IUX -ADNET: MULTI-FUSION DATA ANALYSIS WITH ATTENTION APPROACH COMBINED WITH IUX DENSE MODEL FOR ACCURATE COTTON YIELD PREDICTION

Optimized approaches in agricultural productivity are known and critical factors to predict and identify the precise cotton yield prediction. This paper incorporates an innovative model architecture with the IUX framework using custom functionalities (I (identify), U (SDE), and X (explicit-DE)) implemented to integrate the multi-fusion approach on data. The proposed methodology describes the three models (i) IUX-Dense Neural Net, (ii) IUX-Attention model, and (iii) IUX-Fused Model, which are concatenated with both IUX-Dense and IUX-Attention models. These models are extensively trained with a multimodal-fused dataset (Mendeley dataset + weather-parameter-based synthetic dataset) that is imbued with different features. The experimental evaluations are implemented with 25 epochs, and its performance stability with early stopping criteria demonstrates the IUX-fused approach is far superior with 99.99% accuracy with both disease classification and yield predictions with the IUX-optimized solution with the least MSE (0.05), RMSE (0.24), and MAE (0.188) and the optimal EVS (explained variance score) (97.58). Similar comparisons with machine learning and other SOA architectures are made and tabulated with performance results on both classification and yield prediction analysis. Finally, the proposed framework with the IUX model approach, as IUX-ADNET demonstrates, has far superior capabilities and offers scalable and more reliable solutions towards cotton yield predictions and disease classification.