Integrative Application of Deep Learning and Multispectral Remote Sensing for Predictive Crop Management in Precision Agriculture

Abstract

This study introduces an innovative approach to predictive crop management in precision agriculture by integrating deep learning with multispectral remote sensing technologies. The research aims to develop a framework that combines multispectral data from field sensors, UAVs, and satellites with a deep learning model based on a multimodal architecture incorporating adaptive transfer learning and attention mechanisms. Data were collected over two growing seasons and underwent preprocessing, vegetation feature extraction, and model training and validation. The proposed deep learning model significantly outperformed traditional machine learning algorithms such as Random Forest and Support Vector Machines, achieving up to 97.8% accuracy in crop classification. Predicted crop conditions and yield estimates showed a strong correlation with actual field data (r = 0.89; RMSE = 0.12). Field implementation of the predictive system indicated potential increases in crop yield by 18% and reductions in agricultural input usage by 28%. These results highlight the potential of deep learning and multispectral data integration to enhance decision-making, resource efficiency, and sustainability in precision farming. Furthermore, the approach demonstrates strong scalability for different crop types and geographical regions, providing a solid foundation for the digital transformation of agriculture toward a more adaptive and sustainable food production system.