Applying Variable Selection Methods and Preprocessing Techniques to Hyperspectral Reflectance Data to Estimate Tea Cultivar Chlorophyll Content

Abstract

Tea is second only to water as the world’s most popular drink and it is consumed in various forms, such as black and green teas. A range of cultivars has therefore been developed in response to customer preferences. In Japan, farmers may grow several cultivars to produce different types of tea. Leaf chlorophyll content is affected by disease, nutrition, and environmental factors. It also affects the color of the dried tea leaves: a higher chlorophyll content improves their appearance. The ability to quantify chlorophyll content would therefore facilitate improved tea tree management. Here, we measured the hyperspectral reflectance of 38 cultivars using a compact spectrometer. We also compared various combinations of preprocessing techniques and 14 variable selection methods. According to the ratio of performance to deviation (RPD), detrending was effective at reducing the influence of additive interference of scattered light from particles and then regression coefficients was the best variable selection method for estimating the chlorophyll content of tea leaves, achieving an RPD of 2.60 and a root mean square error of 3.21 μg cm−2.

Carotenoid Content Estimation in Tea Leaves Using Noisy Reflectance Data

“…In studies examining machine learning techniques, the Cubist algorithm has been shown globally to perform the best [37]. This excellent record of performance was also observed in the evaluation of vegetation properties based on reflectance data [23,24]. More recently still, deep-learning-based regression methods have gained in popularity for use in modeling complex relationships and making accurate predictions across various domains.…”

“…Recently, affordable, compact, fingertip-sized spectrometers (such as the C12880MA [Hamamatsu Photonics]) have been developed. These small devices can be attached to unmanned aerial vehicles for conducting measurements over large areas [23,24]. However, the measurements from these sensors contain various degrees of noise and may exhibit shifts.…”

Study on Rapid Non-Destructive Detection Method of Corn Freshness Based on Hyperspectral Imaging Technology

“…Figure 4e shows the spectral image after first-order derivation (D1) processing. This method highlights the rate of change in the peaks and facilitates peak detection and identification [27]. Figure 4f shows the spectral image after the second-order derivative (D2) processing, which highlights the curvature of the peaks and facilitates peak detection and identification [28].…”