Identification of Paddy Rice Diseases Using Deep Convolutional Neural Networks

Year 2022, Volume: 32 Issue: 4, 705 - 713, 30.12.2022

Hakkı Fırat Altınbilek , Ünal Kızıl

https://doi.org/10.29133/yyutbd.1140911

Cited By: 2

Abstract

In modern digital agricultural applications, automatic identification and diagnosis of plant diseases using artificial intelligence is becoming popular and widespread. Deep learning is a promising tool in pattern recognition and machine learning and it can be used to identify and classify diseases in paddy rice. In this study, 2 different paddy rice diseases, including rice blast and brown spot, were investigated in the district of İpsala in the province of Edirne between the 2020 and 2021 production seasons by collecting 1569 images. These diseases are very common and important in Edirne province and surrounding rice production areas. Therefore, practical methods are needed to identify and classify these two diseases. A Convolutional Neural Network (CNN) model was created by applying pre-processing techniques such as rescaling, rotation, and data augmentation to the paddy rice disease images. The classification model was created in Google Colab, which is a web-based Python editor using Tensorflow and Keras libraries. The CNN model was able to classify rice blast and brown spot diseases with high accuracy of 91.70%.

Keywords

Deep learning, Image classification, Paddy rice disease

References

• Affonso, C., Rossi, A. L. D., Vieira, F. H. A., de Carvalho, & de Leon Ferreira de Carvalho, A.C.P. (2017). Deep learning for biological image classification. Expert Systems with Applications, 85, 114–122. https://doi.org/10.1016/j.eswa.2017.05.039
• Anadhan, K., & Singh, A.S. (2021). Detection of paddy crops diseases and early diagnosis using faster regional convolutional neural networks. Paper presentated at the International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 898-902, March 4-5, India.
• Anonymous, (2022). Multi-hot sparse categorical cross-entropy. https://cwiki.apache.org/confluence/display/MXNET/Multi-hot+Sparse+Categorical+Cross-entropy. Access date: 06:06:2022.
• Arnal Barbedo, J.G. (2013). Digital image processing techniques for detecting, quantifying and classifying plant diseases. SpringerPlus, 2, 660. https://doi.org/10.1186/2193-1801-2-660
• Asfarian, A., Herdiyeni, Y., Rauf, A., & Mutaqin, K.H. (2013). Paddy diseases identification with texture analysis using fractal descriptors based on fourier spectrum. Paper presentated at International Conference on Computer, Control, Informatics and Its Applications (IC3INA), 77-81, November 19-21, Indonesia.
• Boulent, J., Foucher, S., Théau, J., & St-Charles, P.L. (2019). Convolutional neural networks for the automatic identification of plant diseases. Frontiers in Plant Science, 10, 941. https://doi.org/10.3389/fpls.2019.00941
• Ferentinos, K.P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311–318.
• Gokulnath, B.V., & Usha, D. G. (2021). Identifying and classifying plant disease using resilient LF-CNN. Ecological Informatics, 63,1, 101283. https://doi.org/10.1016/j.ecoinf.2021.101283
• Kadhim, M. A., & Abed, M. H. (2019). Convolutional neural network for satellite image classification. Studies in Computational Intelligence, 165–178. https://doi.org/10.1007/978-3-030-14132-5_13
• Kanani, P., & Padole, M. (2019). Deep learning to detect skin cancer using google colab. International Journal of Engineering and Advanced Technology, 8,6, 2176-2183. doi:10.35940/ijeat.F8587.088619
• Kawasaki, Y., Uga, H., Kagiwada, S., & Iyatomi, H. (2015). Basic study of automated diagnosis of viral plant diseases using convolutional neural networks. Lecture Notes in Computer Science, 638-645. https://doi.org/10.1007/978-3-319-27863-6_59
• Lu, Y., Yi, S., Zeng, N., Liu, Y., & Zhang, Y. (2017). Identification of rice diseases using deep convolutional neural networks. Neurocomputing, 267, 378-384. https://doi.org/10.1016/j.neucom.2017.06.023
