NONE 20260722111103369 wseas/wseas content-registration-form@crossref.org content-registration-form EARTH SCIENCES AND HUMAN CONSTRUCTIONS 2944-9154 2944-9006 Fenni Mohamed Faculty of Snv Ufas Setif1 University Setif ALGERIA Allag Fateh Faculty of Snv Ufas Setif1 University Setif ALGERIA Harrag Abdelmalek Faculty of Snv Ufas Setif1 University Setif ALGERIA Bounechada Mustapha Faculty of Snv Ufas Setif1 University Setif ALGERIA The inflorescence rot (also known as bud rot) is a disease that affects many species of date palm. It is caused by a fungus called Mauginiella scaettae. Pathogenicity is based on various bioclimatic conditions, and these conditions vary greatly from year to year, thus affecting the amount of loss to harvest from the inflorescence rot disease. Quantifying and predicting the effects of many of these bioclimatic conditions is very complex if not impossible. Therefore, we believed it would be beneficial to analyze bioclimatic conditions and develop models to quantify and predict pathogenicity by utilizing several artificial intelligence methods, including fuzzy logic, which is ideal for analyzing uncertain environments. The fuzzy logic model consists of several input variables which characterize the factors influencing the development of inflorescence rot disease (i.e. temperature, rainfall, wind speed, humidity, and the corresponding season). The model will be capable of expanding to other additional factors not contained in this study, but which still have an impact on the degree of pathogenicity of the inflorescence rot disease. Once an appropriate rule base has been established based on actual observed conditions, the model will then be able to predict the rate of proliferation of the inflorescence rot disease as a function of using the input factors for estimating the potential proliferation of the disease affecting date palms. This method may help to reduce the risk of date palms becoming infected with inflorescence rot disease. 07 22 2026 07 22 2026 81 7 https://creativecommons.org/licenses/by/4.0/deed.en_US 10.37394/232024.2026.6.7 https://wseas.com/journals/eshc/2026/a14eshc-007(2026).pdf Abdullah, H., & Mahmud, N. (2018). Impact of fluorescent decay on date palm yield. Journal of Plant Pathology, 12(3), 45-52. Ahmed, S., & Musa, F. (2021). Unknown factors affecting fungal disease proliferation in date palms. Environmental Mycology, 14(2), 101-115. Al-Khayri, J. M., Jain, S. M., & Johnson, D. V. (2019). Date Palm Genetic Resources and Utilization. Springer. Ben Tahar, A., Bouzid, K., & Hanchi, B. (2017). Climatic conditions influencing Mauginiella scaettae development. International Journal of Agricultural Science, 9(5), 237-245. El-Morsy, A. M., Mahmoud, H. K., & Mansour, M. A. (2020). Fungal diseases of date palms in the Arabian Peninsula. Plant Pathology Journal, 36(1), 78-90. Hussain, M., Omar, A., & Saeed, M. (2016). Temperature effects on the emergence of date palm spathes and fungal infection. Plant Ecology, 9(4), 300-310. Khalil, M. A., Al-Qurainy, F., & Khan, S. (2019). Pathogenic mechanisms of Mauginiella scaettae. Mycological Research, 23(9), 556-568. Li, W., Gao, X., & Chen, Y. (2021). Applications of AI in agricultural disease management. Computers and Electronics in Agriculture, 187, 106281. Nguyen, T., & Tran, P. (2020). Challenges in numerical modeling for complex biological systems. Computational Biology Journal, 15(3), 159-171. Patel, R., & Kumar, S. (2017). Limitations of statistical significance in biological data interpretation. Bioinformatics Review, 7(1), 50-60. Ross, T. J. (2016). Fuzzy Logic with Engineering Applications. Wiley. Smith, A., & Jones, R. (2018). Mathematical modeling of fungal disease spread in plants. Mathematical Biosciences, 14(2), 98-110. Zadeh, L. A. (2017). Fuzzy sets and their applications to artificial intelligence. Artificial Intelligence Review, 20(2), 91-101.