基于GhostNet-CBAM的近红外土壤pH值参数的光谱测量
Jianguo Zhu1,2, Wenjin Wang2,3, Peng Tian2,3
1School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang, Hunan Province, China.
PloS one
|June 13, 2025
概括
一个新的Ghostnet-CBAM神经网络使用近红外 (NIR) 光谱准确测量土壤的pH值. 该方法改进了现有的精确土壤分析技术,有助于营养管理和生物活动理解.
科学领域:
- 农业科学 农业科学
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 土壤的pH值对于植物的营养吸收和土壤的生物活性至关重要.
- 准确的土壤pH值测量对于有效的农业管理至关重要.
- 现有的土壤pH分析方法可能耗时或缺乏精度.
研究的目的:
- 开发一种新的神经网络算法,以快速准确地预测土壤pH值.
- 为了利用近红外 (NIR) 光谱数据测量土壤pH值.
- 根据既有方法评估拟议的Ghostnet-CBAM模型的性能.
主要方法:
- 采用了一个与卷积区注意模块 (CBAM) 集成的Ghostnet模型.
- 蒙特卡洛交叉验证 (MCCV) 用于识别和删除异常的土壤光谱数据.
- 幽灵网-CBAM模型是使用卢卡斯土壤光谱数据集进行训练和验证的.
主要成果:
- 幽灵网络-CBAM模型实现了0.9447.7的确定系数 (R2).
- 根平均平方误差 (RMSE) 低至0.3267,表明精度很高.
- 分散密度图证实了预测和真实的土壤pH值之间的强相关性.
结论:
- 幽灵网-CBAM算法提供了一种快速而准确的方法,用于从NIR光谱中确定土壤pH值.
- 这种方法在农业应用的土壤分析方面取得了重大进展.
- 与部分最小平方回归 (PLSR) 和VGGNet-16.2相比,该模型显示出更高的性能.
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