• Mique, E. L., & Palaoag, T. D. (2018). Rice pest and disease detection using convolutional neural network. Paper presentated at Proceedings of the 2018 International Conference on Information Science and System - ICISS ’18, 147–151, April 27-29, Republic of Korea. https://doi.org/10.1145/3209914.3209945
• Pinki, F.T., Khatun, N., & Islam, S.M.M. (2017). Content based paddy leaf disease recognition and remedy prediction using support vector machine. Paper presentated at 20th International Conference of Computer and Information Technology (ICCIT), 1-5, December 22-24, Bangladesh. doi: 10.1109/ICCITECHN.2017.8281764
• Priyadharshini, R.A., Arivazhagan, S., Arun, M., & Mirnalini, A. (2019). Maize leaf disease classification using deep convolutional neural networks. Neural Computing and Applications, 31, 8887-8895. https://doi.org/10.1007/s00521-019-04228-3
• Rajaraman, S., Candemir, S., Kim, I., Thoma, G., & Antani, S. (2018). Visualization and interpretation of convolutional neural network predictions in detecting pneumonia in pediatric chest radiographs. Applied Sciences, 8,10, 1715. https://doi.org/10.3390/app8101715
• Rawat, W., & Wang, Z. (2017). Deep convolutional neural networks for ımage classification: a comprehensive review. Neural Computation, 29,9, 2352–2449. https://doi.org/10.1162/neco_a_00990
• Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A., & Fei-Fei, L. (2015). Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115,3, 211–252. https://doi.org/10.48550/arXiv.1409.0575
• Ruuska, S., Hämäläinen, W., Kajava, S., Mughal, M., Matilainen, P., & Mononen, J. (2018). Evaluation of the confusion matrix method in the validation of an automated system for measuring feeding behaviour of cattle. Behavioural Processes, 148, 56-62. https://doi.org/10.1016/j.beproc.2018.01.004
• Tawde, T., Verekar, L., Aswale, S., Deshmukh, K., Reddy, A., & Shetgaonkar, P. (2021). Rice plant disease detection and classification techniques: a survey. International Journal of Engineering Research & Technology, 10,7, 560-567.
• Scherer, D., Muller, A., & Behnke, S. (2010). Evaluation of pooling operations in convolutional architectures for object recognition. Paper presentated at In Proceedings of the 20th International Conference on Artificial Neural Networks, 92-101, September 15–18, Thessaloniki, Greece. https://doi.org/10.1007/978-3-642-15825-4_10
• Shrivastava, V.K., Pradhan, M.K., Minz, S., & Thakur, M.P. (2019). Rice plant disease classification using transfer learning of deep convolution neural network. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-3/W6, 631-635. DOI:10.5194/isprs-archives-XLII-3-W6-631-2019
• Siddiqi, R. (2019). Automated pneumonia diagnosis using a customized sequential convolutional neural network. Paper presentated at Proceedings of the 3rd International Conference on Deep Learning Technologies, 64-70, July 5-7, Xiamen, China. https://doi.org/10.1145/3342999.3343001 Vanitha, V. (2019). Rice disease detection using deep learning. International Journal of Recent Technology and Engineering, 7, 534-542.
• Vardhini, P.A.H., Asritha, S., & Devi, Y.S. (2020). Efficient disease detection of paddy crop using CNN. Paper presentated at 2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), 116-119, October 9-10, Bengaluru, India. doi: 10.1109/ICSTCEE49637.2020.9276775
• Wang, P., Fan, E., & Wang, P. (2020). Comparative analysis of image classification algorithms based on traditional machine learning and deep learning. Pattern Recognition Letters, 141(11): 61-67. doi:10.1016/j.patrec.2020.07.042
• Xiao, M., Ma, Y., Feng, Z., Deng, Z., Hou, S., Shu, L., & Lu, Z. (2018). Rice blast recognition based on principal component analysis and neural network. Computers and Electronics in Agriculture, 154, 482–490. https://doi.org/10.1016/j.compag.2018.08.028
• Zhang, J., Xie, Y., Wu, Q., & Xia, Y. (2019). Medical image classification using synergic deep learning. Medical Image Analysis, 54, 10-19. doi: 10.1016/j.media.2019.02.010