Impact of agricultural industry transformation based on deep learning model evaluation and metaheuristic algorithms under dual carbon strategy
Abstract This study provides scientific evidence to support sustainable agricultural development and advance the dual carbon goals. A hybrid deep learning model—combining Convolutional Neural Networks and Long Short-Term Memory networks—is developed to evaluate the effects of agricultural industry t...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-14073-1 |
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| author | Xuan Zhao Weiyun Tang Qiuyan Liu Hongtao Cao Fei Chen |
| author_facet | Xuan Zhao Weiyun Tang Qiuyan Liu Hongtao Cao Fei Chen |
| author_sort | Xuan Zhao |
| collection | DOAJ |
| description | Abstract This study provides scientific evidence to support sustainable agricultural development and advance the dual carbon goals. A hybrid deep learning model—combining Convolutional Neural Networks and Long Short-Term Memory networks—is developed to evaluate the effects of agricultural industry transformation. Convolutional Neural Networks are used to extract spatial features from agricultural data, while Long Short-Term Memory networks processed time series data. To enhance model performance, the slime mould algorithm is employed for parameter optimization. Experimental results demonstrated that the hybrid model achieves excellent predictive accuracy, with crop yield prediction exceeding 99%. The average error between the model’s evaluation and the actual transformation outcomes is only 3.33%. Across various climatic conditions, the average prediction error remains below 2.5%, indicating strong adaptability and stability. Compared with traditional methods—such as deep neural networks, support vector machines, and linear regression—the proposed model effectively integrates static and dynamic agricultural data. Static features, including farmland distribution and soil types, are extracted using Convolutional Neural Networks, while temporal trends in variables such as weather patterns and policy changes are captured by the Long Short-Term Memory network. This adaptive fusion of multidimensional data significantly improves the accuracy of both crop yield forecasting and agricultural transformation assessment. In conclusion, the model offers a robust, high-accuracy decision-support tool for promoting low-carbon agricultural development. It provides practical insights for advancing sustainability and supporting the national dual carbon strategy. |
| format | Article |
| id | doaj-art-4cc368bb33824f0380e8f487f2f2dae2 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-4cc368bb33824f0380e8f487f2f2dae22025-08-20T03:42:33ZengNature PortfolioScientific Reports2045-23222025-07-0115112010.1038/s41598-025-14073-1Impact of agricultural industry transformation based on deep learning model evaluation and metaheuristic algorithms under dual carbon strategyXuan Zhao0Weiyun Tang1Qiuyan Liu2Hongtao Cao3Fei Chen4College of Business Administration, Shanghai Urban Construction Vocational CollegeCollege of Business Administration, Shanghai Urban Construction Vocational CollegeSchool of Economy and Management, Zhejiang University of Water Resources and Electric PowerCollege of Business Administration, Shanghai Urban Construction Vocational CollegeCollege of Business Administration, Shanghai Urban Construction Vocational CollegeAbstract This study provides scientific evidence to support sustainable agricultural development and advance the dual carbon goals. A hybrid deep learning model—combining Convolutional Neural Networks and Long Short-Term Memory networks—is developed to evaluate the effects of agricultural industry transformation. Convolutional Neural Networks are used to extract spatial features from agricultural data, while Long Short-Term Memory networks processed time series data. To enhance model performance, the slime mould algorithm is employed for parameter optimization. Experimental results demonstrated that the hybrid model achieves excellent predictive accuracy, with crop yield prediction exceeding 99%. The average error between the model’s evaluation and the actual transformation outcomes is only 3.33%. Across various climatic conditions, the average prediction error remains below 2.5%, indicating strong adaptability and stability. Compared with traditional methods—such as deep neural networks, support vector machines, and linear regression—the proposed model effectively integrates static and dynamic agricultural data. Static features, including farmland distribution and soil types, are extracted using Convolutional Neural Networks, while temporal trends in variables such as weather patterns and policy changes are captured by the Long Short-Term Memory network. This adaptive fusion of multidimensional data significantly improves the accuracy of both crop yield forecasting and agricultural transformation assessment. In conclusion, the model offers a robust, high-accuracy decision-support tool for promoting low-carbon agricultural development. It provides practical insights for advancing sustainability and supporting the national dual carbon strategy.https://doi.org/10.1038/s41598-025-14073-1Metaheuristic algorithm optimizationAgricultural industry transformationDual carbon strategyConvolutional neural networksLong short-term memorySlime mould algorithm |
| spellingShingle | Xuan Zhao Weiyun Tang Qiuyan Liu Hongtao Cao Fei Chen Impact of agricultural industry transformation based on deep learning model evaluation and metaheuristic algorithms under dual carbon strategy Scientific Reports Metaheuristic algorithm optimization Agricultural industry transformation Dual carbon strategy Convolutional neural networks Long short-term memory Slime mould algorithm |
| title | Impact of agricultural industry transformation based on deep learning model evaluation and metaheuristic algorithms under dual carbon strategy |
| title_full | Impact of agricultural industry transformation based on deep learning model evaluation and metaheuristic algorithms under dual carbon strategy |
| title_fullStr | Impact of agricultural industry transformation based on deep learning model evaluation and metaheuristic algorithms under dual carbon strategy |
| title_full_unstemmed | Impact of agricultural industry transformation based on deep learning model evaluation and metaheuristic algorithms under dual carbon strategy |
| title_short | Impact of agricultural industry transformation based on deep learning model evaluation and metaheuristic algorithms under dual carbon strategy |
| title_sort | impact of agricultural industry transformation based on deep learning model evaluation and metaheuristic algorithms under dual carbon strategy |
| topic | Metaheuristic algorithm optimization Agricultural industry transformation Dual carbon strategy Convolutional neural networks Long short-term memory Slime mould algorithm |
| url | https://doi.org/10.1038/s41598-025-14073-1 |
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