronics in agriculture, 157, 518-529.
Liu, B., Zhang, Y., He, D., & Li, Y. (2018). Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry, 10(1), 11.
Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using deep learning for image-based plant disease detection, Frontiers in plant science, vol. 7, p. 1419.
Mukherjee, J., & Mitra, S. K. (2008). Enhancement of color images by scaling the DCT coefficients. IEEE Transactions on Image processing, 17(10), 1783-1794.
Nowicki, M., Kozik, E. U., & Foolad, M. R. (2013). Late Blight of Tomato. In Translational Genomics for Crop Breeding (pp. 241–265), https://doi.org/10.1002/9781118728475.ch13.
Pal, T., Jaiswal, V., & Chauhan, R. S. (2016). DRPPP: A machine learning based tool for prediction of disease resistance proteins in plants. Computers in biology and medicine, 78, 42-48.
Paoletti, M. E., Haut, J. M., Fernandez-
Beltran, R., Plaza, J., Plaza, A., Li, J., & Pla, F. (2018). Capsule networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 57(4), 2145-2160.
Prince, G., Clarkson, J. P., & Rajpoot, N.
M. (2015). Automatic detection of diseased tomato plants using thermal and stereo visible light images. PloS one, 10(4), e0123262.
Rumpf, T., Mahlein, A. K., Steiner, U., Oerke, E. C., Dehne, H. W., & Plümer, L. (2010). Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Computers and electronics in agriculture, 74(1), 91-99.
Sabour, S., Frosst, N., & Hinton, G. E. (2017). Dynamic routing between capsules. In Advances in neural information processing systems (pp. 3856-3866).
Sabrol, H., & Kumar, S. (2016). Intensity based feature extraction for tomato plant disease recognition by classification using decision tree. International Journal of Computer Science and Information Security, 14(9), 622.
Singh, V., & Misra, A. K. (2017). Detection of plant leaf diseases using image segmentation and soft computing techniques. Information processing in Agriculture, 4(1), 41-49.
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, 2016.
Too, E. C., Yujian, L., Njuki, S., & Yingchun, L. (2019). A comparative study of fine-tuning deep learning models for plant disease identification. Computers and Electronics in Agriculture, 161, 272-279.
Verma, S., Chug, A., & Singh, A. P. (2020). Exploring capsule networks for disease classification in plants. Journal of Statistics and Management Systems, 23(2), 307-315.
Wang, G., Sun, Y., & Wang, J. (2017). Automatic image-based plant disease severity estimation using deep learning. Computational intelligence and neuroscience, 2017.
Zeng, W., & Li, M. (2020). Crop leaf disease recognition based on Self-Attention convolutional neural network. Computers and Electronics in Agriculture, 172, 105341.
Zhang, X., Qiao, Y., Meng, F., Fan, C., & Zhang, M. (2018). Identification of maize leaf diseases using improved deep convolutional neural networks. IEEE Access, 6, 30370-30377.