Application of a Fusion Attention Mechanism-Based Model Combining Bidirectional Gated Recurrent Units and Recurrent Neural Networks in Soil Nutrient Content Estimation

Abstract

Accurately estimating soil nutrient content, including soil organic matter (OM), nitrogen (N), phosphorus (P), and potassium (K) levels, is crucial for optimizing agricultural practices and ensuring sustainable crop production. This paper proposes a model based on a fusion attention mechanism that combines bidirectional gated recurrent units (BiGRU) and recurrent neural networks (RNN) to estimate soil nutrient content. The proposed model integrates the fused attention mechanism with BiGRU and RNN to enhance the accuracy and effectiveness of soil nutrient prediction. The fused attention mechanism captures key features in the input data, while the BiGRU architecture captures both forward and backward contextual information, enabling the model to capture long-term dependencies in the data. The results demonstrate that the proposed Att-BiGRU-RNN model outperforms other constructed models, exhibiting a higher prediction accuracy and robustness. The model shows good estimation capabilities for soil OM, N, P, and K with estimation accuracies (R2) of 0.959, 0.907, 0.921, and 0.914, respectively. The application of this model in soil nutrient estimation has the potential to optimize fertilizer management, enhance soil fertility, and ultimately improve crop yield. Further research can explore the applicability of this model in precision agriculture and sustainable soil management practices, benefiting the agricultural sector and contributing to food security and environmental sustainability.