Broccoli leaf diseases classification using support vector machine with particle swarm optimization based on feature selection

(1) Yulio Ferdinand Mail (Telkom University, Indonesia)
(2) * Wikky Fawwaz Al Maki Mail (Telkom University, Indonesia)
*corresponding author


Broccoli is a plant that has many benefits. The flower parts of broccoli contain protein, calcium, vitamin A, vitamin C, and many more. However, in its cultivation, broccoli plants have obstacles such as the presence of pests and diseases that can affect production of broccoli. To avoid this, the authors build a model to identify diseases in broccoli through leaf images with a size of 128x128 pixels. The model is constructed to classify healthy leaves, and disease leaves using the image processing method that uses machine learning stages. There are several stages, including K-Means segmentation, colour feature extraction, and classification using SVM (Support Vector Machine) with RBF kernel and PSO (Particle Swarm Optimization) for reduce dimensionality data. The model that has been built compares the SVM model and the SVM-PSO model. It produces good accuracy in the training of 97.63% and testing accuracy of 94.48% for SVM-PSO and 85.82% for training, and 86.25% for testing in the SVM model. Therefore, this proposed model can produce good results in categorizing healthy and diseased leaves in broccoli.



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[1] G. S. Nagraj, A. Chouksey, S. Jaiswal, and A. K. Jaiswal, “Broccoli,” in Nutritional Composition and Antioxidant Properties of Fruits and Vegetables, Elsevier, 2020, pp. 5–17, doi: 10.1016/B978-0-12-812780-3.00001-5.

[2] I. Hassini, J. J. Rios, P. Garcia-Ibañez, N. Baenas, M. Carvajal, and D. A. Moreno, “Comparative effect of elicitors on the physiology and secondary metabolites in broccoli plants,” J. Plant Physiol., vol. 239, pp. 1–9, Aug. 2019, doi: 10.1016/j.jplph.2019.05.008.

[3] P. Bhattacharjee and R. S. Singhal, “Broccoli and Cauliflower,” in Handbook of Vegetables and Vegetable Processing, vol. 2–2, Chichester, UK: John Wiley & Sons, Ltd, 2018, pp. 535–558, doi: 10.1002/9781119098935.ch23

[4] O. I. A. LAfi, H. A. El-Hamarnah, N. J. H. Al-Saloul, H. I. A. Radwan, and S. S. Abu-Naser, “A Proposed Expert System for Broccoli Diseases Diagnosis,” International Journal of Engineering and Information Systems (IJEAIS), vol. 6. pp. 43–51, 2022, Accessed: Jan. 04, 2020. [Online]. Available:

[5] N. M. Patil and M. U. Nemade, “Content-Based Audio Classification and Retrieval Using Segmentation, Feature Extraction and Neural Network Approach,” Adv. Intell. Syst. Comput., vol. 924, pp. 263–281, 2019, doi: 10.1007/978-981-13-6861-5_23/COVER.

[6] M. Takruri, M. K. A. Mahmoud, and A. Al-Jumaily, “PSO-SVM hybrid system for melanoma detection from histo-pathological images,” Int. J. Electr. Comput. Eng., vol. 9, no. 4, pp. 2941–2949, Aug. 2019, doi: 10.11591/IJECE.V9I4.PP2941-2949.

[7] N. Zeng, H. Qiu, Z. Wang, W. Liu, H. Zhang, and Y. Li, “A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer’s disease,” Neurocomputing, vol. 320, pp. 195–202, Dec. 2018, doi: 10.1016/J.NEUCOM.2018.09.001.

[8] I. Izonin, A. Trostianchyn, Z. Duriagina, R. Tkachenko, T. Tepla, and N. Lotoshynska, “The combined use of the wiener polynomial and SVM for material classification task in medical implants production,” Int. J. Intell. Syst. Appl., vol. 10, no. 9, pp. 40–47, Sep. 2018, doi: 10.5815/IJISA.2018.09.05.

[9] K. P. Panigrahi, H. Das, A. K. Sahoo, and S. C. Moharana, “Maize Leaf Disease Detection and Classification Using Machine Learning Algorithms,” Adv. Intell. Syst. Comput., vol. 1119, pp. 659–669, 2020, doi: 10.1007/978-981-15-2414-1_66/COVER.

[10] D. K. Choubey, P. Kumar, S. Tripathi, and S. Kumar, “Performance evaluation of classification methods with PCA and PSO for diabetes,” Netw. Model. Anal. Heal. Informatics Bioinforma., vol. 9, no. 1, pp. 1–30, Dec. 2020, doi: 10.1007/S13721-019-0210-8/METRICS.

[11] C. Vega-álvarez, M. Francisco, and P. Soengas, “Black Rot Disease Decreases Young Brassica oleracea Plants’ Biomass but Has No Effect in Adult Plants,” Agron. 2021, Vol. 11, Page 569, vol. 11, no. 3, p. 569, Mar. 2021, doi: 10.3390/AGRONOMY11030569.

[12] S. De Britto, D. Kapera, and S. Jogaiah, “First Report of Leaf Spot Disease Caused by Alternaria brassicicola on Broccoli in Papua New Guinea,”, vol. 104, no. 11, p. 3073, Sep. 2020, doi: 10.1094/PDIS-04-20-0721-PDN.

