Fuzzy Technology Design for Early Detection of Diseases in Tobacco Plants


  • Saiful Anwar Politeknik Negeri Jember
  • Ziani Said Mohammed V University, Agdal, Marocco
  • Muhammad Imam Dinata Universitas Bumigora Mataram




expert system, fuzzy expert system, mamdani, tobacco disease


Tobacco is an agricultural product that uses leaves to be processed into pesticides, medicines and cigarettes. Tobacco quality is determined by plant maintenance and reduced pest and disease attacks. To avoid these disturbances, control is needed quickly, precisely and accurately so that the tobacco plant disease cannot spread throughout agricultural land. In making fuzzy, diseases and symptoms in tobacco plants are used as a rule base in making a fuzzy expert system. The expert system created in this research is an expert system using the concept of fuzzy logic to diagnose tobacco plant diseases, using the Mamdani inference method and the defuzzification process using the centroid method (firmness value) to get the right conclusions in diagnosing tobacco plant diseases. From the results of Mamdani's design and manual fuzzy calculations, it can be concluded that the design is ready to be further implemented into the required programming language. From the sample calculation results, it was found that damping off disease has a moderate degree of risk with a value of 41.54. With the construction of this system, it will provide easy information for farmers to carry out and find out what symptoms are contracting diseases in tobacco plants.


F. K. Neighbor, P. Tembakau, and F. K. Neighbor, “Jurnal Smart Teknologi Diagnosa Penyakit Pada Tanaman Tembakau Di Kabupaten Jember Menggunakan Metode Fuzzy K-Nearst Neighbor Diagnosis Of Disease In Tobacco Plant In Jember Regency Using Fuzzy K-Nearst Neighbor Method Jurnal Smart Teknologi,” vol. 3, no. 6, pp. 646–651, 2022.

A. Pakniyat, R. Hosseini, and M. Mazinai, “A Fuzzy Expert System for Star Classification Based on Photometry,” Int. J. Fuzzy Syst. Appl., vol. 5, no. 3, pp. 109–119, 2016, doi: 10.4018/ijfsa.2016070106.

P. Liu, “Mamdani fuzzy system: universal approximator to a class of random processes,” IEEE Trans. Fuzzy Syst., vol. 10, no. 6, pp. 756–766, 2002, doi: 10.1109/tfuzz.2002.805890.

W. Ben Tagherouit, S. Bennis, and J. Bengassem, “A Fuzzy Expert System for Prioritizing Rehabilitation of Sewer Networks,” Comput. Civ. Infrastruct. Eng., vol. 26, no. 2, pp. 146–152, Feb. 2011, doi: 10.1111/j.1467-8667.2010.00673.x.

Z. Hu, “A fuzzy expert system for site characterization,” Expert Syst. Appl., vol. 24, no. 1, pp. 123–131, 2003, doi: 10.1016/s0957-4174(02)00090-8.

Mamdani, “Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis,” IEEE Trans. Comput., no. 12, pp. 1182–1191, 1977, doi: 10.1109/tc.1977.1674779.

M. Abdulgader and D. Kaur, “Evolving Mamdani Fuzzy Rules Using Swarm Algorithms for Accurate Data Classification,” IEEE Access, vol. 7, pp. 175907–175916, 2019, doi: 10.1109/access.2019.2957735.

M. Yunus and M. R. T. Akbar, “Penerapan Algoritma Fuzzy Tahani Untuk Rekomendasi Penerima Beasiswa Peningkatan Prestasi Akademik,” JTIM J. Teknol. Inf. dan Multimed., vol. 3, no. 2 SE-Articles, Aug. 2021, doi: 10.35746/jtim.v3i2.161.

Q. A. Nezhad, J. P. Zand, and S. S. Hoseini, “An Investigation on Fuzzy Logic Controllers (Takagi-Sugeno & Mamdani) in Inverse Pendulum System,” Int. J. Fuzzy Log. Syst., vol. 3, no. 3, pp. 1–14, 2013, doi: 10.5121/ijfls.2013.3301.

H. Zhou and H. Ying, “A Method for Deriving the Analytical Structure of a Broad Class of Typical Interval Type-2 Mamdani Fuzzy Controllers,” IEEE Trans. Fuzzy Syst., vol. 21, no. 3, pp. 447–458, 2013, doi: 10.1109/tfuzz.2012.2226891.

A. Nowé, “Sugeno, Mamdani, and fuzzy Mamdani controllers put in a uniform interpolation framework,” Int. J. Intell. Syst., vol. 13, no. 23, pp. 243–256, 1998, doi: 10.1002/(sici)1098-111x(199802/03)13:2/3<243::aid-int8>3.3.co;2-n.

H. Wu and J. M. Mendel, “On Choosing Models for Linguistic Connector Words for Mamdani Fuzzy Logic Systems,” IEEE Trans. Fuzzy Syst., vol. 12, no. 1, pp. 29–44, 2004, doi: 10.1109/tfuzz.2003.822675.

Y. Chen, F. Long, W. Kuang, and T. Zhang, “A method for predicting blast-induced ground vibration based on Mamdani Fuzzy Inference System,” J. Intell. & Fuzzy Syst., vol. 44, no. 5, pp. 7513–7522, 2023, doi: 10.3233/jifs-223195.

A. Gegov, D. Sanders, and B. Vatchova, “Mamdani fuzzy networks with feedforward rule bases for complex systems modelling,” J. Intell. & Fuzzy Syst., vol. 30, no. 5, pp. 2623–2637, 2016, doi: 10.3233/ifs-151911.

L. Magdalena and E. Trillas, “Abe Mamdani, in Memoriam,” Fuzzy Sets Syst., vol. 161, no. 23, pp. 2975–2977, 2010, doi: 10.1016/j.fss.2010.08.001.




How to Cite

Anwar, S., Said, Z., & Dinata, M. I. (2023). Fuzzy Technology Design for Early Detection of Diseases in Tobacco Plants. International Journal of Health and Information System, 1(1), 27–39. https://doi.org/10.47134/ijhis.v1i1.8