#UNSWSoilScienceCentral2019: My Top 3
1. The encouragement and support from each other which shows we are a real team
2. The “clear, concise and consistent” rule makes complicated things easy to follow.
3. JT keeping his door open and being patient to us.
Establishing a vis-NIR spectral library for predicting clay in cotton growing soil using machine learning algorithm and various sample sizes
The cotton growing areas of south-eastern Australia are highly productive. To maintain profitability, information pertaining to nutrient management and water use efficiency are needed. In this regard, information about clay content is required. This is a time-consuming and expensive laboratory analysis to undertake. An alternative is the use of visible near infra-red (Vis-NIR) spectroscopy, which has shown great potential on the field and global scales. However, the established spectral libraries are site-specific and not readily available for predicting properties in different areas. In this study, we explore some of these issues to demonstrate these problems by considering clay content prediction using a machine learning algorithm (i.e. Cubist) from Vis-NIR data acquired from topsoil (0-0.3 m) and subsurface (0.3-0.6 m) samples in seven cotton growing areas. The first aim is to assess the ability of soil samples collected from each area to predict clay content independent of the other 6 areas. The second aim is to determine the ability of the soil samples of 6 areas to predict clay content in an area which is withheld from the calibration. The third aim is to explore the potential to improve the prediction model using a spectra-based sampling approach. We also investigate the effects of combining soil samples from different depths on model performance.
Supervisor: Associate Professor John Triantafilis
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Zhao, D., Zhao, X., Khongnawang, T., Arshad, M., & Triantafilis, J. (2018). A Vis-NIR spectral library to predict clay in Australian cotton growing soil. Soil Science Society of America Journal.