[13] “Retracted: Kale ( Brassica oleracea var. sabellica) as miracle food with special reference to therapeutic and nutraceuticals perspective,” Food Sci. Nutr., vol. 10, no. 9, p. 3175, Sep. 2021, doi: 10.1002/FSN3.2476.

[14] Q. H. Nguyen et al., “Influence of data splitting on performance of machine learning models in prediction of shear strength of soil,” Math. Probl. Eng., vol. 2021, 2021, doi: 10.1155/2021/4832864.

[15] S. Krig, “Image Pre-Processing,” in Computer Vision Metrics, Berkeley, CA: Apress, 2014, pp. 39–83, doi: 10.1007/978-1-4302-5930-5_2.

[16] D. S. Manoharan, “Performance Analysis of Clustering Based Image Segmentation Techniques,” J. Innov. Image Process., vol. 2, no. 1, pp. 14–24, Mar. 2020, doi: 10.36548/jiip.2020.1.002.

[17] K. Jeevitha, A. Iyswariya, V. Ramkumar, S. Mahaboob Basha, V. Praveen Kumar, and R. M. K. Engineering, “A Review on Varius Segmentation Techniques in Image Processing,” Eur. J. Mol. Clin. Med., vol. 7, no. 4, pp. 1342–1348, 2020, Accessed: Jan. 04, 2020. [Online]. Available:

[18] R. C. Hrosik, E. Tuba, E. Dolicanin, R. Jovanovic, and M. Tuba, “Brain Image Segmentation Based on Firefly Algorithm Combined with K-means Clustering,” Stud. Informatics Control, vol. 28, no. 2, pp. 167–176, Jul. 2019, doi: 10.24846/v28i2y201905.

[19] P. Shan, “Image segmentation method based on K-mean algorithm,” Eurasip J. Image Video Process., vol. 2018, no. 1, pp. 1–9, Dec. 2018, doi: 10.1186/S13640-018-0322-6/TABLES/2.

[20] H. Qazanfari, H. Hassanpour, and K. Qazanfari, “Content-Based Image Retrieval Using HSV Color Space Features,” Int. J. Comput. Inf. Eng., vol. 13, no. 10, pp. 537–545, 2019, doi: 10.5281/zenodo.3566277.

[21] T. Tiay, P. Benyaphaichit, and P. Riyamongkol, “Flower recognition system based on image processing,” in 2014 Third ICT International Student Project Conference (ICT-ISPC), Mar. 2014, pp. 99–102, doi: 10.1109/ICT-ISPC.2014.6923227.

[22] A. M. Al-Hetar, M. A. Rassam, O. Shormani, A. S. A. Salem, and H. Al-Yousofi, “Color-based Object Categorization Model Using Fuzzy HSV Inference System,” in 2019 First International Conference of Intelligent Computing and Engineering (ICOICE), Dec. 2019, pp. 1–6, doi: 10.1109/ICOICE48418.2019.9035173.

[23] S. Talla, P. Venigalla, A. Shaik, and M. Vuyyuru, “Multiclass Classification Using Random Forest Classifier,” Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol., vol. 5, no. 2, pp. 493–496, Mar. 2019, doi: 10.32628/CSEIT183821.

[24] M. A. El-Shorbagy and A. E. Hassanien, “Particle Swarm Optimization from Theory to Applications,” Int. J. Rough Sets Data Anal., vol. 5, no. 2, pp. 1–24, Apr. 2018, doi: 10.4018/IJRSDA.2018040101.

[25] D. K. Jain et al., “An approach for hyperspectral image classification by optimizing SVM using self organizing map,” J. Comput. Sci., vol. 25, pp. 252–259, Mar. 2018, doi: 10.1016/j.jocs.2017.07.016.

[26] M. Aminul Islam, M. Sayeed Iftekhar Yousuf, and M. M. Billah, “Automatic Plant Detection Using HOG and LBP Features With SVM,” Int. J. Comput., Accessed: Jan. 04, 2023. [Online]. Available:

[27] G. Zeng, “On the confusion matrix in credit scoring and its analytical properties,” vol. 49, no. 9, pp. 2080–2093, May 2019, doi: 10.1080/03610926.2019.1568485.

[28] M. Tripathi, “Analysis of Convolutional Neural Network based Image Classification Techniques,” J. Innov. Image Process., vol. 3, no. 2, pp. 100–117, Jun. 2021, doi: 10.36548/jiip.2021.2.003.

[29] D. Krstinić, M. Braović, L. Šerić, and D. Božić-Štulić, “Multi-label Classifier Performance Evaluation with Confusion Matrix,” in Computer Science & Information Technology, Jun. 2020, pp. 01–14, doi: 10.5121/csit.2020.100801.

[30] G. N. Kouziokas, “SVM kernel based on particle swarm optimized vector and Bayesian optimized SVM in atmospheric particulate matter forecasting,” Appl. Soft Comput., vol. 93, p. 106410, Aug. 2020, doi: 10.1016/j.asoc.2020.106410.

[31] A. Khan, H. Hizam, N. I. bin Abdul Wahab, and M. Lutfi Othman, “Optimal power flow using hybrid firefly and particle swarm optimization algorithm,” PLoS One, vol. 15, no. 8, p. e0235668, Aug. 2020, doi: 10.1371/journal.pone.0235668.

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