Sparse attention regression network-based soil fertility prediction with UMMASO

Abstract

The challenge of imbalanced soil nutrient datasets significantly hampers accurate predictions of soil fertility. To tackle this, a new method is suggested in this research, combining Uniform Manifold Approximation and Projection (UMAP) with Least Absolute Shrinkage and Selection Operator (LASSO). The main aim is to counter the impact of uneven data distribution and improve soil fertility models' predictive precision. The model introduced uses Sparse Attention Regression, effectively incorporating pertinent features from the imbalanced dataset. UMAP is utilised initially to reduce data complexity, unveiling hidden structures and essential patterns. Following this, LASSO is applied to refine features and enhance the model's interpretability. The experimental outcomes highlight the effectiveness of the UMAP and LASSO hybrid approach. The proposed model achieves outstanding performance metrics, reaching a predictive accuracy of 98 %, demonstrating its capability in accurate soil fertility predictions. It also showcases a Precision of 91.25 %, indicating its adeptness in accurately identifying fertile soil instances. The Recall metric stands at 90.90 %, emphasizing the model's ability to capture true positive cases effectively.