通过深度学习技术提高水培和土壤化合物预测的性能
Mustufa Haider Abidi1, Sanjay Chintakindi2, Ateekh Ur Rehman2
1Advanced Manufacturing Institute, King Saud University, Riyadh, Saudi Arabia.
PeerJ. Computer science
|December 13, 2024
概括
本研究引入了一种深度学习模型,用于预测植物生长期间的土壤和水培化合物动态,增强农业决策,以实现可持续的作物生产. 这种创新方法提高了对植物与环境相互作用的理解,并有助于管理土壤污染风险.
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:
- 土壤质量对作物营养和产量至关重要,其成分影响作物选择和杂草管理.
- 由新出现的污染物造成的土壤污染对水资源和粮食生产构成重大风险.
- 对土壤和植物中的化学物质运输和反应进行准确的建模对于有效的缓解策略至关重要.
研究的目的:
- 开发一种创新的深度学习方法,用于预测植物生长过程中的水培和土壤化合物动态.
- 在描述复杂的植物-土壤相互作用时克服传统数值模型的局限性.
- 加强农业决策,以实现可持续和高效的作物生产.
主要方法:
- 从在线资源中获取数据,然后进行特征提取阶段.
- 使用代辅助增强母优化算法 (IEMOA) 确定特征的最佳重量.
- 使用基于融合的多尺度特征卷积自动编码器与门式反复单元 (MS-CAGRU) 网络进行水耕和土壤化合物预测.
主要成果:
- 成功预测水培和土壤化合物动态.
- 提取加权特征,深度信念网络 (DBN) 特征和原始特征.
- 通过对传统方法的性能评估,证明了拟议模型的有效性.
结论:
- 开发的深度学习模型有效地预测了土壤和水培化合物动态.
- 这种方法提高了对植物与环境相互作用的理解,并有助于可持续农业.
- 该模型为管理土壤污染和提高作物生产效率提供了一个有前途的工具.
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