Intelligent Crop Growth Modeling Using Artificial Intelligence and Environmental Data

Abstract

The increasing complexity of crop growth processes under variable environmental conditions has necessitated advanced modeling approaches for accurate prediction and management in modern agriculture. This study presents an intelligent crop growth modeling framework that integrates artificial intelligence with environmental data to enhance the understanding and forecasting of crop development dynamics. The proposed methodology utilizes multi-source datasets, including temperature, rainfall, humidity, solar radiation, soil moisture, nutrient availability, and historical crop growth 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, Support Vector Machines, Gradient Boosting, and Long Short-Term Memory networks, are employed to capture complex nonlinear relationships and temporal dependencies influencing crop growth patterns. The system is designed with real-time data acquisition and adaptive learning capabilities, enabling continuous monitoring and dynamic prediction under changing environmental conditions. The models are trained and validated using region-specific datasets to ensure robustness across diverse agro-climatic conditions and crop types. Experimental results demonstrate that the proposed approach significantly improves the accuracy of crop growth modeling and outperforms traditional empirical and statistical methods. The framework provides actionable insights for optimizing agricultural practices such as irrigation scheduling, nutrient management, and crop planning. The findings highlight the effectiveness of integrating artificial intelligence with environmental data for precision agriculture applications. The study concludes that intelligent crop growth modeling systems offer a scalable, efficient, and data-driven solution for enhancing crop productivity, optimizing resource utilization, and promoting sustainable agricultural development.