Color-Aware Two-Branch DCNN for Efficient Plant Disease Classification
Abstract
Deep convolutional neural networks (DCNNs) have been successfully applied to plant disease detection. Unlike most existing studies, we propose feeding a DCNN CIE Lab instead of RGB color coordinates. We modified an Inception V3 architecture to include one branch specific for achromatic data (L channel) and another branch specific for chromatic data (AB channels). This modification takes advantage of the decoupling of chromatic and achromatic information. Besides, splitting branches reduces the number of trainable parameters and computation load by up to 50% of the original figures using modified layers. We achieved a state-of-the-art classification accuracy of 99.48% on the Plant Village dataset and 76.91% on the Cropped-PlantDoc dataset.
References
Abadi, M., et al. TensorFlow: Large-scale machine learning on heterogeneous systems, 2015. Software available from tensorflow.org.
Amara, J., Bouaziz, B., and Algergawy, A. A deep learning-based approach for banana leaf diseases classification. In Datenbanksysteme f¨ur Business, Technologie und Web – Workshopband (01 2017), pp. 79–88.
Chaudhary, P., Chaudhari, A., and Godara, S. Color transform based approach for disease spot detection on plant leaf. International Journal of Computer Science and Telecommunications 3 (06 2012), 65–70.
Chen, Y., Li, J., Xiao, H., Jin, X., Yan, S., and Feng, J. Dual path networks.
Chollet, F., et al. Keras. https://keras.io, 2015.
Ferentinos, K. P. Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture 145 (2018), 311–318.
Fuentes, A., Yoon, S., Kim, S. C., and Park, D. S. A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors 17, 9 (2017).
Fujita, E., Kawasaki, Y., Uga, H., Kagiwada, S., and Iyatomi, H. Basic investigation on a robust and practical plant diagnostic system. In 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) (2016), pp. 989–992.
Fukushima, K. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics 36, 4 (Apr 1980), 193–202.
G., G., and J., A. P. Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Computers & Electrical Engineering 76 (2019), 323–338.
Gowda, S. N., and Yuan, C. Colornet: Investigating the importance of color spaces for image classification. In Computer Vision – ACCV 2018 (Cham, 2019), C. Jawahar, H. Li, G. Mori, and K. Schindler, Eds., Springer International Publishing, pp. 581–596.
He, K., Zhang, X., Ren, S., and Sun, J. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Los Alamitos, CA, USA, jun 2016), IEEE Computer Society, pp. 770–778.
Huang, G., Liu, Z., and Weinberger, K. Q. Densely connected convolutional networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017), IEEE, pp. 2261–2269.
Hughes, D. P., and Salath’e , M. An open access repository of images on plant health to enable the development of mobile disease diagnostics through machine learning and xrowdsourcing. CoRR abs/1511.08060 (2015).
Ioannou, Y., Robertson, D. P., Cipolla, R., and Criminisi, A. Deep roots: Improving cnn efficiency with hierarchical filter groups. 5977–5986.
Johannes, A., Picon, A., Alvarez-Gila, A., Echazarra, J., Rodriguez-Vaamonde, S., Navajas, A. D., and Ortiz-Barredo, A. Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case. Computers and Electronics in Agriculture 138 (2017), 200–209.
Krizhevsky, A. Learning multiple layers of features from tiny images. Tech. rep., University of Toronto, Toronto, Ontario, 2009.
Krizhevsky, A., Sutskever, I., and Hinton, G. E. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems 25, F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2012, pp. 1097–1105.
Maeda-Gutierrez, V., et al. Comparison of convolutional neural network architectures for classification of tomato plant diseases. Applied Sciences 10, 4 (2020).
Mohanty, S. P., Hughes, D. P., and Salath´e, M. Using deep learning for image-based plant disease detection. Frontiers in Plant Science 7 (2016), 1419.
Ngugi, L. C., Abelwahab, M., and Abo- Zahhad, M. Recent advances in image processing techniques for automated leaf pest and disease recognition – a review. Information Processing in Agriculture 8, 1 (2021), 27–51.
