SOC and Soil Texture Estimation -- 2
Budget: €30 – €250 EUR
I'm seeking a data scientist with expertise in hyperspectral data analysis and soil science to help me estimate Soil Organic Carbon (SOC) and soil texture using Random Forest (RF) algorithm. The deliverable for this project is a comprehensive research report or paper detailing the methodology, findings and implications of the analysis.
Key Requirements:
- Proficient in Python for data analysis and model implementation.
- Extensive experience with the Random Forest (RF) algorithm.
- Ability to write a detailed and clear research report.
- Experience in preprocessing hyperspectral data for machine learning models.
- Proficiency in model validation techniques to ensure accuracy and reliability of results.
- Expertise in feature selection methods to optimize model performance.
Ideal Skills:
- Expertise in soil science and understanding of soil properties.
- Proficient in interpreting hyperspectral data.
- Strong academic writing skills.
Please note, while the primary algorithm for this project is RF, familiarity with Partial Least Squares Regression (PLSR) may be beneficial. The main objective is to apply RF and PLSR to the estimation of SOC and soil texture from hyperspectral data and estimate the sensor error impact on the retrievals.
The research report should be 20 pages long and follow IEEE format. The project should be completed within 1 week. Include graphs and charts for presenting the findings. The freelancer will use a provided dataset for the hyperspectral data analyses. Include a section on Discussion in the research report. The methodology section should include highly detailed steps and explanations. The primary goal of this research is to use the report for internal research purposes. Include R², MAE and RMSE values for model evaluation. The discussion section should focus on the impact of the sensor noise affecting the data on the findings of topsoil properties estimation (SOC and texture). Include detailed comparative charts to clearly represent the results.
Key Requirements:
- Proficient in Python for data analysis and model implementation.
- Extensive experience with the Random Forest (RF) algorithm.
- Ability to write a detailed and clear research report.
- Experience in preprocessing hyperspectral data for machine learning models.
- Proficiency in model validation techniques to ensure accuracy and reliability of results.
- Expertise in feature selection methods to optimize model performance.
Ideal Skills:
- Expertise in soil science and understanding of soil properties.
- Proficient in interpreting hyperspectral data.
- Strong academic writing skills.
Please note, while the primary algorithm for this project is RF, familiarity with Partial Least Squares Regression (PLSR) may be beneficial. The main objective is to apply RF and PLSR to the estimation of SOC and soil texture from hyperspectral data and estimate the sensor error impact on the retrievals.
The research report should be 20 pages long and follow IEEE format. The project should be completed within 1 week. Include graphs and charts for presenting the findings. The freelancer will use a provided dataset for the hyperspectral data analyses. Include a section on Discussion in the research report. The methodology section should include highly detailed steps and explanations. The primary goal of this research is to use the report for internal research purposes. Include R², MAE and RMSE values for model evaluation. The discussion section should focus on the impact of the sensor noise affecting the data on the findings of topsoil properties estimation (SOC and texture). Include detailed comparative charts to clearly represent the results.