Location of Sampling Points in Optical Reflectance Measurements of Chinese Cabbage and Kale Leaves

Abstract

Purpose: A sampling scheme may significantly affect the accuracy of a sensor. This study was conducted to investigate the effects of sampling point locations on optical reflectance measurements of Chinese cabbage and kale plant leaves. Methods: Variability and similarity of multiple measurements for different parts of the leaves were compared. Results: The results indicate that the variability between the average and individual reflectance spectra was smaller for the blade part than for the vein part. Furthermore, the reflectance for the blade part over the upper leaf area was greater and more stable than those for the other parts for both the cabbage and kale leaf samples. Conclusions: The results provide guidelines for optical reflectance measurements of Chinese cabbage and kale plants. The effects of the number of sampling points, the number of leaves, and the relationships between optical reflectance and leaf components remain to be investigated in the future.

Estimation of functional components of Chinese cabbage leaves grown in a plant factory using diffuse reflectance spectroscopy

“…The diameter of the measurement probe ferrule was 6.35 mm. As reported in previous studies, [14][15][16] the leaf spectral reflectance data were collected at 12 evenly distributed locations over the entire top surface.…”

Estimation of Glucosinolates and Anthocyanins in Kale Leaves Grown in a Plant Factory Using Spectral Reflectance

“…The spectrometer was operated by software provided by the manufacturer. Following the methodology of the previous studies of [9,37,40], the spectral reflectance data were measured from 9 sampling points over the blade part of each sampling leaf, as shown in Figure 3.…”

“…Determination of the effects of controlled environmental parameters and soil properties on the growth, formation, release, germination, yield, and quality of crops have been the focus of many studies worldwide [34][35][36]. Analysis of the functional components (e.g., glucosinolates, antho-cyanins, carotenoids, amino acids, and sugars) is also essential for ensuring nutritional levels and determining proper harvesting schedules [9,37,38].…”