This research work concentrate on developing digital soil maps to support field based plant phenotyping research. We have developed soil organic matter content (OM), cation exchange capacity (CEC), natural soil drainage class, and tile drainage line maps using topographic indices and aerial imagery. Various prediction models (universal kriging, cubist, random forest, C5.0, artificial neural network, and multinomial logistic regression) were used to estimate the soil properties of interest.
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/12264902 |
| Date | 07 May 2020 |
| Creators | Shams R Rahmani (8300103) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Digital_Soil_Mapping_of_the_Purdue_Agronomy_Center_for_Research_and_Education/12264902 |
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