Innovative Approaches to Rice (Oryza sativa) Crop Health: A Comprehensive Analysis of Deep Transfer Learning for Early Disease Detection

Year 2024, , 314 - 322, 30.06.2024

Utpal Barman , Dulumani Das , Gunikhan Sonowal , Mala Dutta

https://doi.org/10.29133/yyutbd.1402821

Abstract

In this research, the primary objective is to tackle the pressing issue of identifying and effectively managing diseases in rice plants, a problem that can results in substantial crop losses and pose a severe threat to food security. The study employs Convolutional Neural Networks (CNNs), a type of deep learning model widely used for image analysis, to conduct an extensive investigation using a sizable dataset comprising 5,932 RGB images. These images represent four distinct disease classes in rice plants: Bacterial Leaf Blight (BLB), Blast, Brownspot, and Tungro. To conduct this research, the dataset is split into two subsets: a training set, which comprises 80% of the data, and a testing set, which makes up the remaining 20%. This division allows for a systematic evaluation of the performance of four different CNN architectures: VGGNet, ResNet, MobileNet, and a simpler CNN model. The results of this study consistently show that ResNet and MobileNet outperform the other CNN architectures in terms of their ability to accurately detect diseases in rice plants. These two models consistently achieve remarkable accuracy in identifying these diseases. The research findings not only emphasize the potential of deep learning techniques in addressing the critical issue of rice crop diseases but also highlights the significant role that ResNet and MobileNet play in strengthening crop protection efforts and contributing to global food security.

Keywords

Transfer leaning, CNN, Rice Disease, Artificial Intelligence, Sustainable agriculture

Ethical Statement

Ethics approval was not required for this study

Supporting Institution

Assam down town University

Project Number

Rice Disease Detection

Thanks

We thanks to Assam down town University

References

• Barman, U., & Choudhury, R. D. (2019). Bacterial and virus affected citrus leaf disease classification using smartphone and SVM. Bact. Virus Affect. Citrus Leaf Dis. Classif. Using Smartphone SVM, 8, 4220–4226.
• Barman, U., & Choudhury, R. D. (2022). Smartphone assist deep neural network to detect the citrus diseases in agri-informatics. Global Transitions Proceedings, 3(2), 392–398.
• Barman, U., Choudhury, R. D., Sahu, D., & Barman, G. G. (2020). Comparison of convolution neural networks for smartphone image based real time classification of citrus leaf disease. Computers and Electronics in Agriculture, 177, 105661.
• Barman, U., Pathak, C., & Mazumder, N. K. (2023). Comparative assessment of pestdamageidentification of coconut plant using damage texture and color analysis. Multimedia Tools and Applications, 1–23.
• Barman, U., Sahu, D., Barman, G. G., & Das, J. (2020). Comparative assessment of deep learning to detect the leaf diseases of potato based on data augmentation. 2020 International Conference on Computational Performance Evaluation (ComPE), 682–687. IEEE. Retrieved from https://ieeexplore.ieee.org/abstract/document/9200015/
• Deng, R., Tao, M., Xing, H., Yang, X., Liu, C., Liao, K., & Qi, L. (2021). Automatic diagnosis of rice diseases using deep learning. Frontiers in Plant Science, 12, 701038.
• Gowda, B., Sendhil, R., Adak, T., Raghu, S., Patil, N., Mahendiran, A., … Damalas, C. A. (2021). Determinants of rice farmers’ intention to use pesticides in eastern India: Application of an extended version of the planned behavior theory. Sustainable Production and Consumption, 26, 814–823.
• He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770–778. Retrieved from http://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html
• Latif, G., Abdelhamid, S. E., Mallouhy, R. E., Alghazo, J., & Kazimi, Z. A. (2022). Deep learning utilization in agriculture: Detection of rice plant diseases using an improved CNN model. Plants, 11(17), 2230.
• Liang, W., Zhang, H., Zhang, G., & Cao, H. (2019). Rice blast disease recognition using a deep convolutional neural network. Scientific Reports, 9(1), 2869.
• Mutka, A. M., & Bart, R. S. (2015). Image-based phenotyping of plant disease symptoms. Frontiers in Plant Science, 5, 734.
• Pathak, H., Nayak, A. K., Jena, M., Singh, O. N., Samal, P., & Sharma, S. G. (2018). Rice research for enhancing productivity, profitability and climate resilience. Not Available.
• Patil, R. R., & Kumar, S. (2021). Predicting rice diseases across diverse agro-meteorological conditions using an artificial intelligence approach. PeerJ Computer Science, 7, e687.
• Prakash, A., Rao, J., Berliner, J., Mukherjee, A., Adak, T., Lenka, S., … Nayak, U. K. (2014). Emerging pest scenario in rice in India. J. Appl. Zool. Res, 25(2), 179–181.
• Sandhya Keelery. (n.d.). Volume of rice production across Assam in India from financial year 2009 to 2021 [Data set]. https://www.statista.com/statistics/1019612/india-rice-production-volume-in-assam/. Retrieved from https://www.statista.com/statistics/1019612/india-rice-production-volume-in-assam/
• Sethy, P. K., Barpanda, N. K., Rath, A. K., & Behera, S. K. (2020). Deep feature based rice leaf disease identification using support vector machine. Computers and Electronics in Agriculture, 175, 105527.
• Sharma, N., Jain, V., & Mishra, A. (2018). An analysis of convolutional neural networks for image classification. Procedia Computer Science, 132, 377–384.
• Simonyan, K., & Zisserman, A. (2015, April 10). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv. Retrieved from http://arxiv.org/abs/1409.1556
• Soren, K. R., Madugula, P., Kumar, N., Barmukh, R., Sengar, M. S., Bharadwaj, C., … Singh, J. (2020). Genetic dissection and identification of candidate genes for salinity tolerance using Axiom® CicerSNP array in chickpea. International Journal of Molecular Sciences, 21(14), 5058.
• Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2818–2826. Retrieved from https://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Szegedy_Rethinking_the_Inception_CVPR_2016_paper.html
• Upadhyay, S. K., & Kumar, A. (2021). A novel approach for rice plant diseases classification with deep convolutional neural network. International Journal of Information Technology, 1–15.
• Voulodimos, A., Doulamis, N., Bebis, G., & Stathaki, T. (2018). Recent developments in deep learning for engineering applications. Computational Intelligence and Neuroscience, 2018. Retrieved from https://www.hindawi.com/journals/cin/2018/8141259/
• Wang, Y., Wang, H., & Peng, Z. (2021). Rice diseases detection and classification using attention based neural network and bayesian optimization. Expert Systems with Applications, 178, 114770.
• Sharma, N., Dutta, M., 2023. Yield Prediction and Recommendation of Crops in India’s Northeastern Region Using Machine Learning Regression Models. Yuzuncu Yil University Journal of Agricultural Sciences, 33(4): 700-708. DOI: https://doi.org/10.29133/yyutbd.1321518