Detection of Paddy Blast: An Image Processing Approach with Threshold based OTSU
Keywords:Paddy blast , Rice blast , Threshold based OTSU, Image Processing, Plant disease
If rice infections spread, the agricultural industry as well as the people who eat rice as their primary food grain suffer greatly from production and financial losses as well as food shortages. One of the deadliest diseases that can affect paddy plants at any stage of development and hinder the growth of rice plants is paddy leaf blast. Because the brown spot and the leaf blast have the same appearance but distinct shapes, it is quite difficult to distinguish between them. In this case, paddy leaf blast is detected using computer vision methods. But because of their resemblance to other spots and poor color channel selection, previous procedures are difficult, time-consuming, and poorly able to detect blasts. In this article, an effective and automated image analysis method has been proposed to identify paddy leaf blasts that can identify leaf blasts by utilizing various shapes. Additionally, the process minimized pointless data exploration and provided superior accuracy of 95.34 percent.
Liu, W., Liu, J., Ning, Y., Ding, B., Wang, X., Wang, Z., & Wang, G. L. (2013). “Recent progress in understanding PAMP-and effector triggered immunity against the rice blast fungus Magnaporthe oryzae”. Molecular plant, 6(3), 605-620.
Prabavathy, V. R., Mathivanan, N., & Murugesan, K. (2006). “Control of blast and sheath blight diseases of rice using antifungal metabolites produced by Streptomyces” sp. PM5. Biological Control, 39(3), 313319. Conference Name:ACM Woodstock conference
Narmadha, R. P., & Arulvadivu, G. (2017, January). “Detection and measurement of paddy leaf disease symptoms using image processing”. In 2017 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1-4). IEEE.
Shabana, Y. M., Abdel-Fattah, G. M., Ismail, A. E., & Rashad, Y. M. (2008). “Control of brown spot pathogen of rice (Bipolaris oryzae) using some phenolic antioxidants”. Brazilian Journal of Microbiology, 39(3), 438-444.
Phadikar, S., Sil, J., & Das, A. K. (2013). “Rice diseases classification using feature selection and rule generation techniques”. Computers and electronics in agriculture, 90, 76-85.
Kurniawati, N. N., Abdullah, S. N. H. S., Abdullah, S., & Abdullah, S. (2009, December). “Investigation on image processing techniques for diagnosing paddy diseases”. In 2009 international conference of soft computing and pattern recognition (pp. 272-277). IEEE.
Skamnioti, P., & Gurr, S. J. (2009). “Against the grain: safeguarding rice from rice blast disease”. Trends in biotechnology, 27(3), 141-150.
Huke, R. E. (1982). “Rice area by type of culture: South, Southeast, and East Asia”. Int. Rice Res. Inst.
Levy, M., Correa-Victoria, F. J., Zeigler, R. S., Xu, S., & Hamer, J. E. (1993). “Genetic diversity of the rice blast fungus in a disease nursery in Colombia”. Phytopathology, 83(12), 1427-1433.
Al-Hiary, H., et al. “Fast and accurate detection and classification of plant diseases.” Machine learning 14.5 (2011).
Phadikar, Santanu, and Jaya Sil. “Rice disease identification using pattern recognition techniques.” Computer and Information Technology, 2008. ICCIT 2008. 11th International Conference on. IEEE, 2008.
Nobuyuki Otsu (1979). “A threshold selection method from gray-level histograms”. IEEE Trans. Sys. Man. Cyber. 9 (1): 62–66
Wang, Haiguang, et al. “Application of neural networks to image recognition of plant diseases.” Systems and Informatics (ICSAI), 2012 International Conference on. IEEE, 2012.
Phadikar, Santanu, Jaya Sil, and Asit Kumar Das. “Classification of Rice Leaf Diseases Based onMorphological Changes.” International Journal of Information and Electronics Engineering 2.3 (2012): 460.
Kurniawati, Nunik Noviana, et al. “Texture analysis for diagnosing paddy disease.” Electrical Engineering and Informatics, 2009. ICEEI’09. International Conference on. Vol. 1. IEEE, 2009.
Deshmukh, Radhika.” Detection of Paddy Leaf Diseases. “Energy (An- gular Second Moment) 2 (2015): 4-3.
Pixia, Dong, and Wang Xiangdong. “Recognition of greenhouse cu- cumber disease based on image processing technology.” Open Journal of Applied Sciences 3.01 (2013): 27.
Al Bashish, Dheeb, Malik Braik, and Sulieman Bani-Ahmad. “A frame- work for detection and classification of plant leaf and stem diseases.” Signal and Image Processing (ICSIP), 2010 International Conference on. IEEE, 2010.
Amoda, Niket, Bharat Jadhav, and Smeeta Naikwadi. “Detection and classification of plant diseases by image processing.” International Journal of Innovative Science, Engineering & Technology 1.2 (2014).
Aji, A. F., Munajat, Q., Pratama, A. P., Kalamullah, H., Setiyawan, J., & Arymurthy, A. M. (2013). Detection of palm oil leaf disease with image processing and neural network classification on mobile device. International Journal of Computer Theory and Engineering, 5(3), 528.
How to Cite
Copyright (c) 2022 Tanvir Ahmed, Rashidul Hasan Nabil, MD. Siyamul Islam
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.