A machine learning based approach for prediction and interpretation of soil properties from soil spectral data

Abstract

Aim: An active agricultural sector depends on good soil quality, essential for sustained food cultivation. However, intensive farming and rising demand can lead to soil deterioration, affecting crop yields. Smart soil prediction driven by machine learning is crucial for precision farming and efficient nutrient distribution. Methodology: Visible-near infrared Spectroscopy (vis-NIRS) is used to capture the soil's spectral data.Then, the spectral data is preprocessed with Savitzky-Golay Smoothing.The data that has been preprocessed is then used to train the machine learning model.The preprocessed data enhances model performance compared to spectral reflectance data in its unprocessed state.The machine learning model acquires data-based knowledge, identifies patterns, and predicts soil quality parameters. The Random Forest and Gradient Boosted Regression Tree are two algorithms employed in this study. Results: The spectral reflectance data is used to train, validate, and evaluate the machine learning model.In determining soil properties, both algorithms demonstrated a high degree of prediction accuracy, as demonstrated by the results.Gradient Boosted Regression Tree out performs Random Forest, but is expensive and requires sequential data. Random forest algorithm works well with large datasets, but over-fitting issues arise in some instances. Interpretation: The findings of the study indicate that machine learning can automate the current soil testing procedure in laboratories, thereby making it more efficient, affordable, and environmentally friendly. Key words: Gradient Boosted Regression Tree, Machine learning, Random forest, Soil fertility, Soil moisture