Soil texture identification using LIBS data combined with machine learning algorithm

A machine learning based model is developed to classify the soil texture [Clay, Sandy clay (SC), Sandy clay loam (SCL), Sandy loam (SL) and Sand]. The model utilizes the data obtained with Laser Induced Breakdown Spectroscopy (LIBS) method. An investigation on LIBS experiment with and without black slit placed on the top of the soil surface is carried out to determine the appropriate input data format for the model. The considered input formats are raw spectrum, relative intensity (I-lambda/I-393 nm) and relative intensity (I-lambda/I-553 nm). The selection of.. 553 nm as reference line during the computation of relative intensity results in projecting the reflection phenomenon at other wavelengths. Also the reflection from the soil surface is highly dependent on incident plasma emission wavelength and soil texture. The Principle Component Analysis (PCA) method is used to explore the classification power of the input data format. The input data formats are given to machine learning classifiers such as k- Nearest Neighbor (k-NN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RB) and Naive Bayes (NB). From the experimental analysis, it is observed that the Naive Bayes classifier achieved higher F1-score (Clay = 0.93, SC = 1.00, SCL = 0.86, SL = 0.85 and Sand = 1.00), when it is subjected with I-lambda/I-553 nm data format. This improvement in performance metric is mainly attributed to the consideration of plasma reflection phenomenon at longer wavelength.