Submit Your Paper
  1. Home
  2. Journals
  3. Fusion: Practice and Applications

Fusion: Practice and Applications

Journal DOI

https://doi.org/10.54216/FPA

Submit Your Paper

2692-4048ISSN (Online) 2770-0070ISSN (Print)
Submit Your Paper

Journal Menu

Homepage Journal Overview Editorial Board Submitting Articles Indexing & Abstracting

Journal Volumes

Fusion: Practice and Applications

Volume 18 , Issue 2 , PP: 55-65, 2025 | Cite this article as | XML | Html | PDF | Full Length Article

An Intelligent Model to combat Soybean Plant Disease based on Random Forest and Support Vector Machine Algorithms

Zainab A. Abdulazeez 1 * , Israa Abdulkadhim Jabbar Al Ali 2 , Basma Mustafa M. H. 3 , Ghada Kamil Mustafa 4 , Refed Adnan Jaleel 5

  • 1 College Of Education For Human Sciences, University of Kerbala, Kerbala, Iraq - (zainab.abdulhameed@uokerbala.edu.iq)
  • 2 College Of Education For Human Sciences, University of Kerbala, Kerbala, Iraq - (israa.jabbar@uokerbala.edu.iq)
  • 3 College of Science, University of Kerbala, Kerbala, Iraq - (basma.m@uokerbala.edu.iq)
  • 4 College Of Education For Human Sciences, University of Kerbala, Kerbala, Iraq - (ghadakamil.87@gmail.com)
  • 5 Department of Information And Communication Engineering Al-Nahrain Universit, Baghdad, Iraq; Commission of Rightness for Persons with Disabilities and Special Needs, Ministry of Labor and Social Affairs, Baghdad, Iraq - (iraq_it_2010@yahoo.com)

    • Doi: https://doi.org/10.54216/FPA.180205

    Received: July 20, 2024 Revised: November 02, 2024 Accepted: January 14, 2025
    Abstract

    Given that plant disease is the primary factor contributing to damage in most plants, decision makers in the agriculture industry are highly interested in enhancing prediction strategies to detect illness in plants at an early stage. This is crucial for ensuring timely and effective plant care. Classifying healthy soybean plants is a dependable and efficient use of noninvasive techniques like machine learning (ML). In this work, we used ML to enhance a smart forecasting model for the prediction of soybean diseases. We utilized two feature selection techniques, namely gain ratio and correlation, two supervised ML algorithms (support vector machine and Random forest) and the cross-validation technique was used for assessing the proposed system, such as accuracy, F-measure, specificity, executing time, and sensitivity. The suggested technique can readily differentiate between soybean plants that are infected and those that are healthy. The suggested approach has undergone testing using a comprehensive collection of soybean characteristics, as well as a subset of attributes. The findings show that performance metrics are impacted when soybean traits are reduced.

    Keywords :

    Support Vector Machine , Random Forest , Plant Disease , Soybean , Feature Selection  ,

    References

    [1] A. M. Abdu, M. M. M. Mokji, and U. U. U. Sheikh, “Machine learning for plant disease detection: an investigative comparison between support vector machine and deep learning,” IAES Int. J. Artif. Intell., vol. 9, no. 4, pp. 670–683, Dec. 2020, doi: 10.11591/ijai.v9.i4.pp670-683.

    [2] S. B. Jadhav, “Convolutional neural networks for leaf image-based plant disease classification,” IAES Int. J. Artif. Intell., vol. 8, no. 4, pp. 328–341, Dec. 2019, doi: 10.11591/ijai.v8.i4.pp328-341.

    [3] M. A. I. Aquil and W. H. W. Ishak, “Evaluation of scratch and pre-trained convolutional neural networks for the classification of Tomato plant diseases,” IAES Int. J. Artif. Intell., vol. 10, no. 2, pp. 467–475, Jun. 2021, doi: 10.11591/ijai.v10.i2.pp467-475.

