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. 2025 Jul 31;15(1):27929.
doi: 10.1038/s41598-025-14073-1.

Impact of agricultural industry transformation based on deep learning model evaluation and metaheuristic algorithms under dual carbon strategy

Xuan Zhao #  1 , Weiyun Tang #  1 , Qiuyan Liu  2 , Hongtao Cao  1 , Fei Chen  1
Affiliations

Impact of agricultural industry transformation based on deep learning model evaluation and metaheuristic algorithms under dual carbon strategy

Xuan Zhao et al. Sci Rep. .

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.

Keywords: Agricultural industry transformation; Convolutional neural networks; Dual carbon strategy; Long short-term memory; Metaheuristic algorithm optimization; Slime mould algorithm.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests. Ethics statement: This article does not contain any studies with human participants or animals performed by any of the authors. All methods were performed in accordance with relevant guidelines and regulations.

Figures

Fig. 1
Fig. 1
Convolution operation process.
Fig. 2
Fig. 2
Process of Pooling Operation.
Fig. 3
Fig. 3
Parameter Tuning and Optimization Process of SMA.
Fig. 4
Fig. 4
Structure and Optimization Process of the CNN-LSTM-SMA Hybrid Model.
Fig. 5
Fig. 5
Evaluation of the Effects of Agricultural Industry Transformation Predicted by Hybrid Deep Learning Models.
Fig. 6
Fig. 6
Evaluation of the Effects of Agricultural Industry Transformation in Different Regions by Hybrid Deep Learning Models.
Fig. 7
Fig. 7
Convergence analysis of the fitness function (a) Box plot; (b) Convergence performance.
Fig. 8
Fig. 8
Comparison between Model-Predicted Effects of Agricultural Industry Transformation and Actual Effects.
Fig. 9
Fig. 9
Prediction Stability of the Model under Different Climate Conditions.
Fig. 10
Fig. 10
Performance Comparison of Different Models in Agricultural Transformation Classification Tasks.
See this image and copyright information in PMC

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