Pattern Discovery in Crop Growth Data Using a Gradient-Based Ant Lion Optimization Model

Crop growth prediction plays a crucial role in precision agriculture, where accurate insights into crop behavior can significantly enhance yield and resource utilization. This paper presents a novel approach for pattern discovery in crop growth data using a Gradient-Based Ant Lion Optimization (GBALO) model. Traditional methods often struggle with high-dimensional agricultural datasets and lack the adaptability to select optimal features for prediction, resulting in poor model performance and low prediction accuracy. To overcome these challenges, the proposed GBALO framework integrates gradient-based learning into the Ant Lion Optimization algorithm for efficient feature selection and model parameter tuning. This hybrid model is further combined with predictive modeling techniques, such as Random Forest, to build an accurate and interpretable crop growth prediction system. The proposed method is applied to real-world paddy cultivation data, enabling effective identification of key factors influencing growth and yield. It not only enhances predictive accuracy but also aids farmers and researchers in making informed decisions based on discovered patterns. Experimental results demonstrate that the GBALO-based model outperforms existing approaches in terms of accuracy, feature relevance, and computation time, thus establishing a robust framework for intelligent agricultural analytics.

Keywords :

Crop Growth Prediction, Ant Lion Optimization, Feature Selection, Gradient-Based Optimization, Precision Agriculture, Pattern Discovery.