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Diagnosis of Bacterial Leaf Blight, Brown Spots, and Leaf Smut Rice Plant Diseases using Light GBM

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2022
G.R.I.L. Jayasooriya, Samantha Mathara Arachchi

G R I L Jayasooriya and Samantha Mathara Arachchi. Diagnosis of Bacterial Leaf Blight, Brown Spots, and Leaf Smut Rice Plant Diseases using Light GBM. International Journal of Computer Applications 183(48):53-58, January 2022. BibTeX

	author = {G.R.I.L. Jayasooriya and Samantha Mathara Arachchi},
	title = {Diagnosis of Bacterial Leaf Blight, Brown Spots, and Leaf Smut Rice Plant Diseases using Light GBM},
	journal = {International Journal of Computer Applications},
	issue_date = {January 2022},
	volume = {183},
	number = {48},
	month = {Jan},
	year = {2022},
	issn = {0975-8887},
	pages = {53-58},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2022921895},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Considering the human population, food is one of the major problems Sri Lanka might face in the near future. Rice is the most widely consumed food product and one of the extensively cultivated crops in Sri Lanka. Therefore, increasing the crop yield is one of the primary needs of the country. When rice crops are infected with diseases, it results in a loss of crops. Therefore, it is essential to identify the disease in the early stage of infection to prevent the damage that can be done. Disease identification could be challenging without a clear understanding. With the advancement of new technologies, researchers are interested in identifying paddy diseases through machine learning and image processing techniques to help farmers identify infectious diseases accurately.

It is difficult to observe the paddy leaf with the naked eye to diagnose the infected disease. In this research, an algorithm was developed to check whether the image contains different changes to the paddy leaf by considering the green colour pixels and their variance. OpenCV libraries have been used to develop the algorithm for feature extraction. Those features were used as attributes to the LightGBM algorithm to classify the disease images with over 80% accuracy.


  1. A. A. Gunawardana, "Agriculture sector performance in the Sri Lankan Economy: A systematic review and a Meta data analysis from 2012 to 2016.," 2018.
  2. R. Rambukwella and E. A. C. Priyankara, "Production and marketing of traditional rice varieties in selected districts in Sri Lanka: present status and future prospects," Hector Kobbekaduwa Agrarian Research and Training Institute, Colombo, Sri Lanka, 2016.
  3. S. N. Seneviratne, S. de and P. Jeyanandarajah, "RICE DISEASES -PROBLEM AND PROGRESS," Tropical Agricultural Research and Extension, 2004.
  4. D. N. Mangla, P. B. Raj and S. G. Hegde, "Paddy Leaf Disease Detection Using Image Processing and Machine Learning," 2019.
  5. P. K. Sethy, N. K. Barpanda, A. K. Rath and S. K. Behera, "Image Processing Techniques for Diagnosing Rice Plant Disease: A Survey," Procedia Computer Science , pp. 516-530, 2020.
  6. L. Nugaliyadde, N. Dissanayake and J. Mitrasena, "Advance in pest and disease management of rice in Sri Lanka : A review".
  7. J. W. Orillo, J. D. Cruz, L. Agapito, P. J. Satimbre and I. Valenzuela, "Identification of diseases in rice plants (oryza sativa) using back propagation Artificial Neural Network," in International Conference on Humanoid, Nanotechnology, Information Technology, Communication, and Control, Environment and Management (HNICEM), IEEE, Palawan, Philippines, 2014.
  8. M. Mukherjee, T. Pal and D. Samanta, "DAMAGED PADDY LEAF DETECTION USING IMAGE PROCESSING," 2010.
  9. C. Yu, C. Dian-ren, L. Yang and C. Lei, "Otsu’s thresholding method based on gray level-gradient two-dimensional histogram," in 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010), IEEE, Wuhan, China, 2010.
  10. W. Noble, "What is a support vector machine? Nat Biotechnol," p. 1565–1567, 2006.
  11. P. R. Chaudhari, N. Tamrakar, L. Singh, A. Tandon and D. Sharma, "Rice nutritional and medicinal properties: A review article 7".
  12. J. A. M. Basilio, G. A. Torres, S. Pérez, L. K. T. Medina and H. M. P. Meana, "Explicit Image Detection using YCbCr Space Color Model as Skin Detection," vol. 7, 2014.
  13. B. C. K. Ly, E. B. Dyer, J. L. Feig, A. L. Chien and S. D. Bino, "Research Techniques Made Simple: Cutaneous Colorimetry: A Reliable Technique for Objective Skin Color Measurement.," Journal of Investigative Dermatology, vol. 140, pp. 3-12, 2020.
  14. M. Buscema, "Back Propagation Neural Networks 38," 1998.
  15. G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye and T. Y. Liu, "LightGBM: A Highly Efficient Gradient Boosting Decision Tree".
  16. X. Zheng, Q. Lei, R. Yao, Y. Gong and Q. Yin, "Image segmentation based on adaptive K-means algorithm. J Image Video Proc," 2018.
  17. J. H. Bear, "Understanding HSV Color Model," ThoughtCo, 2017. [Online]. Available:


Decision Tree, Diagnosis, Diseases, Leaves, Light GBM, Open CV, Rice