使用反射光谱和机器学习算法的土壤度自动预测
Fabio Eliveny Rivadeneira-Bolaños1, Sandra Esperanza Nope-Rodríguez1, Martha Isabel Páez-Melo2
1Escuela de Ingeniería Eléctrica y Electrónica (EIEE) Facultad de Ingeniería, Universidad del Valle, Cali, Colombia.
MethodsX
|November 5, 2024
概括
这项研究引入了使用UV-VIS光谱测量和机器学习预测土壤的更快方法. 它准确地确定了总度,有助于农业营养管理.
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:
- 准确的土壤营养分析对于有效的农业管理和肥料应用至关重要.
- 传统的实验室方法来确定总度可能是耗时的.
- 光谱技术为快速土壤分析提供了潜力.
研究的目的:
- 开发和验证一种基于机器学习的方法,用于预测土壤中总度.
- 使用UV-VIS光谱光度和先进的数据分析来快速估计.
- 通过更快,更准确的土壤分析,加强营养管理策略.
主要方法:
- 用UV-VIS V-750光谱仪分析了土壤样本,以获得反射频谱 (200-900nm).
- 预处理了包括Savitzky-Golay过在内的光谱,并使用投影变量重要性 - 部分最小方形 (VIP-PLS) 和随机森林 (RF) 识别了关键波长.
- 主要组件分析 (PCA) 用于减小维度,然后对预测的前人工神经网络进行训练.
主要成果:
- 开发的方法成功预测了来自哥伦比亚桑坦德尔·德·基利查奥 (Santander de Quilichao) 的土壤中的总度.
- 特性选择技术 (VIP-PLS,RF) 减少了光谱带,PCA进一步降低了数据维度.
- 与传统方法相比,机器学习模型证明了快速估计的潜力.
结论:
- 拟议的方法为土壤分析提供了比传统的实验室测试更快的替代方法.
- 光谱学和机器学习的整合为农业中增强营养管理提供了有价值的工具.
- 未来的工作包括将该方法扩展到各种土壤类型和其他必需的宏观营养素.
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