A lightweight hybrid model for accurate ammonia prediction in pig houses
In extensive pig farms, keeping good living conditions is crucial for pigs' good health, growth, and productivity, as ammonia (NH₃) negatively impacts health and reproduction. Recently, deep learning techniques have proven potential in predicting ammonia concentration by capturing complex tempo...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525004976 |
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| author | Jacqueline Musabimana Qiuju Xie Hong Zhou Ping Zheng Honggui Liu Tiemin Ma Jiming Liu |
| author_facet | Jacqueline Musabimana Qiuju Xie Hong Zhou Ping Zheng Honggui Liu Tiemin Ma Jiming Liu |
| author_sort | Jacqueline Musabimana |
| collection | DOAJ |
| description | In extensive pig farms, keeping good living conditions is crucial for pigs' good health, growth, and productivity, as ammonia (NH₃) negatively impacts health and reproduction. Recently, deep learning techniques have proven potential in predicting ammonia concentration by capturing complex temporal dependencies. However, the standard transformer deep learning method relies heavily on self-attention, leading to high computational complexity and long training times, limiting its use in dynamic environments and real-time applications with limited resources. To overcome these issues, this study presents a novel, lightweight hybrid transformer model that combines Convolutional Neural Networks, Long Short-Term Memory, and Transformer (CNN-LSTM-Transformer) for NH₃ prediction. The model replaces feedforward networks with separable convolutional layers to capture local and spatial dependencies more efficiently, as well as reduce computational complexity. It also replaces positional encoding with global average pooling, simplifying the architecture and improving temporal feature aggregation. The outcomes indicated that, compared to the standard transformer, the proposed model reduces memory size by 21.1 % (4.9 MB vs. 6.21 MB) and training time by 29.7 % (5 min vs. 7.11 min). It also outperforms the standard transformer in accuracy and efficiency, with Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and coefficient of determination R² of 0.3994, 0.0225, and 0.9260, respectively, compared to 0.4250, 0.0227, and 0.9162 for the standard model, reducing RMSE by 5.99 %, MAPE by 0.88 %, and increasing R² by 1.08 %. The model improves accuracy compared to other state-of-the-art and ability for NH3 prediction. |
| format | Article |
| id | doaj-art-e5782700d1ec40a19dc62a5e3f850500 |
| institution | Kabale University |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-e5782700d1ec40a19dc62a5e3f8505002025-08-20T03:40:41ZengElsevierSmart Agricultural Technology2772-37552025-12-011210126610.1016/j.atech.2025.101266A lightweight hybrid model for accurate ammonia prediction in pig housesJacqueline Musabimana0Qiuju Xie1Hong Zhou2Ping Zheng3Honggui Liu4Tiemin Ma5Jiming Liu6College of Electrical and Information, Northeast Agricultural University, Harbin 150030, ChinaCollege of Electrical and Information, Northeast Agricultural University, Harbin 150030, China; The Key Laboratory of Swine Facilities Engineering, Ministry of Agriculture and Rural Affairs, China; Engineering Research Center of Pig Intelligent Breeding and Farming in Northern Cold Region, Ministry of Education, Harbin 150030, China; Correspondence author: College of Electrical and Information, Northeast Agricultural University, Harbin 150030, ChinaCollege of Electrical and Information, Northeast Agricultural University, Harbin 150030, ChinaCollege of Electrical and Information, Northeast Agricultural University, Harbin 150030, ChinaThe Key Laboratory of Swine Facilities Engineering, Ministry of Agriculture and Rural Affairs, China; Engineering Research Center of Pig Intelligent Breeding and Farming in Northern Cold Region, Ministry of Education, Harbin 150030, China; College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, ChinaCollege of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, ChinaCollege of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, ChinaIn extensive pig farms, keeping good living conditions is crucial for pigs' good health, growth, and productivity, as ammonia (NH₃) negatively impacts health and reproduction. Recently, deep learning techniques have proven potential in predicting ammonia concentration by capturing complex temporal dependencies. However, the standard transformer deep learning method relies heavily on self-attention, leading to high computational complexity and long training times, limiting its use in dynamic environments and real-time applications with limited resources. To overcome these issues, this study presents a novel, lightweight hybrid transformer model that combines Convolutional Neural Networks, Long Short-Term Memory, and Transformer (CNN-LSTM-Transformer) for NH₃ prediction. The model replaces feedforward networks with separable convolutional layers to capture local and spatial dependencies more efficiently, as well as reduce computational complexity. It also replaces positional encoding with global average pooling, simplifying the architecture and improving temporal feature aggregation. The outcomes indicated that, compared to the standard transformer, the proposed model reduces memory size by 21.1 % (4.9 MB vs. 6.21 MB) and training time by 29.7 % (5 min vs. 7.11 min). It also outperforms the standard transformer in accuracy and efficiency, with Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and coefficient of determination R² of 0.3994, 0.0225, and 0.9260, respectively, compared to 0.4250, 0.0227, and 0.9162 for the standard model, reducing RMSE by 5.99 %, MAPE by 0.88 %, and increasing R² by 1.08 %. The model improves accuracy compared to other state-of-the-art and ability for NH3 prediction.http://www.sciencedirect.com/science/article/pii/S2772375525004976Deep learningTime seriesHybrid modelAir qualityEnvironment control |
| spellingShingle | Jacqueline Musabimana Qiuju Xie Hong Zhou Ping Zheng Honggui Liu Tiemin Ma Jiming Liu A lightweight hybrid model for accurate ammonia prediction in pig houses Smart Agricultural Technology Deep learning Time series Hybrid model Air quality Environment control |
| title | A lightweight hybrid model for accurate ammonia prediction in pig houses |
| title_full | A lightweight hybrid model for accurate ammonia prediction in pig houses |
| title_fullStr | A lightweight hybrid model for accurate ammonia prediction in pig houses |
| title_full_unstemmed | A lightweight hybrid model for accurate ammonia prediction in pig houses |
| title_short | A lightweight hybrid model for accurate ammonia prediction in pig houses |
| title_sort | lightweight hybrid model for accurate ammonia prediction in pig houses |
| topic | Deep learning Time series Hybrid model Air quality Environment control |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525004976 |
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