Application of a Fusion Attention Mechanism-Based Model Combining Bidirectional Gated Recurrent Units and Recurrent Neural Networks in Soil Nutrient Content Estimation
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 c...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/13/11/2724 |
| _version_ | 1797460541419028480 |
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| author | Huan Wang Lixin Zhang Jiawei Zhao |
| author_facet | Huan Wang Lixin Zhang Jiawei Zhao |
| author_sort | Huan Wang |
| collection | DOAJ |
| description | 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 (<i>R</i><sup>2</sup>) 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. |
| first_indexed | 2024-03-09T17:06:29Z |
| format | Article |
| id | doaj.art-85f5d793d8c248bcb5fef9fed383e0ad |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-09T17:06:29Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-85f5d793d8c248bcb5fef9fed383e0ad2023-11-24T14:23:46ZengMDPI AGAgronomy2073-43952023-10-011311272410.3390/agronomy13112724Application of a Fusion Attention Mechanism-Based Model Combining Bidirectional Gated Recurrent Units and Recurrent Neural Networks in Soil Nutrient Content EstimationHuan Wang0Lixin Zhang1Jiawei Zhao2School of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, ChinaSchool of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, ChinaSchool of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, ChinaAccurately 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 (<i>R</i><sup>2</sup>) 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.https://www.mdpi.com/2073-4395/13/11/2724soil nutrienthyperspectralattention mechanismdeep learningfeature extraction |
| spellingShingle | Huan Wang Lixin Zhang Jiawei Zhao Application of a Fusion Attention Mechanism-Based Model Combining Bidirectional Gated Recurrent Units and Recurrent Neural Networks in Soil Nutrient Content Estimation Agronomy soil nutrient hyperspectral attention mechanism deep learning feature extraction |
| title | Application of a Fusion Attention Mechanism-Based Model Combining Bidirectional Gated Recurrent Units and Recurrent Neural Networks in Soil Nutrient Content Estimation |
| title_full | Application of a Fusion Attention Mechanism-Based Model Combining Bidirectional Gated Recurrent Units and Recurrent Neural Networks in Soil Nutrient Content Estimation |
| title_fullStr | Application of a Fusion Attention Mechanism-Based Model Combining Bidirectional Gated Recurrent Units and Recurrent Neural Networks in Soil Nutrient Content Estimation |
| title_full_unstemmed | Application of a Fusion Attention Mechanism-Based Model Combining Bidirectional Gated Recurrent Units and Recurrent Neural Networks in Soil Nutrient Content Estimation |
| title_short | Application of a Fusion Attention Mechanism-Based Model Combining Bidirectional Gated Recurrent Units and Recurrent Neural Networks in Soil Nutrient Content Estimation |
| title_sort | application of a fusion attention mechanism based model combining bidirectional gated recurrent units and recurrent neural networks in soil nutrient content estimation |
| topic | soil nutrient hyperspectral attention mechanism deep learning feature extraction |
| url | https://www.mdpi.com/2073-4395/13/11/2724 |
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