Ensemble Model-Based Crop Recommendation System with Data Generation Using GAN

The increasing global demand for agricultural products to sustain a growing population requires a transformative approach to traditional agricultural practices. Precision agriculture is emerging as an important solution to meet growing food needs and reduce environmental impacts. Existing studies in precision agriculture have primarily focused on utilizing machine learning techniques for crop recommendation. However, there is a lack of research on integrating GANs with rule-based algorithms to generate synthetic data specifically tailored to agricultural settings. This research bridges this gap by proposing an innovative methodology that leverages the strengths of both approaches to improve CRS recommendations by generating synthetic farm records. GANs are data similar to real-world agricultural records, which manage data that is authentic and solid. Our approach enhances the quality of the data by applying real agricultural intelligence in the reserved areas of Tamil Nadu. The integration of ensemble methods, such as Artificial Neural Network (ANN) and Radial Basis Function (RBF) networks, in crop selection within precision agriculture is a relatively unexplored area. Artificial Neural Networks (ANN) excel at capturing complex patterns, while Radial Basis Function (RBF) networks provide complex classification by modelling intricate decision boundaries. This hybrid model overcomes the challenge of data scarcity and enhances CRS with improved machine-learning capabilities. By improving prediction accuracy, CRS can enable farmers to gain insights that align traditional practices with new technologies, contributing to sustainable agriculture and efficient resource utilization. This initiative represents a beacon of innovation in the journey towards precision farming.