Sparse Attention Regression Network Based Soil Fertility Prediction With Ummaso

TL;DR

This paper introduces a novel soil fertility prediction model combining Sparse Attention Regression with UMAP and LASSO to address imbalanced datasets, achieving high accuracy and interpretability.

Contribution

The study presents a new hybrid approach integrating UMAP, LASSO, and Sparse Attention Regression for improved soil fertility prediction on imbalanced datasets.

Findings

Achieved 98% predictive accuracy.

Attained 91.25% precision in identifying fertile soil.

Reached 90.90% recall, effectively capturing true positives.

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 utilized initially to reduce data complexity, unveiling hidden structures and important 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. Additionally, it showcases a Precision of 91.25%, indicating its adeptness in identifying fertile soil instances accurately. The Recall metric stands at 90.90%, emphasizing the model's ability to capture true positive cases effectively.