Rice Leaf Diseases Classification Using Image Processing and Machine Learning

Abdul Mahatir Najar, Maulidyani Abu, Resnawati Resnawati, Syahrullah Syahrullah

Abstract

Identification of diseases from images of plants is one of the interesting research areas in the agriculture field, for which machine learning concepts from the computer science field can be applied. This article presents a prototype system for the detection and classification of rice diseases based on images of infected rice plants. This prototype system was developed after detailed experimental analysis of various techniques used in image processing operations. We consider three rice plant diseases: Bacterial Leaf Blight, Blast, and Tungro. We used the Otsu method to remove the background. To enable accurate extraction of features, we combined Gabor and Sobel techniques. In the classification process, we used five machine learning techniques: Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes (NB), and Quadratic Discriminant Analysis (QDA). We empirically evaluated these methods, achieving 77%, 50%, 60%, and 37% accuracy, respectively.

 Full Text:

PDF

References

Azim, M. A., Islam, M. K., Rahman, Md. M., & Jahan, F. (2021). An effective feature extraction method for rice leaf disease classification. TELKOMNIKA, 19(2), 463. https://doi.org/10.12928/telkomnika.v19i2.16488

Biau, G., & Scornet, E. (2016). A random forest guided tour. TEST, 25(2), 197–227. https://doi.org/10.1007/s11749-016-0481-7

Daniya, T., & Vigneshwari, D. S. (2019). A review on machine learning techniques for rice plant disease detection in agricultural research. International Journal of Advanced Science and Technology, 28(13), 49-62.

FAO. (2018). Asia and the Pacific regional overview of food security and nutrition 2018: Accelerating progress towards the SDGs. Food & Agriculture Organization of the United Nations.

FAO. (2019). Global Report on Food Crises. Food & Agriculture Organization of the United Nations.

Gupta, S., & Sarvaiya, J. N. (2020). Emerging Technology Trends in Electronics, Communication and Networking: Third International Conference, ET2ECN 2020, Surat, India, February 7–8, 2020, Revised Selected Papers (Vol. 1214). Springer Singapore. https://doi.org/10.1007/978-981-15-7219-7

Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23-37.

Kemenkes. (2018). Hasil Utama Riskesdas 2018. Badan Penelitian dan Pengembangan Kesehatan-Kementerian Kesehatan.

Li, N., Lv, X., Xu, S., Li, B., & Gu, Y. (2020). An improved water surface images segmentation algorithm based on the otsu method. Journal of Circuits, Systems and Computers, 29(15), 2050251. https://doi.org/10.1142/S0218126620502515

Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7, 1419.

Pisner, D. A., & Schnyer, D. M. (2020). Support vector machine. In Machine Learning (pp. 101–121). Elsevier. https://doi.org/10.1016/B978-0-12-815739-8.00006-7

Shah, J. P., Prajapati, H. B., & Dabhi, V. K. (2016). A survey on detection and classification of rice plant diseases. 2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC), 1–8. https://doi.org/10.1109/ICCTAC.2016.7567333

Swain, S., Nayak, S. K., & Barik, S. S. (2020). A review on plant leaf diseases detection and classification based on machine learning models. Mukt Shabd Journal, 9(7), 5195-5205. https://doi.org/09.0014.MSJ.2020.V9I6.0086781.105023

Tharwat, A. (2016). Linear vs. quadratic discriminant analysis classifier: A tutorial. International Journal of Applied Pattern Recognition, 3(2), 145. https://doi.org/10.1504/IJAPR.2016.079050

Yang, F.-J. (2018). An Implementation of Naive Bayes Classifier. 2018 International Conference on Computational Science and Computational Intelligence (CSCI), 301–306. https://doi.org/10.1109/CSCI46756.2018.00065

Zheng, Z., Xie, S., Dai, H., Chen, X., & Wang, H. (2020). An overview of blockchain technology: Architecture, consensus, and future trends. In IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) (pp. 1861-1865). IEEE.

DOI: https://doi.org/10.24815/tjoma.v3i1.38642

Refbacks

  • There are currently no refbacks.
Copyright (c) 2024 Transcendent Journal of Mathematics and Applications Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.