Impact of agricultural industry transformation based on deep learning model evaluation and metaheuristic algorithms under dual carbon strategy - Xuan Zhao; Weiyun Tang; Qiuyan Liu; Hongtao Cao; Fei Chen (Scientific Reports)
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.