    [4] T. U. Rehman, M. S. Mahmud, Y. K. Chang, J. Jin, and J. Shin, “Current and future applications of statistical machine learning algorithms for agricultural machine vision systems,” Comput. Electron. Agric., vol. 156, pp. 585–605, Jan. 2019, doi: 10.1016/j.compag.2018.12.006.

    [5] G. Prem, M. Hema, L. Basava, and A. Mathur, “Plant disease prediction using machine learning algorithms,” Int. J. Comput. Appl., vol. 182, no. 25, pp. 1–7, Nov. 2018, doi: 10.5120/ijca2018918049.

    [6] E. Fujita, Y. Kawasaki, H. Uga, S. Kagiwada, and H. Iyatomi, “Basic investigation on a robust and practical plant diagnostic system,” in 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), Dec. 2016, pp. 989–992, doi: 10.1109/ICMLA.2016.0178.

    [7] E. Khalili, S. Kouchaki, S. Ramazi, and F. Ghanati, “Machine learning techniques for soybean charcoal rot disease prediction,” Front. Plant Sci., vol. 11, Dec. 2020, doi: 10.3389/fpls.2020.590529.

    [8] M. G. Roth et al., “Integrated management of important soybean pathogens of the United States in changing climate,” J. Integr. Pest Manag., vol. 11, no. 1, Jan. 2020, doi: 10.1093/jipm/pmaa013.

    [9] G. Kaushal and R. Bala, “GLCM and KNN based algorithm for plant disease detection,” Int. J. Adv. Res. Electr. Electron. Instrum. Eng., vol. 6, no. 7, pp. 5845–5852, 2017, doi: 10.15662/IJAREEIE.2017.0607036.

    [10] R. M. Prakash, G. P. Saraswathy, G. Ramalakshmi, K. H. Mangaleswari, and T. Kaviya, “Detection of leaf diseases and classification using digital image processing,” in 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Mar. 2017, pp. 1–4, doi: 10.1109/ICIIECS.2017.8275915.

    [11] S. Bhuvana, B. K. Bharati, P. Kousiga, and S. R. Selvi, “Leaf disease detection using clustering optimization and multi-class classifier,” WSEAS Trans. Comput. Arch., vol. 17, 2018.

    [12] V. V. Adit, C. V. Rubesh, S. S. Bharathi, G. Santhiya, and R. Anuradha, “A Comparison of Deep Learning Algorithms for Plant Disease Classification,” Adv. Cybern. Cogn. Mach. Learn. Commun. Technol. Lect. Notes Electr. Eng., vol. 643, pp. 153–161, 2020.

    [13] R. U. Khan, K. Khan, W. Albattah, and A. M. Qamar, “Image-based detection of plant diseases: from classical machine learning to deep learning journey,” Wirel. Commun. Mob. Comput., vol. 2021, pp. 1–13, Jun. 2021, doi: 10.1155/2021/5541859.

    [14] G. Geetha, S. Samundeswari, G. Saranya, K. Meenakshi, and M. Nithya, “Plant leaf disease classification and detection system using machine learning,” J. Phys. Conf. Ser., vol. 1712, no. 1, p. 12012, Dec. 2020, doi: 10.1088/1742-6596/1712/1/012012.

    [15] K. S. Sankaran, N. Vasudevan, and V. Nagarajan, “Plant disease detection and recognition using k means clustering,” in 2020 International Conference on Communication and Signal Processing (ICCSP), Jul. 2020, pp. 1406–1409, doi: 10.1109/ICCSP48568.2020.9182095.

    [16] M. A. Hussein, L. E. Kadhim, and A. A. Abdulameer, “Optimum placement of nodes in networks of wireless sensors,” AIP Conf. Proc., vol. 2591, Mar. 2023, doi: 10.1063/5.0119640.

