AgroFusion: Combining Multiple Deep Learning Techniques of Data to Improve Crop Management and Forecast Yield

AgroFusion is a state-of-the-art deep learning framework that combines GNNs, LSTMs, and CNNs under one architecture. The AgroFusion framework has calculated a broad collection of agricultural data-including multispectral satellite imaging, the time-series readings from sensors, and spatial linkages among the fields-to provide highly accurate insights into crop recommendations, disease diagnosis, and yield predictions. Though the LSTM manages to capture long-term temporal correlations in environmental factors, the GNN really captures the complicated spatial interaction among agriculture farmlands. Through an adapted attention mechanism, the performance of the CNN in extracting relevant information from the remote sensing images and minimal alterations of crop condition improves. This combines the outputs of these components through a special cross-modal attention layer to allow for comprehensive analysis that leverages several data modalities. Experimental results show a significant rise in accuracy in agricultural insights in comparison with traditional methods. The novel architecture provides farmers with a strong tool for data-driven decision-making and sustainable practices, thereby ultimately making agricultural management and productivity more efficient in the face of modern challenges.