BPSO based Feature Selection for Rice Plant Leaf Disease Detection with Random Forest Classifier

BPSO based Feature Selection for Rice Plant Leaf Disease Detection with Random Forest Classifier

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2021 by IJETT Journal
Volume-69 Issue-4
Year of Publication : 2021
Authors : Ashutosh Kumar Singh, Dr. Bharti Chourasia, Dr. Neetesh Raghuwanshi, Dr. K. Raju
DOI :  10.14445/22315381/IJETT-V69I4P206

Citation 

MLA Style: Ashutosh Kumar Singh, Dr. Bharti Chourasia, Dr. Neetesh Raghuwanshi, Dr. K. Raju  "BPSO based Feature Selection for Rice Plant Leaf Disease Detection with Random Forest Classifier" International Journal of Engineering Trends and Technology 69.4(2021):34-43. 

APA Style:Ashutosh Kumar Singh, Dr. Bharti Chourasia, Dr. Neetesh Raghuwanshi, Dr. K. Raju. BPSO based Feature Selection for Rice Plant Leaf Disease Detection with Random Forest Classifier  International Journal of Engineering Trends and Technology, 69(4),34-43.

Abstract
Recently, Machine Learning and computer vision have generated interest and have found new applications in engineering. In agriculture, “smart” systems have become important tools for detecting anomalies that decrease the quality and quantity in the harvest of agricultural products. This paper intended to detect three rice diseases, namely Brown-spot, Bacterial Leaf blight, and Leaf smut, using the Random Forest Classifier technique of machine learning with image processing. The color moments are extracted for color features, while the Gabor Wavelet and Harris Corner methods are used for texture features extraction of PlantVillage Dataset images for rice plant leaf disease detection. The binary particle swarm optimization (BPSO) is then applied for the feature selection from the extracted features. Finally, Random Forest Classifier is used for the classification of extracted features to obtain the simulation results in terms of precision, sensitivity, and accuracy using a confusion matrix plot.

Reference
[1] Tai, A.P., Martin, M.V. and Heald, C.L., Threat to future global food security from climate change and ozone air pollution. Nature Climate Change, 4(9)(2014) 817-821.
[2] Harvey, C.A., Rakotobe, Z.L., Rao, N.S., Dave, R., Razafimahatratra, H., Rabarijohn, R.H., Rajaofara, H. and MacKinnon, J.L., 2014. The extreme vulnerability of smallholder farmers to agricultural risks and climate change in Madagascar. Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1639) 20130089.
[3] Sanchez, P.A., and Swaminathan, M.S., 2005. Cutting world hunger in half. Science, 307(5708) 357-359.
[4] Barbedo, J.G.A., 2013. Digital image processing techniques for detecting, quantifying, and classifying plant diseases. SpringerPlus, 2(1)(2013) 660.
[5] DerwinSuhartono, W.A., Lestari, M. and Yasin, M., 2013. Expert system in detecting coffee plant diseases. Int. J. Electr. Energy, 1(3) 156-162.
[6] Bhange, M. and Hingoliwala, H.A.,. Smart farming: Pomegranate disease detection using image processing. Procedia Computer Science, 58(2015) 280-288.
[7] Mengistu, A.D., Alemayehu, D.M. and Mengistu, S.G., 2016. Ethiopian coffee plant disease recognition based on imaging and machine learning techniques. International Journal of Database Theory and Application, 9(4)(2016) 79-88.
[8] Singh, V. and Misra, A.K., Detection of plant leaf diseases using image segmentation and soft computing techniques. Information processing in Agriculture, 4(1)(2017) 41-49.
[9] Barbedo, J.G.A., Koenigkan, L.V. and Santos, T.T., Identifying multiple plant diseases using digital image processing. Biosystems Engineering, 147(2016) 104-116.
[10] Qin, F., Liu, D., Sun, B., Ruan, L., Ma, Z. and Wang, H., Identification of alfalfa leaf diseases using image recognition technology. PLoS One, 11(12)(2016) e0168274.
[11] Pujari, D., Yakkundimath, R. and Byadgi, A.S., 2016. SVM and ANN-based classification of plant diseases using feature reduction technique. IJIMAI, 3(7)(2016) 6-14.
[12] Kiani, E. and Mamedov, T., Identification of plant disease infection using soft-computing: Application to modern botany. Procedia computer science, 120(2017) 893-900.
[13] Krizhevsky, A., Sutskever, I. and Hinton, G.E., 2017. Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6)(2017) 84-90.
[14] Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V. and Rabinovich, A., 2015. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (2015) (1-9).
[15] Krizhevsky, A., 2014. One weird trick for parallelizing convolutional neural networks. arXiv preprint arXiv:1404.5997.
[16] Mohanty S.P., Hughes, D.P. and Salathé, M., Using deep learning for image-based plant disease detection. Frontiers in plant science, 7(2016) 1419.
[17] Ashqar, B.A. and Abu-Naser, S.S.,. Image-Based Tomato Leaves Diseases Detection Using Deep Learning.
[18] Barbedo, J.G.A., 2019. Plant disease identification from individual lesions and spots using deep learning. Biosystems Engineering, 180(2018) 96-107.
[19] Simonyan, K. and Zisserman, A., Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556., (2014).
[20] Khan, S. and Narvekar, M., Disorder Detection in Tomato Plant Using Deep Learning. In Advanced Computing Technologies and Applications (2020) 187-197. Springer, Singapore.
[21] PlantVillage Dataset for leaf disease detection, Available online at https://www.kaggle.com/emmarex/plantdisease
[22] Keen, N., Color moments. School Of Informatics, University Of Edinburgh, (2005) 3-6.
[23] Daugman, J.G., Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression. IEEE Transactions on acoustics, speech, and signal processing, 36(7)(1988) 1169-1179.
[24] Prasad, S., Kumar, P., Hazra, R. and Kumar, A., 2012, December. Plant leaf disease detection using Gabor wavelet transform. In International Conference on Swarm, Evolutionary, and Memetic Computing (2012) 372-379. Springer, Berlin, Heidelberg.
[25] Sun, Y., Jiang, Z., Zhang, L., Dong, W. and Rao, Y., SLIC_SVM based leaf diseases saliency map extraction of the tea plant. Computers and Electronics in Agriculture, 157(2019) 102-109.
[26] Devi, K.S., Srinivasan, P. and Bandhopadhyay, S., H2K–A robust and optimum approach for detection and classification of groundnut leaf diseases. Computers and Electronics in Agriculture, 178(2020) 105749.
[27] Kenedy, J., and R. C. Eberhart. A discrete binary version of the particle swarm optimization." Computational Cybernetics and Simulation 5 (1997) 4104-4108.
[28] Khanesar, M.A., Teshnehlab, M. and Shoorehdeli, M.A., June. A novel binary particle swarm optimization. In Mediterranean conference on control & automation (2007) 1-6 IEEE.
[29] Breiman, L., 2001. Random forests. Machine learning, 45(1)(2007) 5-32.
[30] K. Vimala, Dr. D. Usha. An Efficient Classification of Congenital Fetal Heart Disorder using Improved Random Forest Algorithm International Journal of Engineering Trends and Technology 68(12) (2020) 182-186. [31] Raju, K., Pilli, S. K., Kumar, G. S. S., Saikumar, K., & Jagan, B. O. L., Implementation of natural random forest machine learning methods on multispectral image compression. J Crit Rev, 6(5)(2019) 265-273.

Keywords
BPSO, Gabor Wavelet, Harris Corner, Random Forest Classifier.