Support vector machine with a firefly optimization algorithm for classification of apple fruit disease

  • Wikky Fawwaz Al Maki Universitas Telkom
  • Amien Jafar Makrufi Universitas Telkom
Keywords: Apple Disease, Classification, Support Vector Machine, Firefly Algorithm


Fruit diseases became one of the serious problems that the farmer faced because it could threaten their economic outcome. The main focus of this study is apples. Apple fruit is very susceptible to disease, in general diseases that usually attack the apple are blotch apple, rot apple, and scab apple. In this study, the author is classifying these three apple diseases and normal apples. Classification is a process that we can do manually by human power, which costs a lot of fortune, takes a long time, and it's also very vulnerable to false identification. This study takes advantage of computer vision technology and machine learning to overcome the classification problem. By using the SVM method and parameter FA optimization algorithm, we can get the highest result only in the first experiment and also with 94% accuracy.


Download data is not yet available.


[1] S. Musacchi and S. Serra, “Apple fruit quality: Overview on pre-harvest factors,” Sci Hortic, vol. 234, pp. 409–430, Apr. 2018.
[2] R. H. Raheema, “Apples and Health,” researchgate, 2020.
[3] M. A. Acquavia et al., “Analytical Methods for Extraction and Identification of Primary and Secondary Metabolites of Apple (Malus domestica) Fruits: A Review,” Separations 2021, Vol. 8, Page 91, vol. 8, no. 7, p. 91, Jun. 2021.
[4] A. A. Bracino, R. S. Concepcion, R. A. R. Bedruz, E. P. Dadios, and R. R. P. Vicerra, “Development of a Hybrid Machine Learning Model for Apple (Malus domestica) Health Detection and Disease Classification,” in 2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), Dec. 2020, pp. 1–6.
[5] Y. Huang, Y. Yang, Y. Sun, H. Zhou, and K. Chen, “Identification of Apple Varieties Using a Multichannel Hyperspectral Imaging System,” Sensors 2020, Vol. 20, Page 5120, vol. 20, no. 18, p. 5120, Sep. 2020.
[6] P. K. Sujatha, J. Sandhya, J. S. Chaitanya, and R. Subashini, “Enhancement Of Segmentation And Feature Fusion For Apple Disease Classification,” in 2018 Tenth International Conference on Advanced Computing (ICoAC), Dec. 2018, pp. 175–181.
[7] Sumanto, Y. Sugiarti, A. Supriyatna, I. Carolina, R. Amin, and A. Yani, “Model Naïve Bayes Classifiers For Detection Apple Diseases,” in 2021 9th International Conference on Cyber and IT Service Management (CITSM), Sep. 2021, pp. 1–4.
[8] A. G. Alharbi and M. Arif, “Detection And Classification Of Apple Diseases using Convolutional Neural Networks,” in 2020 2nd International Conference on Computer and Information Sciences (ICCIS), Oct. 2020, pp. 1–6.
[9] D. A. Pisner and D. M. Schnyer, “Support vector machine,” Machine Learning: Methods and Applications to Brain Disorders, pp. 101–121, Jan. 2020.
[10] I. Ahmad, M. Basheri, M. J. Iqbal, and A. Rahim, “Performance Comparison of Support Vector Machine, Random Forest, and Extreme Learning Machine for Intrusion Detection,” IEEE Access, vol. 6, pp. 33789–33795, May 2018.
[11] S. Ghosh, A. Dasgupta, and A. Swetapadma, “A Study on Support Vector Machine based Linear and Non-Linear Pattern Classification,” in 2019 International Conference on Intelligent Sustainable Systems (ICISS), Feb. 2019, pp. 24–28.
[12] J. Cervantes, F. Garcia-Lamont, L. Rodríguez-Mazahua, and A. Lopez, “A comprehensive survey on support vector machine classification: Applications, challenges and trends,” Neurocomputing, vol. 408, pp. 189–215, Sep. 2020.
[13] P. Tao, Z. Sun, and Z. Sun, “An Improved Intrusion Detection Algorithm Based on GA and SVM,” IEEE Access, vol. 6, pp. 13624–13631, Mar. 2018.
