Intelligent Crop Growth Prediction Using Artificial Intelligence and Environmental Data

Abstract

The increasing complexity of crop growth dynamics under variable environmental conditions has necessitated advanced predictive approaches for accurate and timely decision-making in agriculture. This study presents an intelligent crop growth prediction framework that integrates artificial intelligence with environmental data to enhance forecasting accuracy and support precision farming practices. 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, Gradient Boosting, Support Vector Machines, 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 zones and crop types. Experimental results demonstrate that the proposed approach significantly improves prediction accuracy and outperforms conventional empirical and statistical models. 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 prediction systems offer a scalable, efficient, and data-driven solution for enhancing crop productivity, optimizing resource utilization, and promoting sustainable agricultural development.