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Estimation of chlorophyll content in radish leaves using hyperspectral remote sensing data and machine learning algorithms
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Paper Abstract

Slag consists mostly of mixed oxides of elements such as silicon, and recycled slag may be used for cultivating. The relationship between slag fertilization and plant growth rate can be expected to change depending on the volume of slag applied. Quantifying the chlorophyll content, an effective indicator of disease as well as nutritional and environmental stresses on plants, will enable optimal slag fertilization and then monitoring chlorophyll content using field measurements would enable the determination of optimal slag fertilization rates. In this study, radish plants (Raphanus sativus L), which belongs to the family Brassicaceae and is popular root vegetable in both tropical and temperate regions, were cultivated with slag fertilization and the potential use of hyperspectral reflectance was evaluated. Some preprocessing techniques were effective for retrieving chlorophyll contents in radish leaves from hyperspectral reflectance and then the regression model based on random forest and continuum-removed reflectance had the highest performance with a root mean square error of 5.141 μg cm-2 and RPD values of 1.858 for the test data set.

Paper Details

Date Published: 12 September 2021
PDF: 8 pages
Proc. SPIE 11856, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII, 1185609 (12 September 2021); doi: 10.1117/12.2600072
Show Author Affiliations
Adenan Yandra Nofrizal, Shizuoka Univ. (Japan)
Rei Sonobe, Shizuoka Univ. (Japan)
Hiroto Yamashita, Shizuoka Univ. (Japan)
Takashi Ikka, Shizuoka Univ. (Japan)
Akio Morita, Shizuoka Univ. (Japan)


Published in SPIE Proceedings Vol. 11856:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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