Abstract:
In the current context, optimizing the utilization of agricultural land
resources is increasingly vital for production intensification. This study
presents a methodological approach employing numerical methods and
machine learning algorithms to analyze and forecast land use optimality. The
objective is to develop effective models and tools facilitating rational and
sustainable agricultural land resource management, ultimately enhancing
productivity and economic efficiency. The research employs data
dimensionality reduction techniques such as principal component analysis
and factor analysis (FA) to extract key factors from multidimensional land
data. The simplex method is utilized to optimize resource allocation among
crops while considering constraints. Machine learning algorithms including
extreme gradient boosting (XGBoost), support vector machine (SVM), and
light gradient boosting machine (LightGBM) are employed to predict
optimal land use and yield with high accuracy and efficiency. Analysis
reveals significant differences in model performance, with LightGBM
achieving the highest accuracy of 99.98%, followed by XGBoost at 95.99%,
and SVM at 43.65%. These findings underscore the importance of selecting
appropriate algorithms for agronomic data tasks. The study's outcomes offer
valuable insights for formulating agricultural practice recommendations and
land management strategies, integrable into decision support systems for the
agricultural sector, thereby enhancing productivity and production efficiency.
