ECP-IEM:通过深度集成的模型提高季节性作物生产率
Ghulam Mustafa1, Muhammad Ali Moazzam1, Asif Nawaz1
1University Institute of Information Technology, PMAS-Arid Agriculture University, Rawalpindi, Pakistan.
PloS one
|February 5, 2025
概括
准确的作物产量预测对于粮食安全至关重要. 结合Bi-GRU和时间序列CNN的新人工智能模型显著提高了预测准确性,超过了用于更好的农业规划的传统方法.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 准确的作物产量预测对于全球粮食安全至关重要,特别是随着人口增长和气候变化.
- 传统的机器学习 (ML) 方法在涉及多个变量 (天气,土壤,气候) 时,难以准确.
- 人工智能 (AI) 提供先进的解决方案,以提高作物产量预测的准确性.
研究的目的:
- 开发和评估一个先进的AI模型,用于准确的作物产量预测.
- 解决现有的ML方法在处理复杂,多变量数据集方面的局限性.
- 通过早期产量洞察力,为改善作物生产提供可行的建议.
主要方法:
- 使用支持矢量机器 (SVM) 进行数据收集,预处理和特征提取.
- 功能选择包括标准化谷歌距离 (NGD) 和新星排名方法.
- 实现双向门循环单元 (Bi-GRU) 和时间序列卷积神经网络 (CNN) 的集成,用于产量预测.
主要成果:
- 提出的模型,ECP-IEM,在多个数据集中表现出卓越的性能.
- 实现了高精度 (96.34%),精度 (94.56%) 和回忆 (95.23%).
- 低误差指标 (MAE:0.191,MSE:0.0674,RMSE:0.238) 表明了强大的预测能力.
结论:
- 与基线模型相比,人工智能驱动的方法显著提高了作物产量预测的准确性.
- 该模型提供可靠的早期产量预测,帮助农民优化作物生产.
- 这项研究有助于通过先进的农业技术提高粮食安全.
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