DEVELOPMENT OF A NEURAL NETWORK MODEL FOR TRAINING DATA ON THE EFFECTS OF PHOSPHORUS ON SPRING WHEAT GROWTH

Sharipova S. Аkanova A. Ospanova N.
2023 Technology Center

Eastern-European Journal of Enterprise Technologies
2023 #6 Issue 4(126)32 - 38 pp.

A neural network was developed to predict the effect of phosphorus on spring wheat yields. The focus is on the neural network, including its structure, parame-ters, training methods and results related to phosphorus effects on spring wheat yields. An algorithm for deve-loping a neural network model is also presented. The study was conducted to address the criti-cal need for developing a neural network to predict the effect of phosphorus on spring wheat yields in the Republic of Kazakhstan. For data analysis, input data were used that cover the period from 2012 to 2022, including climatic indi-cators, regional features and phosphorus application. The target variable is spring wheat yield. To ensure the accuracy of the study, the data were preprocessed and standardized, and an outlier and variance analysis was performed. The developed neural network was trained and tested to obtain the best results. The mean squared error was used as a metric for evaluating the quality of forecasting. Additionally, indicators such as mean absolute error and coefficient of determination were considered. The results of the study showed an MSE of 7.12, indicating that the model agrees well with the data and makes accurate predictions, which also suggests its practical relevance. The correlation analysis of the fea-tures showed that phosphorus application and spring wheat yield have a positive relationship. These results can be very useful for agriculture and farming enter-prises, as they allow optimizing phosphorus application to the soil and increasing wheat yields

neural network forecasting model , neural networks , phosphorus data , yield forecasting

Text of the article Перейти на текст статьи

Candidate of Pedagogical Sciences, Department of Computer Science, Toraighyrov University, Lomov str. 64, Pavlodar, 140008, Kazakhstan
Department of Computer Engineering and Software, S. Seifullin Kazakh Agro Technical Research, University Zhenis, ave. 62, Astana, 010011, Kazakhstan

Candidate of Pedagogical Sciences
Department of Computer Engineering and Software

10 лет помогаем публиковать статьи Международный издатель

Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026