Crop Yield Prediction: Data Structure and Ai-Powered Methods

Abstract

Smart farming, also known as intelligent agriculture, represents a modern stage in the development of agricultural science and practice. Its defining feature lies in the active application of artificial intelligence methods, particularly machine learning and deep learning, to address specific tasks aimed at ensuring sustainable crop production. (Research purpose) The aim of this study is to analyze data structures and compare machine learning and deep learning algorithms used in used in crop yield prediction. (Materials and methods) Using a convergent approach and applying methods of cognitive and semantic analysis, the authors examined the subject area of artificial intelligence applications in crop yield prediction. The study also explores key aspects related to the structure of input data, the main stages of implementing predictive models, and the most widely used machine learning and deep learning methods. (Results and discussion) The study presents the core data structure and methods for data acquisition, along with a typical workflow for implementing predictive analytics models for crop yield prediction. The most commonly used machine learning and deep learning methods are identified and their functional characteristics are examined in detail. Comparative analysis demonstrates that deep learning and hybrid approaches outperform traditional machine learning methods in terms of prediction accuracy, as measured by standard error metrics. (Conclusions) The findings confirm the advantages of deep learning methods (mean R² = 0.85) and hybrid approaches (mean R² = 0.87) in crop yield prediction under varying conditions and management interventions. Future research may focus on adapting modern AI approaches to spatial land use objects and crop types, with an emphasis on remote sensing data.

Keywords

• crop yield prediction
• smart farming
• artificial intelligence
• data structure
• machine learning
• deep learning