Intelligent Crop Productivity Analysis Using Artificial Intelligence and Data Analytics

Abstract

The increasing demand for higher agricultural output under constraints of climate variability, resource limitations, and environmental sustainability has necessitated advanced analytical approaches for understanding crop productivity patterns. This study presents an intelligent crop productivity analysis framework that integrates artificial intelligence with data analytics to evaluate and enhance agricultural performance. The proposed methodology utilizes multi-source datasets, including soil properties, climatic variables, crop health indicators, irrigation practices, and historical yield records. These data are preprocessed through cleaning, normalization, and feature engineering techniques to ensure consistency and reliability. Advanced machine learning and deep learning models, such as Random Forest, Gradient Boosting, Support Vector Machines, and Artificial Neural Networks, are employed to capture complex nonlinear relationships and identify key factors influencing crop productivity. The system is designed with real-time data processing and adaptive learning capabilities, enabling continuous analysis and dynamic insights under changing environmental conditions. The models are trained and validated using region-specific datasets to ensure robustness across diverse agro-climatic zones and crop types. Experimental results demonstrate that the proposed approach significantly improves the accuracy of productivity analysis and outperforms conventional statistical methods. Additionally, the framework provides actionable insights for optimizing agricultural practices such as irrigation scheduling, nutrient management, and crop planning. The findings highlight the effectiveness of combining artificial intelligence with data analytics for precision agriculture applications. The study concludes that intelligent crop productivity analysis systems offer a scalable, efficient, and data-driven solution for enhancing yield performance, optimizing resource utilization, and promoting sustainable agricultural development.