Soil science challenges—An interdisciplinary overview of current and future topics

Pohl, Lydia; Maurischat, Philipp; Schiedung, Marcus; Weber, Tobias; Winkler, Pauline

doi:10.1002/jpln.202200396

Cited by 1 publication

(1 citation statement)

References 27 publications

(36 reference statements)

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“…However, traditional soil nutrient analysis methods are often timeconsuming, labor-intensive, and costly, making them unsuitable for large-scale monitoring and real-time decision-making [29,30]. These methods involve chemical extraction and analysis to determine nutrient concentrations in the soil [31,32]. While these methods ensure the reliability of soil nutrient detection, they are time-consuming and have stringent personnel and equipment requirements [33].…”

Section: Introductionmentioning

confidence: 99%

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

Wang,

Zhang,

Zhao

2023

Agronomy

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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.

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