AI-Driven NAS-GBM Model for Precision Agriculture: Enhancing Crop Yield Prediction Accuracy

Precision agriculture has emerged as a vital approach for optimizing crop yield prediction, enabling data-driven decision-making to improve agricultural productivity. Traditional forecasting methods encounter difficulties due to extreme complexity within environmental factors while operating under dynamic farming conditions. An AI framework combining NAS and GBM serves as the solution to address these issues through enhancing predictive capabilities. This study works to produce an automated system which selects optimal models through optimization processes for more accurate crop yield forecasts. Through NAS component exploration the optimal neural network architecture can be identified whereas GBM component effectively analyzes non-linear dependencies in data which leads to superior predictive capabilities. Data processing techniques precede model development by using Recursive Feature Elimination (RFE) for feature selection which leads to training NAS-optimized deep learning architectures together with GBM. The researchers applied the model to real agriculture datasets which included essential agricultural variables comprising soil conditions and weather elements and crop health measurements. The experimental results prove that the developed NAS-GBM framework achieves superior performance compared to standard models across three major aspects including predictive accuracy and computation efficiency in addition to generalization capability. The research project uses TensorFlow and Scikit-learn alongside Optuna for model optimization while it depends on cloud-based computational resources for extensive processing requirements. AI-driven hybrid models based on the research demonstrate their capability to improve decision-making capabilities for farmers together with agronomists.