METHODS FOR PREPARING A DATA SET FOR DETERMINING SOIL FERTILITY BASED ON ARTIFICIAL INTELLIGENCE MODELS

Fayzullo Nazarov; Abror Akhatov

Авторы

Fayzullo Nazarov Samarkand State University named after Sharof Rashidov
Abror Akhatov Samarkand State University named after Sharof Rashidov

Ключевые слова:

Soil fertility, machine learning, intelligent analysis, logistic regression, salinity level, data preparation, standardization, agriculture, climate change, degradation, food security

Аннотация

This research explores methods for preparing datasets to assess soil fertility using artificial intelligence models. The study analyzes the key characteristics determining soil fertility and the factors influencing it for artificial intelligence applications. The process of preparing a data set based on soil samples taken from grain and vineyards in Fergana district of Fergana region was thoroughly reviewed. Using the logistic regression algorithm, this dataset helped identify the soil salinity level as a major factor affecting fertility. The model’s accuracy was evaluated using standard techniques. The paper highlights the significance of employing artificial intelligence technologies in determining and managing soil fertility.

Библиографические ссылки

S.Pardaev, T.Ortikov. Ways to improve the humus status of different types of soils in Uzbekistan. ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY. 2019, 257.

Sh.Kholikulov, T.Goziev. Change of Temperature and Humidity of soil at its Regulation. 20th World Congress of Soil Science. Jeju, Southth Korea-2014

F.Khashimov, O.Tashkenbaev, M.Khayitov. The Interdependence of the Humus State and the Productivity of Individual Crops on Irrigated Soils. GLOBAL FOOD FORUM—2021, 33

McBratney, A., Field, D. J., & Koch, A. The Dimensions of Soil Security. Geoderma-2014, 213, 203-213.

Pretty, J., Benton, T. G., Bharucha, Z. P., et al. Global Assessment of Agricultural System Redesign for Sustainable Intensification. Nature Sustainability-2018, 1(8), 441-446.

Xu, J. M., Tang, C., & Chen, Z. L. Soil Microbial Biomass and Activity in Relation to Soil Fertility. Soil Biology and Biochemistry-2018, 123, 1-10.

Inubushi, K., Hadi, A., & Tsuruta, H. Soil Carbon Dynamics in Paddy Fields. Soil Science and Plant Nutrition-2016, 62(5-6), 456-463).

Jordan, M. I., & Mitchell, T. M.. Machine Learning: Trends, Perspectives, and Prospects. Science-2015, 349(6245), 255-260.

https://www.tutorialspoint.com/machine_learning/machine_learning_preparing_data.html

Karimova M. X, Nuraliyeva F. A, To‘rayev R. N, Abdumo‘minov B.S. Mashinali o‘qitish yordamida maktab bitiruvchilarining kasb tanlash jarayonini optimallashtirish. Inter education & global study. ISSN 2992-9024 /2025

https://www.geeksforgeeks.org/feature-selection-techniques-in-machine-learning

Ndung‘u Rachael Njeri - Data Preparation for Machine Learning Modelling, International Journal of Computer Applications Technology and Research Volume 11–Issue 06, 231-235, 2022, ISSN:-2319–8656.

Guyon, I., & Elisseeff, A., 2003. An Introduction to Variable and Feature Selection. Journal of Machine Learning Research, 3, pp. 1157–1182.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Duchesnay, E. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research-2011. 12, 2825-2830

Nazarov, F. M., & Yarmatov, S. Optimization of Prediction Results Based on Ensemble Methods of Machine Learning. International Russian Smart Industry Conference-2023, Proceedings, 45-52.