    [17] A. Yousuf and U. Khan, “Ensemble classifier for plant disease detection,” Int. J. Comput. Sci. Mob. Comput., vol. 10, no. 1, pp. 14–22, Jan. 2021, doi: 10.47760/ijcsmc.2021.v10i01.003.

    [18] M. Morgan, C. Blank, and R. Seetan, “Plant disease prediction using classification algorithms,” IAES Int. J. Artif. Intell., vol. 10, no. 1, pp. 257–264, Mar. 2021, doi: 10.11591/ijai.v10.i1.pp257-264.

    [19] A. A. Abdulameer and R. K. Oubida, “The impact of IoT on real-world future decisions,” AIP Conf. Proc., vol. 2591, Mar. 2023, doi: 10.1063/5.0119572.

    [20] R. S. Michalski and R. L. Chilausky, “Learning by being told and learning from examples: an experimental comparison of the two methods of knowledge acquisition in the context of developing an expert system for soybean disease diagnosis,” Int. J. Policy Anal. Inf. Syst., vol. 4, no. 2, 1980.

    [21] A. S. Ahmed, Z. K. Obeas, B. A. Alhade, and R. A. Jaleel, “Improving prediction of plant disease using k-efficient clustering and classification algorithms,” IAES Int. J. Artif. Intell., vol. 11, no. 3, pp. 939–948, Sep. 2022, doi: 10.11591/ijai.v11.i3.pp939-948.

    [22] P. G. Krishna, S. A. Ahamed, K. S. R. Anjaneyulu, and K. S. R. Anjaneyulu, “Artificial Neural Network (ANN) Enabled Weather Monitoring and Prediction System using IoT,” in Proc. 2023 Int. Conf. Intell. Data Commun. Technol. Internet Things (IDCIoT), Jan. 2023, pp. 1–5, doi: 10.1109/IDCIoT56793.2023.10053534.

    Cite This Article As :
    A., Zainab. , Abdulkadhim, Israa. , Mustafa, Basma. , Kamil, Ghada. , Adnan, Refed. An Intelligent Model to combat Soybean Plant Disease based on Random Forest and Support Vector Machine Algorithms. Fusion: Practice and Applications, vol. , no. , 2025, pp. 55-65. DOI: https://doi.org/10.54216/FPA.180205
    A., Z. Abdulkadhim, I. Mustafa, B. Kamil, G. Adnan, R. (2025). An Intelligent Model to combat Soybean Plant Disease based on Random Forest and Support Vector Machine Algorithms. Fusion: Practice and Applications, (), 55-65. DOI: https://doi.org/10.54216/FPA.180205
    A., Zainab. Abdulkadhim, Israa. Mustafa, Basma. Kamil, Ghada. Adnan, Refed. An Intelligent Model to combat Soybean Plant Disease based on Random Forest and Support Vector Machine Algorithms. Fusion: Practice and Applications , no. (2025): 55-65. DOI: https://doi.org/10.54216/FPA.180205
    A., Z. , Abdulkadhim, I. , Mustafa, B. , Kamil, G. , Adnan, R. (2025) . An Intelligent Model to combat Soybean Plant Disease based on Random Forest and Support Vector Machine Algorithms. Fusion: Practice and Applications , () , 55-65 . DOI: https://doi.org/10.54216/FPA.180205
    A. Z. , Abdulkadhim I. , Mustafa B. , Kamil G. , Adnan R. [2025]. An Intelligent Model to combat Soybean Plant Disease based on Random Forest and Support Vector Machine Algorithms. Fusion: Practice and Applications. (): 55-65. DOI: https://doi.org/10.54216/FPA.180205
    A., Z. Abdulkadhim, I. Mustafa, B. Kamil, G. Adnan, R. "An Intelligent Model to combat Soybean Plant Disease based on Random Forest and Support Vector Machine Algorithms," Fusion: Practice and Applications, vol. , no. , pp. 55-65, 2025. DOI: https://doi.org/10.54216/FPA.180205

    Article Statistics

    View
    55
    Download
    23

    Download