Machine Learning for Estimating Leaf Dust Retention Based on Hyperspectral Measurements

Abstract

Hyperspectral sensors provide detailed information for dust retention content (DRC) estimation. However, rich hyperspectral data are not fully utilized by traditional image analysis techniques. We integrated several recently developed machine learning algorithms to estimate DRC on plant leaves using the spectra measured by the ASD FieldSpec 3. The experiments were carried out on three common green plants of southern China. The important hyperspectral variables were first identified by applying the random forest (RF) algorithm. Three estimation models were then developed using the support vector machine (SVM), classification and regression tree (CART), and RF algorithms. The results showed that the increase in dust retention contents on plant leaves enhanced their reflectance in the visible wavelength but weakened their reflectance in the infrared wavelength. Wavelengths in the ranges of 450–500 nm, 550–600 nm, 750–1000 nm, and 1100–1300 nm were identified as important variables using the RF algorithm and were used to estimate the DRC. The comparison of the three machine learning techniques for DRC estimation confirmed that the SVM and RF models performed well because their estimations were similar to the measured DRC. Specifically, the average R2 for SVM and RF model are 0.85 and 0.88. The technical approach of this study proved to be a successful illustration of using hyperspectral measurements to estimate the DRC on plant leaves. The findings of this study can be applied to monitor the DRC on leaves of other plants and can also be integrated with other types of spectral data to measure the DRC at a regional scale.