Optimizing machine learning models for predicting soil pH and total P in intact soil profiles with visible and near-infrared reflectance (VNIR) spectroscopy

Author:

Xu Shengxiang,Zhao Yongcun,Wang Yingyi

Publisher

Elsevier BV

Subject

Horticulture,Computer Science Applications,Agronomy and Crop Science,Forestry

Reference68 articles.

1. Eliminating the interference of soil moisture and particle size on predicting soil total nitrogen content using a NIRS-based portable detector;An;Comput. Electron. Agr.,2015

2. Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy;Bellon-Maurel;Trend Anal. Chem.,2010

3. Quantitative remote sensing of soil properties;Ben-Dor;Adv. Agron.,2002

4. Comparison of ultraviolet, visible, and near infrared sensing for soil phosphorus;Bogrekci;Biosyst. Eng.,2007

5. Brochu, E., Cora, V.M., de Freitas, N., 2010. A tutorial on Bayesian Optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv preprint arXiv:1012.2599.

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