根据双碳战略,基于深度学习模型评估和元启发算法的农业产业转型的影响
Xuan Zhao1, Weiyun Tang1, Qiuyan Liu2
1College of Business Administration, Shanghai Urban Construction Vocational College, Shanghai, 200000, China.
Scientific reports
|July 31, 2025
概括
一个新的混合深度学习模型准确预测作物产量 (超过99%) 并评估农业转型的影响,支持可持续的低碳发展和双碳目标.
科学领域:
- 农业科学 农业科学
- 环境科学 环境科学
- 计算机科学 计算机科学
背景情况:
- 实现可持续农业发展和双碳目标需要对产业转型进行准确的评估.
- 传统的方法很难有效地整合各种农业数据.
研究的目的:
- 开发一种混合深度学习模型,用于评估农业产业转型的影响.
- 为了提高作物产量和转化结果的预测准确性.
- 支持低碳农业发展战略.
主要方法:
- 开发了一种混合模型,将空间特征的卷积神经网络 (CNN) 与时间数据的长短期记忆 (LSTM) 网络结合起来.
- 利用粘液模具算法优化模型参数.
- 集成的静态 (如土壤类型) 和动态 (如天气) 农业数据.
主要成果:
- 在作物产量预测中达到99%以上的准确性.
- 在评估转型结果时,平均误差仅为3.33%.
- 在各种气候条件下表现出强大的适应性和稳定性,预测误差低于2.5%.
结论:
- 混合CNN-LSTM模型为可持续农业提供了强大的,高精度的决策支持工具.
- 有效地整合多维数据,提高预测和评估准确性.
- 为推进国家双碳战略和促进低碳农业提供实用见解.
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