[14] F. Maulidina, Z. Rustam, and J. Pandelaki, “Lung Cancer Classification using Support Vector Machine and Hybrid Particle Swarm Optimization-Genetic Algorithm,” in 2021 International Conference on Decision Aid Sciences and Application (DASA), Dec. 2021, pp. 751–755.
[15] U. Shruthi, V. Nagaveni, and B. K. Raghavendra, “A Review on Machine Learning Classification Techniques for Plant Disease Detection,” in 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), Mar. 2019, pp. 281–284.
[16] M. Bhagat, D. Kumar, I. Haque, H. S. Munda, and R. Bhagat, “Plant Leaf Disease Classification Using Grid Search Based SVM,” in 2nd International Conference on Data, Engineering and Applications (IDEA), Feb. 2020, pp. 1–6.
[17] H. Kibriya, I. Abdullah, and A. Nasrullah, “Plant Disease Identification and Classification Using Convolutional Neural Network and SVM,” in 2021 International Conference on Frontiers of Information Technology (FIT), Dec. 2021, pp. 264–268.
[18] A. Aruraj, A. Alex, M. S. P. Subathra, N. J. Sairamya, S. T. George, and S. E. V. Ewards, “Detection and Classification of Diseases of Banana Plant Using Local Binary Pattern and Support Vector Machine,” in 2019 2nd International Conference on Signal Processing and Communication (ICSPC), Mar. 2019, pp. 231–235.
[19] P. Kumar Sethy, A. Rath, and N. Kanta Barpanda, “Detection & Identification of Rice Leaf Diseases using Multiclass SVM and Particle Swarm Optimization Technique Special Session : Smart City with Emerging Technologies View project Agricultural Image Processing View project,” International Journal of Innovative Technology and Exploring Engineering (IJITEE), pp. 2278–3075, 2019, Accessed: Aug. 08, 2022. [Online]. Available:
[20] K. R. Aravind, P. Raja, K. V. Mukesh, R. Aniirudh, R. Ashiwin, and C. Szczepanski, “Disease classification in maize crop using bag of features and multiclass support vector machine,” in 2018 2nd International Conference on Inventive Systems and Control (ICISC), Jan. 2018, pp. 1191–1196.
[21] H. Inoue, “Data Augmentation by Pairing Samples for Images Classification,” Jan. 2018, doi: 10.48550/arxiv.1801.02929.
[22] A. Hilal, I. Arai, and S. El-Tawab, “DataLoc+: A Data Augmentation Technique for Machine Learning in Room-Level Indoor Localization,” in 2021 IEEE Wireless Communications and Networking Conference (WCNC), Mar. 2021, vol. 2021-March, pp. 1–7.
[23] W. K. Mutlag, S. K. Ali, Z. M. Aydam, and B. H. Taher, “Feature Extraction Methods: A Review,” J Phys Conf Ser, vol. 1591, no. 1, p. 012028, Jul. 2020.
[24] A. O. Salau and S. Jain, “Feature Extraction: A Survey of the Types, Techniques, Applications,” in 2019 International Conference on Signal Processing and Communication (ICSC), Mar. 2019, pp. 158–164.
[25] S. X. Wu, H.-T. Wai, L. Li, and A. Scaglione, “A Review of Distributed Algorithms for Principal Component Analysis,” Proceedings of the IEEE, vol. 106, no. 8, pp. 1321–1340, Aug. 2018.
[26] D. A. Pisner and D. M. Schnyer, “Support vector machine,” in Machine Learning, Elsevier, 2020, pp. 101–121.
[27] M. Sheykhmousa, M. Mahdianpari, H. Ghanbari, F. Mohammadimanesh, P. Ghamisi, and S. Homayouni, “Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review,” IEEE J Sel Top Appl Earth Obs Remote Sens, vol. 13, pp. 6308–6325, 2020.
[28] X.-S. Yang and A. Slowik, “Firefly Algorithm,” Swarm Intelligence Algorithms, pp. 163–174, Aug. 2020.
[29] X. S. Yang and X. S. He, “Why the firefly algorithm works?,” Studies in Computational Intelligence, vol. 744, pp. 245–259, 2018.
[30] S. L. Tilahun, J. M. T. Ngnotchouye, and N. N. Hamadneh, “Continuous versions of firefly algorithm: a review,” Artificial Intelligence Review 2017 51:3, vol. 51, no. 3, pp. 445–492, Jul. 2017.
How to Cite
Fawwaz Al Maki, W., & Jafar Makrufi, A. (2022). Support vector machine with a firefly optimization algorithm for classification of apple fruit disease. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 22(1), 177-188.