In precision agriculture, microbial biostimulants offer significant potential for enhancing crop growth, soil health, and overall farm productivity. However, their efficacy highly depends on complex interactions between the microbial agents and environmental factors. This chapter explores applying hybrid deep-learning models for the predictive modelling of microbial biostimulants in precision agriculture and integrating deep learning architectures, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), with domain-specific features to improve prediction accuracy and provide actionable insights for optimising biostimulants applications. This chapter reviews the current state of deep learning techniques in agricultural modelling, highlights case studies demonstrating the effectiveness of hybrid models, and discusses future directions and challenges in this evolving field.