An Attention-Enhanced PSO-CNN-BiLSTM Model for Precise Soil Phosphorus Content Prediction

Abstract

Background: The impact of soil phosphate, an essential micronutrient, on agricultural productivity and crop growth is significant. The accurate determination of phosphorus in the soil is crucial for sustainable agriculture and efficient fertilizer usage. Standard laboratory analysis of soil is time-consuming and expensive, making it inadequate for monitoring large areas of land. Simple methods for estimating soil nutrients can now be achieved through the use of local environmental parameters, thanks to ML and deep learning.
Results: A model that enhances attention was developed by PSO-CNN-Bidirectional Long Short-Term Memory Particle Swarm Optimization–Convolutional Neural Network–Bidirectional Long Short-Term Memory (PSO–CNR-BILSTM), which accurately predicted the soil phosphorus content. Our study included this work. No details? The proposed method involves the use of convolutional neural networks (CNN) for spatial characteristics, BILSTM for discovering patterns over time in the target variable, and an attention mechanism to identify crucial factors that impact phosphorus dynamics. Particle Swarm Optimization (PSO) was used to optimize the model hyperparameters and enhance our predictions. This was achieved through automation.
The model was adapted to the conditions by taking into account soil and environmental variables from the Hulan soil monitoring dataset before being tested. Using experimental data, the proposed model has an R2 of approximately 0.96 and an inversely related RMSE of around 0.20. Both are empirically significant. The reliability of the model was assessed by examining correlation heat maps, residual variance distributions and actual versus predicted curves.
Conclusion: This proposed hybrid framework provides intelligent and effective means to predict soil nutrients, which is beneficial for precision agriculture and farm management systems based on data.