AN INTUITIVE ANALYSIS ON EARLY DETECTION WITH IAGT MODEL FOR COTTON CROP YIELD PREDICTION

Cotton yield prediction plays a critical role in modern agriculture, influencing food security, economic stability, and effective resource management. Despite the advancements in various predictive algorithms like support vector machines (SVM), random forests, and artificial neural networks, challenges persist in handling the complex, high-dimensional, and non-linear nature of agricultural data. Traditional models struggle with issues such as dynamic environmental fluctuations, incomplete datasets, and an inability to effectively adapt to evolving conditions in the field. As a result, these models often fail to provide accurate and timely predictions, leading to inefficiencies in crop management, resource allocation, and risk mitigation. This study addresses the knowledge gap in cotton yield prediction by introducing the Integrated Adaptive Growth Tree (IAGT) algorithm, which combines decision trees with deep learning techniques for real-time adaptation to changing agricultural conditions. By integrating multi-source data, including satellite imagery, weather forecasts, and soil sensor readings, the IAGT model offers a novel approach to yield forecasting, surpassing traditional methods in both accuracy and adaptability. Simulations using both original and synthetic cotton yield datasets showed a remarkable 98% accuracy, demonstrating significant improvements in prediction performance. This study not only provides new insights into the effective integration of diverse data sources for crop yield forecasting but also introduces a robust framework for early-stage disease detection and anomaly identification, thus contributing to the growing field of precision agriculture. The IAGT model’s success in enhancing cotton yield predictions sets the stage for broader applications in crop management and agricultural sustainability.