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

Adenan Yandra Nofrizal; Rei Sonobe; Hiroto Yamashita; Takashi Ikka; Akio Morita

doi:10.1117/12.2600072

12 September 2021 Estimation of chlorophyll content in radish leaves using hyperspectral remote sensing data and machine learning algorithms

Adenan Yandra Nofrizal, Rei Sonobe, Hiroto Yamashita, Takashi Ikka, Akio Morita

Author Affiliations +

Proceedings Volume 11856, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII; 1185609 (2021) https://doi.org/10.1117/12.2600072
Event: SPIE Remote Sensing, 2021, Online Only

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.

Conference Presentation

Citation Download Citation

Adenan Yandra Nofrizal, Rei Sonobe, Hiroto Yamashita, Takashi Ikka, and Akio Morita "Estimation of chlorophyll content in radish leaves using hyperspectral remote sensing data and machine learning algorithms", Proc. SPIE 11856, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII, 1185609 (12 September 2021); https://doi.org/10.1117/12.2600072

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available