Pouli, T., Reinhard, E., and Cunningham, D. W. Image Statistics in Visual Computing, 1st ed. A. K. Peters, Ltd., USA, 2013.
Ramcharan, A., Baranowski, K., Mc-Closkey, P., Ahmed, B., Legg, J., and Hughes, D. P. Deep learning for image-based cassava disease detection. Frontiers in Plant Science 8 (2017), 1852.
Rijsbergen, C. J. V. Information Retrieval, 2nd ed. Butterworth-Heinemann, USA, 1979.
Robertson, A. R. The cie 1976 color-difference formulae. Color Research & Application 2, 1 (1977), 7–11.
Schuler, J., Roman´ı, S., Abdel-nasser, M., Rashwan, H., and Puig, D. Grouped Pointwise Convolutions Significantly Reduces Parameters in EfficientNet. IOS Press, 10 2021, pp. 383–391.
Schuler, J., Roman´ı, S., Abdel-nasser, M., Rashwan, H., and Puig, D. Reliable Deep Learning Plant Leaf Disease Classification Based on Light-Chroma Separated Branches. IOS Press, 10 2021, pp. 375–381.
Schuler, J. P. S. K-cai neural api, Dec. 2021.
Schwarz Schuler, J. P., Romani, S., Abdel-Nasser, M., Rashwan, H., and Puig, D. Grouped pointwise convolutions reduce parameters in convolutional neural networks. MENDEL 28, 1 (Jun. 2022), 23–31.
Schuler, J. P. S. Optimizing cnns first layer with respect to color encoding. In 6th URV Doctoral Workshop in Computer Science and Mathematics (Tarragona, Catalunya, Spain, 4 2020), C. J. . A. Valls, Ed., vol. 1, Universitat Rovira i Virgil, Universitat Rovira i Virgil, p. 4.
Simonyan, K., and Zisserman, A. Very deep convolutional networks for large-scale image recognition. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015), Y. Bengio and Y. LeCun, Eds.
Singh, D., Jain, N., Jain, P., Kayal, P., Kumawat, S., and Batra, N. Plantdoc: A dataset for visual plant disease detection. In Proceedings of the 7th ACM IKDD CoDS and 25th COMAD (New York, NY, USA, 2020), CoDS COMAD 2020, Association for Computing Machinery, p. 249–253.
Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., and Stefanovic, D. Deep neural networks based recognition of plant diseases by leaf image classification. Computational Intelligence and Neuroscience 2016 (Jun 2016), 3289801.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. Going deeper with convolutions. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Los Alamitos, CA, USA, jun 2015), IEEE Computer Society, pp. 1–9.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. Rethinking the inception architecture for computer vision. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016), IEEE, pp. 2818–2826.
Toda, Y., and Okura, F. How convolutional neural networks diagnose plant disease. Plant Phenomics 2019 (03 2019).
Wang, G., Sun, Y., and Wang, J. Automatic image-based plant disease severity estimation using deep learning. Computational Intelligence and Neuroscience 2017 (Jul 2017), 2917536.
Wang, M. Multi-path convolutional neural networks for complex image classification. CoRR abs/1506.04701 (2015).
Weiss, K., Khoshgoftaar, T. M., and Wang, D. A survey of transfer learning. Journal of Big Data 3, 1 (May 2016), 9.
Wyszecki, G., and Stiles, W. Color Science: Concepts and Methods, Quantitative Data and Formulae, 2nd ed. John Wiley & Sons, New York, 07 2000.
Zambon, I., Cecchini, M., Egidi, G., Saporito, M. G., and Colantoni, A. Revolution 4.0: Industry vs. agriculture in a future development for smes. Processes 7, 1 (2019), 36.
Zeiler, Matthew D.and Fergus, R. Visualizing and understanding convolutional networks. In Computer Vision – ECCV 2014 (Cham, 2014), D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, Eds., Springer International Publishing, pp. 818–833.
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